{ "aboutHtml": "

DeepSeek VL2 ialah edisi penglihatan-bahasa penuh dalam siri DeepSeek VL2. Pengeluar menerangkan penjawaban soalan visual, pengecaman aksara optik, pemahaman dokumen, jadual dan carta serta penyetempatan objek berdasarkan huraian visual. Edisi penuh dibina atas DeepSeekMoE-27B dan mempunyai 4.5 bilion parameter aktif; siri ini turut mengandungi edisi Tiny dan Small dengan nama tersendiri.

Hubungkan soalan visual dengan sumber berlabel

Berikan imej yang jelas dibaca dan kenal pasti kawasan atau objek yang penting. Tugas dokumen boleh meminta maklumat yang boleh dibaca dalam medan tertentu. Tugas carta boleh meminta trend yang kelihatan serta label atau nilai yang menyokongnya. Simpan pengecam imej atau halaman bersama pemerhatian supaya penyemak boleh kembali kepada bahan asal.

Untuk penyetempatan visual, huraikan objek yang hendak dicari dan hasil yang diperlukan oleh tugas. Pastikan permintaan cukup khusus untuk membandingkan jawapan dengan imej. Bekalkan konteks bertulis apabila konteks itu menerangkan sebab sesuatu butiran penting.

Bandingkan koleksi imej yang terhad

Labelkan imej dan nyatakan ciri yang hendak dibandingkan. Minta satu pemerhatian bagi setiap sumber, dengan mengekalkan label bersama maklumat yang diekstrak. Semak teks yang boleh dibaca dan kuantiti terhadap bahan asal sebelum menggunakannya dalam operasi lain.

Pemproses pengeluar menggunakan persediaan imej yang berbeza mengikut bilangan imej: pembahagian jubin dinamik untuk sehingga dua imej dan penambahan ruang terus kepada 384×384 untuk tiga imej atau lebih. Ini ialah butiran kaedah pemprosesan yang diterbitkan pengeluar. Aliran kerja dihoskan perlu menggunakan persediaan imej dan had antara muka penyampaian sebenar.

Bezakan edisi penuh dan konteks

Profil dihoskan yang dinyatakan menyenaraikan panjang konteks 4,000 token. Sediakan soalan visual terfokus dan konteks bertulis berkaitan, dengan meninggalkan ruang untuk jawapan. Pilih kawasan imej dan teks yang diperlukan tugas, bukannya menambah bahan tidak berkaitan.

Simpan pengecam VL2 penuh bersama prompt dan hasil. Contoh kod inferens repositori menggunakan edisi Small yang mempunyai nama tersendiri, jadi tetapan contoh perlu kekal sebagai contoh, bukannya had tegas atau bukti pelaksanaan untuk profil dihoskan penuh ini.

", "aboutText": "DeepSeek VL2 ialah edisi penglihatan-bahasa penuh dalam siri DeepSeek VL2. Pengeluar menerangkan penjawaban soalan visual, pengecaman aksara optik, pemahaman dokumen, jadual dan carta serta penyetempatan objek berdasarkan huraian visual. Edisi penuh dibina atas DeepSeekMoE-27B dan mempunyai 4.5 bilion parameter aktif; siri ini turut mengandungi edisi Tiny dan Small dengan nama tersendiri.\n\n### Hubungkan soalan visual dengan sumber berlabel\n\nBerikan imej yang jelas dibaca dan kenal pasti kawasan atau objek yang penting. Tugas dokumen boleh meminta maklumat yang boleh dibaca dalam medan tertentu. Tugas carta boleh meminta trend yang kelihatan serta label atau nilai yang menyokongnya. Simpan pengecam imej atau halaman bersama pemerhatian supaya penyemak boleh kembali kepada bahan asal.\n\nUntuk penyetempatan visual, huraikan objek yang hendak dicari dan hasil yang diperlukan oleh tugas. Pastikan permintaan cukup khusus untuk membandingkan jawapan dengan imej. Bekalkan konteks bertulis apabila konteks itu menerangkan sebab sesuatu butiran penting.\n\n### Bandingkan koleksi imej yang terhad\n\nLabelkan imej dan nyatakan ciri yang hendak dibandingkan. Minta satu pemerhatian bagi setiap sumber, dengan mengekalkan label bersama maklumat yang diekstrak. Semak teks yang boleh dibaca dan kuantiti terhadap bahan asal sebelum menggunakannya dalam operasi lain.\n\nPemproses pengeluar menggunakan persediaan imej yang berbeza mengikut bilangan imej: pembahagian jubin dinamik untuk sehingga dua imej dan penambahan ruang terus kepada 384×384 untuk tiga imej atau lebih. Ini ialah butiran kaedah pemprosesan yang diterbitkan pengeluar. Aliran kerja dihoskan perlu menggunakan persediaan imej dan had antara muka penyampaian sebenar.\n\n### Bezakan edisi penuh dan konteks\n\nProfil dihoskan yang dinyatakan menyenaraikan panjang konteks 4,000 token. Sediakan soalan visual terfokus dan konteks bertulis berkaitan, dengan meninggalkan ruang untuk jawapan. Pilih kawasan imej dan teks yang diperlukan tugas, bukannya menambah bahan tidak berkaitan.\n\nSimpan pengecam VL2 penuh bersama prompt dan hasil. Contoh kod inferens repositori menggunakan edisi Small yang mempunyai nama tersendiri, jadi tetapan contoh perlu kekal sebagai contoh, bukannya had tegas atau bukti pelaksanaan untuk profil dihoskan penuh ini.", "editorialOverlay": { "copy_fields": null, "copy_sha256": null, "copy_source": null, "html_sha256": "e44f9bf1821ad614a5a20a16b6bb0bd8595e6a81bbf4f4656751db4da464ecbf", "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06", "raw_model_unchanged": true, "schema": "ailoot-native-model-editorial-v1" }, "locale": "ms", "methods": [ { "html": "
DS

DeepSeek VL2

DeepSeek /deepseek-vl2

Pengenalan

DeepSeek VL2 ialah model penglihatan-bahasa untuk soalan visual, OCR, dokumen, jadual, carta dan pemautan kepada sumber visual. Edisi penuh dibina berasaskan DeepSeekMoE-27B dengan 4.5B parameter diaktifkan; profil dihoskan ini menyenaraikan konteks 4,000 token untuk tugasan imej dan teks yang berfokus.

Input yang diterima
Teks
Hasil
Teks
Tentang model

Huraian ini merangkumi keupayaan model. Input, output dan pilihan yang tersedia di Ailoot disenaraikan di bawah untuk kaedah yang dipilih.

DeepSeek VL2 ialah edisi penglihatan-bahasa penuh dalam siri DeepSeek VL2. Pengeluar menerangkan penjawaban soalan visual, pengecaman aksara optik, pemahaman dokumen, jadual dan carta serta penyetempatan objek berdasarkan huraian visual. Edisi penuh dibina atas DeepSeekMoE-27B dan mempunyai 4.5 bilion parameter aktif; siri ini turut mengandungi edisi Tiny dan Small dengan nama tersendiri.

Hubungkan soalan visual dengan sumber berlabel

Berikan imej yang jelas dibaca dan kenal pasti kawasan atau objek yang penting. Tugas dokumen boleh meminta maklumat yang boleh dibaca dalam medan tertentu. Tugas carta boleh meminta trend yang kelihatan serta label atau nilai yang menyokongnya. Simpan pengecam imej atau halaman bersama pemerhatian supaya penyemak boleh kembali kepada bahan asal.

Untuk penyetempatan visual, huraikan objek yang hendak dicari dan hasil yang diperlukan oleh tugas. Pastikan permintaan cukup khusus untuk membandingkan jawapan dengan imej. Bekalkan konteks bertulis apabila konteks itu menerangkan sebab sesuatu butiran penting.

Bandingkan koleksi imej yang terhad

Labelkan imej dan nyatakan ciri yang hendak dibandingkan. Minta satu pemerhatian bagi setiap sumber, dengan mengekalkan label bersama maklumat yang diekstrak. Semak teks yang boleh dibaca dan kuantiti terhadap bahan asal sebelum menggunakannya dalam operasi lain.

Pemproses pengeluar menggunakan persediaan imej yang berbeza mengikut bilangan imej: pembahagian jubin dinamik untuk sehingga dua imej dan penambahan ruang terus kepada 384×384 untuk tiga imej atau lebih. Ini ialah butiran kaedah pemprosesan yang diterbitkan pengeluar. Aliran kerja dihoskan perlu menggunakan persediaan imej dan had antara muka penyampaian sebenar.

Bezakan edisi penuh dan konteks

Profil dihoskan yang dinyatakan menyenaraikan panjang konteks 4,000 token. Sediakan soalan visual terfokus dan konteks bertulis berkaitan, dengan meninggalkan ruang untuk jawapan. Pilih kawasan imej dan teks yang diperlukan tugas, bukannya menambah bahan tidak berkaitan.

Simpan pengecam VL2 penuh bersama prompt dan hasil. Contoh kod inferens repositori menggunakan edisi Small yang mempunyai nama tersendiri, jadi tetapan contoh perlu kekal sebagai contoh, bukannya had tegas atau bukti pelaksanaan untuk profil dihoskan penuh ini.

Tersedia di Ailoot · input & kaedah

Ailoot Ultra · OpenAI Chat POST /v1/chat/completions
Input
text
Output
text
Format output
json atau SSE
MedanJenisKeperluanNilai & hadLalai
MedanmodelJenisstringKeperluanDiperlukanNilai & had
deepseek-vl2
Lalai—
MedanmessagesJenismessage[]KeperluanDiperlukanNilai & had
Item minimum: 1
Lalai—
Medanmessages[].roleJenisstringKeperluanDiperlukanNilai & had
Bergantung pada model
Lalai—
Medanmessages[].contentJenisstringKeperluanPilihanNilai & had
Terpakai apabila: Perbualan teks
Lalai—
MedanstreamJenisbooleanKeperluanPilihanNilai & hadLalaifalse
Gabungan bahan input

Teks · Teks UTF8

messages[].content

Parameter kaedah dan kitar hayat
ParameterKontrak
Parametercontent_typeKontrakapplication/json
ParameterrequiredKontrakmodel, messages
Parametermaterial_typesKontraktext
ParametermessagesKontraktatasusunan mesej dengan struktur role/content
Parameterfile_materialKontrakdokumen UTF-8 sebenar dimasukkan sebagai teks literal; ID fail tidak diandaikan
Parameteroptional_controlsKontrakstream
Parametercontrol_scopeKontrakPerbualan teks; stream ialah nilai boolean dengan false sebagai lalai.
Parameteroutput_modalitiesKontraktext
Parameterhosted_operationsKontrakfalse
Parametermultimodal_input_bindingKontraknull

Alias kaedah: /chat/completions

Kitar hayat: synchronous_or_streamed

Hantar ID model yang tepat bersama messages.

Panjang konteks bukan had output.

Fail teks boleh membekalkan teks literal; input imej/audio/objek fail memerlukan pengikatan tepat.

Pelan harga

Rujukan tarif tidak menambah pilihan permintaan. Parameter yang disokong disenaraikan untuk kaedah yang dipilih.

Mod pantas
Tidak dinyatakan Apabila mod pantas dipilih
Mod flex
Tidak dinyatakan Apabila mod flex dipilih
Usaha penaakulan maksimum
Tidak dinyatakan Apabila usaha penaakulan maksimum menggunakan pelarasan default

Kadar Ailoot

Harga mengikut konfigurasi

KonfigurasiJadual kadarCaj
KonfigurasiKadar asas tokenJadual kadarKadar asasCaj
Input · Token input $0.00033 / 1K token
Output · Token output $0.00033 / 1K token
Input · Bacaan cache $0 / 1K token
Input · Penulisan cache $0 / 1K token
Input · Penulisan cache, 1 jam — / 1K token

Ailoot merizabkan anggaran amaun sebelum permintaan. Selepas hasil diterima, caj akhir diselesaikan berdasarkan penggunaan terukur. Penyelesaian caj penstriman menggunakan rekod penggunaan akhir. Cara rizab dan penyelesaian caj berfungsi →

Contoh permintaan

Ailoot Ultra · OpenAI Chat

set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model":"deepseek-vl2","messages":[{"role":"user","content":"hello"}],"stream":false}')\n\nPath("request.json").write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/chat/completions \\\n  -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \\\n  --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\nr=json.loads(Path("result.json").read_text())\nfor x in r.get("choices",[]):\n    if x.get("message",{}).get("content"): print(x["message"]["content"])\nPY_RESULT\n

Terima dan simpan hasil

Ailoot Ultra · OpenAI Chat

json atau SSE

Simpan semua hasil yang selesai

Simpan setiap respons lengkap, fail dan bahagian kandungan yang dikembalikan.

Kekalkan setiap pilihan jawapan bersama kandungan, panggilan alat dan penaakulannya. Dalam SSE, gabungkan delta mengikut indeks pilihan jawapan/alat dan kekalkan sebab penamatan akhir serta data penggunaan.

Jika respons asal perlu disemak

Kekalkan ID permintaan/respons dan pengepala. Tiada URL umum bagi pengambilan tugas teks dibekalkan.

Medan respons
MedanJenis
Medanchoices[].indexJenisinteger
Medanchoices[].message.contentJenisstring|null
Medanchoices[].message.tool_callsJenistool_call[]
Medanchoices[].message.reasoning_contentJenisstring
Medanchoices[].message.reasoning_detailsJenisobject|array
Medanchoices[].finish_reasonJenisstring|null
MedanusageJenisobject
Ralat dan langkah seterusnya
ResponsSyaratLangkah seterusnya
Respons400 · invalid_request_errorSyaratMedan wajib, perwakilan bahan atau nilai parameter tidak sahLangkah seterusnyaBetulkan medan yang ditunjukkan sebelum permintaan penciptaan baharu.

Alias

Alias model

Ailoot Ultra deepseek-vl2

Alias laluan kaedah

/chat/completions

ID pemanggil berkaitan

deepseek-vl2

", "method_identity": { "effective_request_model_id": "deepseek-vl2", "fmt": "chat", "http": "POST", "path": "/v1/chat/completions", "request_model_id": "deepseek-vl2", "route_label": "Ultra" }, "method_key": "Ultra|chat|/v1/chat/completions|deepseek-vl2", "ordinal": 0, "rawMethod": { "available": null, "cache_read": 0, "cache_write": 0, "cache_write_1h": null, "contract": { "data": { "all_results": "Keep every choice and its content/tool calls/reasoning. In SSE assemble deltas by choice/tool index, retain the final finish reason and usage.", "authentication": { "api_key_source": "AILOOT_API_KEY", "header": "Authorization: Bearer" }, "errors": [ { "code": null, "condition": "Medan wajib, perwakilan bahan atau nilai parameter tidak sah", "http_status": 400, "recovery": "Betulkan medan yang ditunjukkan sebelum permintaan penciptaan baharu.", "type": "invalid_request_error" } ], "hints": [ "Model context is not an output cap.", "Send the conversation as text messages. Stream must be a boolean; omitted stream defaults to false." ], "ignored_fields": [], "lifecycle": { "create": { "http": "POST", "path": "/v1/chat/completions" }, "job_read": null, "mode": "synchronous_or_streamed", "unknown": "Kekalkan ID permintaan/respons dan pengepala. Tiada URL umum bagi pengambilan tugas teks dibekalkan." }, "material_combinations": [ { "mode": "text", "representation": "messages[].content", "required_material": "Teks UTF8" } ], "operation_modes": [ "conversation" ], "outputs": { "all_results": "Kekalkan setiap pilihan jawapan bersama kandungan, panggilan alat dan penaakulannya. Dalam SSE, gabungkan delta mengikut indeks pilihan jawapan/alat dan kekalkan sebab penamatan akhir serta data penggunaan.", "family": "text", "fields": [ { "description": null, "name": "choices[].index", "type": "integer" }, { "description": null, "name": "choices[].message.content", "type": "string|null" }, { "description": null, "name": "choices[].message.tool_calls", "type": "tool_call[]" }, { "description": null, "name": "choices[].message.reasoning_content", "type": "string" }, { "description": null, "name": "choices[].message.reasoning_details", "type": "object|array" }, { "description": null, "name": "choices[].finish_reason", "type": "string|null" }, { "description": null, "name": "usage", "type": "object" } ], "format": "json atau SSE" }, "parameters": [ { "availability": "source_interface", "default": null, "default_known": false, "enum": [ "deepseek-vl2" ], "enum_known": true, "name": "model", "required": true, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "min_items": 1, "name": "messages", "required": true, "type": "message[]" }, { "availability": "model_dependent", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "messages[].role", "required": true, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "messages[].content", "required": false, "type": "string", "when": "Perbualan teks" }, { "availability": "source_interface", "default": false, "default_known": true, "enum": null, "enum_known": false, "name": "stream", "required": false, "type": "boolean" } ], "profile_binding": "Text conversation at the exact model ID and method path.", "unsupported": [ "additional models/models list", "plugins", "route", "hosted tools; ordinary client function tools remain separate", "non-text output modalities", "provider.options overrides", "one-hour prompt-cache TTL" ] }, "hints": [ "Hantar ID model yang tepat bersama messages.", "Panjang konteks bukan had output.", "Fail teks boleh membekalkan teks literal; input imej/audio/objek fail memerlukan pengikatan tepat." ], "input": { "content_type": "application/json", "control_scope": "Perbualan teks; stream ialah nilai boolean dengan false sebagai lalai.", "file_material": "dokumen UTF-8 sebenar dimasukkan sebagai teks literal; ID fail tidak diandaikan", "hosted_operations": false, "material_types": [ "text" ], "messages": "tatasusunan mesej dengan struktur role/content", "multimodal_input_binding": null, "optional_controls": [ "stream" ], "output_modalities": [ "text" ], "required": [ "model", "messages" ] }, "lifecycle": { "mode": "synchronous_or_streamed", "unknown": "preserve original request id; no generic chat job retrieval path" }, "manufacturer_capability": null, "method": { "http": "POST", "path": "/v1/chat/completions" }, "method_aliases": [ "/chat/completions" ], "operation_modes": [ "text_chat" ], "output": { "content": "choices[].message.content or stream delta", "family": "text", "format": "json or supported SSE", "tool_calls": "retain actual returned function calls" } }, "ctx": null, "declared_pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Kadar asas token", "units": [ { "amount": "0.00033000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000033", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": "0.00033000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.00000033", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": "0", "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": "0", "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" } ] } ], "examples": [ { "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{\"model\":\"deepseek-vl2\",\"messages\":[{\"role\":\"user\",\"content\":\"hello\"}],\"stream\":false}')\n\nPath(\"request.json\").write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/chat/completions \\\n -H \"Authorization: Bearer $AILOOT_API_KEY\" -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\nr=json.loads(Path(\"result.json\").read_text())\nfor x in r.get(\"choices\",[]):\n if x.get(\"message\",{}).get(\"content\"): print(x[\"message\"][\"content\"])\nPY_RESULT\n", "label": "curl · retained exact creation", "language": "bash" }, { "code": "from openai import OpenAI\nimport os\n\nif not os.environ.get(\"AILOOT_API_KEY\", \"\").strip(): raise SystemExit(\"Set AILOOT_API_KEY before making a request\")\nclient=OpenAI(base_url=\"https://api.ailoot.ai/v1\",api_key=os.environ[\"AILOOT_API_KEY\"],max_retries=0)\nresult=client.chat.completions.create(model=\"deepseek-vl2\", messages=[{\"role\":\"user\",\"content\":\"hello\"}], stream=False)\nimport base64, json\nfrom pathlib import Path\n\ndef save_result(data, stem=\"result\"):\n # Keep every field, including usage, IDs, reasoning and tool requests.\n Path(stem + \".json\").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n print(json.dumps(data, ensure_ascii=False, indent=2))\n def save_blob(encoded, suffix, label):\n raw = base64.b64decode(encoded, altchars=b\"-_\", validate=True)\n ext = \"png\" if raw.startswith(b\"\\x89PNG\\r\\n\\x1a\\n\") else \"jpg\" if raw.startswith(b\"\\xff\\xd8\") else \"webp\" if raw[:4] == b\"RIFF\" and raw[8:12] == b\"WEBP\" else suffix\n file = Path(stem + \"-\" + label + \".\" + ext)\n file.write_bytes(raw)\n print(\"Saved\", file)\n for index, block in enumerate(data.get(\"content\") or []):\n print(\"Content\", index, json.dumps(block, ensure_ascii=False))\n for choice in data.get(\"choices\") or []:\n print(\"Choice\", choice.get(\"index\"), json.dumps(choice.get(\"message\", choice.get(\"delta\", {})), ensure_ascii=False))\n for index, item in enumerate(data.get(\"output\") or []):\n print(\"Output\", index, json.dumps(item, ensure_ascii=False))\n if item.get(\"type\") == \"image_generation_call\" and item.get(\"result\"):\n save_blob(item[\"result\"], item.get(\"output_format\") or \"bin\", \"output-\" + str(index))\n for ci, candidate in enumerate(data.get(\"candidates\") or []):\n for pi, part in enumerate((candidate.get(\"content\") or {}).get(\"parts\") or []):\n print(\"Candidate/part\", ci, pi, json.dumps(part, ensure_ascii=False))\n blob = part.get(\"inlineData\") or part.get(\"inline_data\")\n if blob and blob.get(\"data\"):\n mime = blob.get(\"mimeType\") or blob.get(\"mime_type\")\n suffix = {\"image/png\":\"png\", \"image/jpeg\":\"jpg\", \"image/webp\":\"webp\", \"audio/wav\":\"wav\", \"audio/mpeg\":\"mp3\", \"video/mp4\":\"mp4\"}.get(mime, \"bin\")\n save_blob(blob[\"data\"], suffix, \"candidate-\" + str(ci) + \"-part-\" + str(pi))\n\nsave_result(result.to_dict(mode=\"json\"))\n", "label": "Python SDK · all choices", "language": "python" } ], "family": "openai", "fast_multiplier": null, "flex_multiplier": null, "fmt": "chat", "hints": [ "Send exact model ID with messages.", "Context length is not an output cap.", "A text file may supply literal text; image/audio/file-object inputs need exact binding." ], "http": "POST", "in": 0.33, "input": [ "text" ], "label": "OpenAI Chat", "max_out": null, "max_reasoning_effort_multiplier": null, "method_aliases": [ "/chat/completions" ], "out": 0.33, "output": [ "text" ], "path": "/v1/chat/completions", "price_conditions": [ { "label": "Fast mode", "value": "Not specified", "when": "When fast mode is selected" }, { "label": "Flex mode", "value": "Not specified", "when": "When flex mode is selected" }, { "label": "Maximum reasoning effort", "value": "Not specified", "when": "When maximum reasoning effort uses the default adjustment" } ], "pricing": { "billing_mode": "token", "image_input_price": null, "image_output_price": null, "per_request_price": null }, "pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Base token rates", "units": [ { "amount": "0.00033000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000033", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": "0.00033000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.00000033", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": "0", "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": "0", "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" } ] } ], "request_model_id": "deepseek-vl2", "route_label": "Ultra", "status_reads": [], "tiers": [], "units": [ { "amount": "0.00033000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000033", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": "0.00033000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.00000033", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": "0", "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": "0", "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" } ] } }, { "html": "
DS

DeepSeek VL2

DeepSeek /deepseek-vl2

Pengenalan

DeepSeek VL2 ialah model penglihatan-bahasa untuk soalan visual, OCR, dokumen, jadual, carta dan pemautan kepada sumber visual. Edisi penuh dibina berasaskan DeepSeekMoE-27B dengan 4.5B parameter diaktifkan; profil dihoskan ini menyenaraikan konteks 4,000 token untuk tugasan imej dan teks yang berfokus.

Input yang diterima
Teks
Hasil
json · sse
Tentang model

Huraian ini merangkumi keupayaan model. Input, output dan pilihan yang tersedia di Ailoot disenaraikan di bawah untuk kaedah yang dipilih.

DeepSeek VL2 ialah edisi penglihatan-bahasa penuh dalam siri DeepSeek VL2. Pengeluar menerangkan penjawaban soalan visual, pengecaman aksara optik, pemahaman dokumen, jadual dan carta serta penyetempatan objek berdasarkan huraian visual. Edisi penuh dibina atas DeepSeekMoE-27B dan mempunyai 4.5 bilion parameter aktif; siri ini turut mengandungi edisi Tiny dan Small dengan nama tersendiri.

Hubungkan soalan visual dengan sumber berlabel

Berikan imej yang jelas dibaca dan kenal pasti kawasan atau objek yang penting. Tugas dokumen boleh meminta maklumat yang boleh dibaca dalam medan tertentu. Tugas carta boleh meminta trend yang kelihatan serta label atau nilai yang menyokongnya. Simpan pengecam imej atau halaman bersama pemerhatian supaya penyemak boleh kembali kepada bahan asal.

Untuk penyetempatan visual, huraikan objek yang hendak dicari dan hasil yang diperlukan oleh tugas. Pastikan permintaan cukup khusus untuk membandingkan jawapan dengan imej. Bekalkan konteks bertulis apabila konteks itu menerangkan sebab sesuatu butiran penting.

Bandingkan koleksi imej yang terhad

Labelkan imej dan nyatakan ciri yang hendak dibandingkan. Minta satu pemerhatian bagi setiap sumber, dengan mengekalkan label bersama maklumat yang diekstrak. Semak teks yang boleh dibaca dan kuantiti terhadap bahan asal sebelum menggunakannya dalam operasi lain.

Pemproses pengeluar menggunakan persediaan imej yang berbeza mengikut bilangan imej: pembahagian jubin dinamik untuk sehingga dua imej dan penambahan ruang terus kepada 384×384 untuk tiga imej atau lebih. Ini ialah butiran kaedah pemprosesan yang diterbitkan pengeluar. Aliran kerja dihoskan perlu menggunakan persediaan imej dan had antara muka penyampaian sebenar.

Bezakan edisi penuh dan konteks

Profil dihoskan yang dinyatakan menyenaraikan panjang konteks 4,000 token. Sediakan soalan visual terfokus dan konteks bertulis berkaitan, dengan meninggalkan ruang untuk jawapan. Pilih kawasan imej dan teks yang diperlukan tugas, bukannya menambah bahan tidak berkaitan.

Simpan pengecam VL2 penuh bersama prompt dan hasil. Contoh kod inferens repositori menggunakan edisi Small yang mempunyai nama tersendiri, jadi tetapan contoh perlu kekal sebagai contoh, bukannya had tegas atau bukti pelaksanaan untuk profil dihoskan penuh ini.

Tersedia di Ailoot · input & kaedah

Ailoot Ultra · Anthropic Messages POST /v1/messages
Input
text
Output
json, sse
Format output
JSON atau SSE
MedanJenisKeperluanNilai & hadLalai
MedanmodelJenisstringKeperluanDiperlukanNilai & had
deepseek-vl2
Lalai—
MedanmessagesJenismessage[]KeperluanDiperlukanNilai & hadLalai—
Medanmessages[].roleJenisstringKeperluanDiperlukanNilai & had
user, assistant
Lalai—
Medanmessages[].contentJenisstringKeperluanDiperlukanNilai & hadLalai—
Medanmax_tokensJenisintegerKeperluanDiperlukanNilai & hadLalai—
MedanstreamJenisbooleanKeperluanPilihanNilai & hadLalaifalse
Gabungan bahan input

Teks · Teks UTF-8

rentetan messages[].content

Parameter kaedah dan kitar hayat
ParameterKontrak
Parametercontent_typeKontrakapplication/json
ParameterrequiredKontrakmodel, messages, max_tokens
Parameteroptional_controlsKontrakstream
Parametermaterial_typesKontraktext
Parametertext_file_materialKontrakBaca dokumen UTF-8 sebenar dan letakkan teksnya dalam rentetan input atau kandungan mesej yang ditunjukkan.

Kitar hayat: synchronous_or_streamed

Pelan harga

Rujukan tarif tidak menambah pilihan permintaan. Parameter yang disokong disenaraikan untuk kaedah yang dipilih.

Mod pantas
Tidak dinyatakan Apabila mod pantas dipilih
Mod flex
Tidak dinyatakan Apabila mod flex dipilih
Usaha penaakulan maksimum
Tidak dinyatakan Apabila usaha penaakulan maksimum menggunakan pelarasan default

Kadar Ailoot

Harga mengikut konfigurasi

KonfigurasiJadual kadarCaj
KonfigurasiKadar asas tokenJadual kadarKadar asasCaj
Input · Token input $0.00033 / 1K token
Output · Token output $0.00033 / 1K token
Input · Bacaan cache $0 / 1K token
Input · Penulisan cache $0 / 1K token
Input · Penulisan cache, 1 jam — / 1K token

Ailoot merizabkan anggaran amaun sebelum permintaan. Selepas hasil diterima, caj akhir diselesaikan berdasarkan penggunaan terukur. Penyelesaian caj penstriman menggunakan rekod penggunaan akhir. Cara rizab dan penyelesaian caj berfungsi →

Contoh permintaan

Ailoot Ultra · Anthropic Messages

{\n  "model": "deepseek-vl2",\n  "max_tokens": 128,\n  "messages": [\n    {\n      "role": "user",\n      "content": "Summarize the incident report."\n    }\n  ],\n  "stream": false\n}

Terima dan simpan hasil

Ailoot Ultra · Anthropic Messages

JSON atau SSE

Simpan semua hasil yang selesai

Simpan setiap respons lengkap, fail dan bahagian kandungan yang dikembalikan.

Kekalkan semua blok kandungan, tandatangan penaakulan dan ID penggunaan alat sebenar. Gabungkan semua delta blok SSE; blok teks pertama bukan keseluruhan output.

Jika respons asal perlu disemak

Kekalkan ID permintaan/respons dan pengepala. Tiada URL umum bagi pengambilan tugas teks dibekalkan.

Medan respons
MedanJenis
MedanidJenisstring
MedanmodelJenisstring
Medancontent[]Jenistext|thinking|tool_use|other content block
Medanstop_reasonJenisstring|null
MedanusageJenisobject
Ralat dan langkah seterusnya
ResponsSyaratLangkah seterusnya
Respons400 · invalid_request_errorSyaratMedan wajib, perwakilan bahan atau nilai parameter tidak sahLangkah seterusnyaBetulkan medan yang ditunjukkan sebelum permintaan penciptaan baharu.

Alias

Alias model

Ailoot Ultra deepseek-vl2

Alias laluan kaedah

/chat/completions

ID pemanggil berkaitan

deepseek-vl2

", "method_identity": { "effective_request_model_id": "deepseek-vl2", "fmt": "messages", "http": "POST", "path": "/v1/messages", "request_model_id": "deepseek-vl2", "route_label": "Ultra" }, "method_key": "Ultra|messages|/v1/messages|deepseek-vl2", "ordinal": 1, "rawMethod": { "available": null, "cache_read": 0, "cache_write": 0, "cache_write_1h": null, "contract": { "data": { "authentication": { "api_key_source": "AILOOT_API_KEY", "headers": [ "Authorization: Bearer", "x-api-key", "anthropic-version" ], "version_example": "2023-06-01" }, "errors": [ { "code": null, "condition": "Medan wajib, perwakilan bahan atau nilai parameter tidak sah", "http_status": 400, "recovery": "Betulkan medan yang ditunjukkan sebelum permintaan penciptaan baharu.", "type": "invalid_request_error" } ], "example_body": { "max_tokens": 128, "messages": [ { "content": "Summarize the incident report.", "role": "user" } ], "model": "deepseek-vl2", "stream": false }, "lifecycle": { "create": { "http": "POST", "path": "/v1/messages" }, "job_read": null, "mode": "synchronous_or_streamed", "unknown": "Kekalkan ID permintaan/respons dan pengepala. Tiada URL umum bagi pengambilan tugas teks dibekalkan." }, "material_combinations": [ { "mode": "text", "representation": "rentetan messages[].content", "required_material": "Teks UTF-8" } ], "operation_modes": [ "synchronous_json", "streamed_SSE" ], "outputs": { "all_results": "Kekalkan semua blok kandungan, tandatangan penaakulan dan ID penggunaan alat sebenar. Gabungkan semua delta blok SSE; blok teks pertama bukan keseluruhan output.", "family": "protocol", "fields": [ { "description": null, "name": "id", "type": "string" }, { "description": null, "name": "model", "type": "string" }, { "description": null, "name": "content[]", "type": "text|thinking|tool_use|other content block" }, { "description": null, "name": "stop_reason", "type": "string|null" }, { "description": null, "name": "usage", "type": "object" } ], "format": "JSON atau SSE" }, "parameters": [ { "availability": "source_interface", "default": null, "default_known": false, "enum": [ "deepseek-vl2" ], "enum_known": true, "name": "model", "required": true, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "messages", "required": true, "type": "message[]" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": [ "user", "assistant" ], "enum_known": true, "name": "messages[].role", "required": true, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "messages[].content", "required": true, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "max_tokens", "required": true, "type": "integer" }, { "availability": "source_interface", "default": false, "default_known": true, "enum": null, "enum_known": false, "name": "stream", "required": false, "type": "boolean" } ], "profile_binding": "Documented current stock Messages bridge; optional model capabilities are separate from this minimal text request.", "request_schema": { "additionalProperties": false, "properties": { "max_tokens": { "minimum": 1, "type": "integer" }, "messages": { "items": { "additionalProperties": false, "properties": { "content": { "oneOf": [ { "type": "string" }, { "items": { "oneOf": [ { "properties": { "text": { "type": "string" }, "type": { "const": "text" } }, "required": [ "type", "text" ], "type": "object" }, { "properties": { "source": { "oneOf": [ { "properties": { "type": { "const": "url" }, "url": { "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "properties": { "data": { "type": "string" }, "media_type": { "type": "string" }, "type": { "const": "base64" } }, "required": [ "type", "media_type", "data" ], "type": "object" } ] }, "type": { "const": "image" } }, "required": [ "type", "source" ], "type": "object" } ] }, "type": "array" } ] }, "role": { "enum": [ "user", "assistant" ], "type": "string" } }, "required": [ "role", "content" ], "type": "object" }, "minItems": 1, "type": "array" }, "model": { "enum": [ "deepseek-vl2" ], "type": "string" }, "stream": { "default": false, "type": "boolean" } }, "required": [ "model", "messages", "max_tokens" ], "type": "object" }, "schema_template": { "max_tokens": 128, "messages": [ { "content": "Summarize the incident report.", "role": "user" } ], "model": "deepseek-vl2", "stream": false }, "unsupported": [ "No query API key.", "Hosted/server-side tools and model-specific optional controls are not part of this minimal family example." ] }, "input": { "content_type": "application/json", "material_types": [ "text" ], "optional_controls": [ "stream" ], "required": [ "model", "messages", "max_tokens" ], "text_file_material": "Baca dokumen UTF-8 sebenar dan letakkan teksnya dalam rentetan input atau kandungan mesej yang ditunjukkan." }, "lifecycle": { "create": { "http": "POST", "path": "/v1/messages" }, "job_read": null, "mode": "synchronous_or_streamed", "unknown": "Keep request/response IDs and headers. No generic text job retrieval URL is supplied." }, "manufacturer_capability": null, "method": { "http": "POST", "path": "/v1/messages" }, "output": { "all_results": "Keep all content blocks, thinking signatures and actual tool-use IDs. Assemble all SSE block deltas; a first text block is not the complete output.", "family": "protocol", "format": "JSON or SSE" } }, "ctx": null, "declared_pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Kadar asas token", "units": [ { "amount": "0.00033000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000033", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": "0.00033000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.00000033", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": "0", "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": "0", "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" } ] } ], "examples": [ { "code": "{\n \"model\": \"deepseek-vl2\",\n \"max_tokens\": 128,\n \"messages\": [\n {\n \"role\": \"user\",\n \"content\": \"Summarize the incident report.\"\n }\n ],\n \"stream\": false\n}", "label": "Complete minimal JSON body", "language": "json" }, { "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\n: \"${AILOOT_TEXT_FILE:?Set your actual UTF-8 document}\"\nexport AILOOT_TEXT_FILE\npython3 - <<'PY_REQUEST'\nimport json, os\nfrom pathlib import Path\n\nsource_file = Path(os.environ['AILOOT_TEXT_FILE'])\nif not source_file.is_file():\n raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')\ntext = source_file.read_text(encoding='utf-8')\nif not text.strip():\n raise SystemExit('Choose a document containing text')\npayload = {'model': 'deepseek-vl2', 'max_tokens': 128, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]}\npayload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\\n\\n' + text\nPath('request.json').write_text(json.dumps(payload, ensure_ascii=False), encoding='utf-8')\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/messages \\\n -H \"x-api-key: $AILOOT_API_KEY\" -H 'anthropic-version: 2023-06-01' -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\n\nresult = json.loads(Path('result.json').read_text(encoding='utf-8'))\nprint(json.dumps(result, ensure_ascii=False, indent=2))\nPY_RESULT\n", "label": "curl · use your UTF-8 document", "language": "bash" }, { "code": "import json, os\nfrom pathlib import Path\n\nsource_file = Path(os.environ['AILOOT_TEXT_FILE'])\nif not source_file.is_file():\n raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')\ntext = source_file.read_text(encoding='utf-8')\nif not text.strip():\n raise SystemExit('Choose a document containing text')\npayload = {'model': 'deepseek-vl2', 'max_tokens': 128, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]}\npayload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\\n\\n' + text\nfrom urllib.request import Request, urlopen\nfrom urllib.error import HTTPError\n\napi_key = os.environ['AILOOT_API_KEY']\nif not api_key.strip():\n raise SystemExit('Set AILOOT_API_KEY before making a request')\nheaders = {'x-api-key': 'PLACEHOLDER_KEY', 'anthropic-version': '2023-06-01', 'Content-Type': 'application/json'}\nheaders['x-api-key'] = api_key\nrequest = Request('https://api.ailoot.ai/v1/messages', data=json.dumps(payload, ensure_ascii=False).encode('utf-8'), headers=headers, method='POST')\ntry:\n with urlopen(request, timeout=120) as response:\n raw = response.read()\n Path('response-headers.txt').write_text(str(response.headers), encoding='utf-8')\nexcept HTTPError as error:\n Path('result.json').write_bytes(error.read())\n Path('response-headers.txt').write_text(str(error.headers), encoding='utf-8')\n raise\nPath('result.json').write_bytes(raw)\nimport json\nfrom pathlib import Path\n\nresult = json.loads(Path('result.json').read_text(encoding='utf-8'))\nprint(json.dumps(result, ensure_ascii=False, indent=2))\n", "label": "Python · save the complete result", "language": "python" } ], "family": "anthropic", "fast_multiplier": null, "flex_multiplier": null, "fmt": "messages", "http": "POST", "in": 0.33, "input": [ "text" ], "label": "Anthropic Messages", "max_out": null, "max_reasoning_effort_multiplier": null, "method_aliases": [], "out": 0.33, "output": [ "json", "sse" ], "path": "/v1/messages", "price_conditions": [ { "label": "Fast mode", "value": "Not specified", "when": "When fast mode is selected" }, { "label": "Flex mode", "value": "Not specified", "when": "When flex mode is selected" }, { "label": "Maximum reasoning effort", "value": "Not specified", "when": "When maximum reasoning effort uses the default adjustment" } ], "pricing": { "billing_mode": "token", "image_input_price": null, "image_output_price": null, "per_request_price": null }, "pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Base token rates", "units": [ { "amount": "0.00033000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000033", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": "0.00033000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.00000033", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": "0", "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": "0", "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" } ] } ], "request_model_id": "deepseek-vl2", "route_label": "Ultra", "status_reads": [], "tiers": [], "units": [ { "amount": "0.00033000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000033", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": "0.00033000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.00000033", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": "0", "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": "0", "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" } ] } } ], "model_id": "deepseek-vl2", "nativeProvenance": { "input_facts_canonical_sha256": "30cac23d918c0db06ab5e0a0243d53cd3125a0e428e1f062754c5e0baee64c40", "input_html_sha256": "629f716319ab56fbfb69ccbc1ae2f4a9eaf8f8c56c9e76bbb14fd254f9e4b752", "private_audit_sha256": "0e6a9a0ad8741b240a54c0198191ccc5c1407f87d21f487d9ee1f1102e97c2e3", "role": "historical-normalized-input-audit-hashes", "schema": "ailoot-native-model-provenance-v2" }, "page": { "canonical": "https://ailoot.ai/models/ms/deepseek-vl2/", "description": "DeepSeek VL2 ialah model penglihatan-bahasa untuk soalan visual, OCR, dokumen, jadual, carta dan pemautan kepada sumber visual. Edisi penuh dibina berasaskan DeepSeekMoE-27B dengan 4.5B parameter diaktifkan; profil dihoskan ini menyenaraikan konteks 4,000 token untuk tugasan imej dan teks yang berfokus.", "locale": "ms", "md_url": "https://ailoot.ai/models/ms/deepseek-vl2/index.md", "title": "DeepSeek VL2 — Ailoot", "url": "https://ailoot.ai/models/ms/deepseek-vl2/" }, "rawModel": { "about": "DeepSeek VL2 ialah edisi penglihatan-bahasa penuh dalam siri DeepSeek VL2. Pengeluar menerangkan penjawaban soalan visual, pengecaman aksara optik, pemahaman dokumen, jadual dan carta serta penyetempatan objek berdasarkan huraian visual. Edisi penuh dibina atas DeepSeekMoE-27B dan mempunyai 4.5 bilion parameter aktif; siri ini turut mengandungi edisi Tiny dan Small dengan nama tersendiri.\n\n### Hubungkan soalan visual dengan sumber berlabel\n\nBerikan imej yang jelas dibaca dan kenal pasti kawasan atau objek yang penting. Tugas dokumen boleh meminta maklumat yang boleh dibaca dalam medan tertentu. Tugas carta boleh meminta trend yang kelihatan serta label atau nilai yang menyokongnya. Simpan pengecam imej atau halaman bersama pemerhatian supaya penyemak boleh kembali kepada bahan asal.\n\nUntuk penyetempatan visual, huraikan objek yang hendak dicari dan hasil yang diperlukan oleh tugas. Pastikan permintaan cukup khusus untuk membandingkan jawapan dengan imej. Bekalkan konteks bertulis apabila konteks itu menerangkan sebab sesuatu butiran penting.\n\n### Bandingkan koleksi imej yang terhad\n\nLabelkan imej dan nyatakan ciri yang hendak dibandingkan. Minta satu pemerhatian bagi setiap sumber, dengan mengekalkan label bersama maklumat yang diekstrak. Semak teks yang boleh dibaca dan kuantiti terhadap bahan asal sebelum menggunakannya dalam operasi lain.\n\nPemproses pengeluar menggunakan persediaan imej yang berbeza mengikut bilangan imej: pembahagian jubin dinamik untuk sehingga dua imej dan penambahan ruang terus kepada 384×384 untuk tiga imej atau lebih. Ini ialah butiran kaedah pemprosesan yang diterbitkan pengeluar. Aliran kerja dihoskan perlu menggunakan persediaan imej dan had antara muka penyampaian sebenar.\n\n### Bezakan edisi penuh dan konteks\n\nProfil dihoskan yang dinyatakan menyenaraikan panjang konteks 4,000 token. Sediakan soalan visual terfokus dan konteks bertulis berkaitan, dengan meninggalkan ruang untuk jawapan. Pilih kawasan imej dan teks yang diperlukan tugas, bukannya menambah bahan tidak berkaitan.\n\nSimpan pengecam VL2 penuh bersama prompt dan hasil. Contoh kod inferens repositori menggunakan edisi Small yang mempunyai nama tersendiri, jadi tetapan contoh perlu kekal sebagai contoh, bukannya had tegas atau bukti pelaksanaan untuk profil dihoskan penuh ini.", "alias_of": null, "aliases": [], "author": "DeepSeek", "capabilities": { "context_length": 4000, "manufacturer_input": [], "manufacturer_output": [] }, "card_routes": [ { "label": "Ultra", "rates": [ { "amount": "0.00033000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000033", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": "0.00033000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.00000033", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" } ], "summary": "Maklumat lengkap tentang harga konfigurasi terdapat dalam butiran." } ], "ctx": null, "display_name": "DeepSeek VL2", "formats": [ "chat", "messages" ], "id": "deepseek-vl2", "input": [ "text" ], "kind": "text", "max_out": null, "methods": [ { "available": null, "cache_read": 0, "cache_write": 0, "cache_write_1h": null, "contract": { "data": { "all_results": "Keep every choice and its content/tool calls/reasoning. In SSE assemble deltas by choice/tool index, retain the final finish reason and usage.", "authentication": { "api_key_source": "AILOOT_API_KEY", "header": "Authorization: Bearer" }, "errors": [ { "code": null, "condition": "Medan wajib, perwakilan bahan atau nilai parameter tidak sah", "http_status": 400, "recovery": "Betulkan medan yang ditunjukkan sebelum permintaan penciptaan baharu.", "type": "invalid_request_error" } ], "hints": [ "Model context is not an output cap.", "Send the conversation as text messages. Stream must be a boolean; omitted stream defaults to false." ], "ignored_fields": [], "lifecycle": { "create": { "http": "POST", "path": "/v1/chat/completions" }, "job_read": null, "mode": "synchronous_or_streamed", "unknown": "Kekalkan ID permintaan/respons dan pengepala. Tiada URL umum bagi pengambilan tugas teks dibekalkan." }, "material_combinations": [ { "mode": "text", "representation": "messages[].content", "required_material": "Teks UTF8" } ], "operation_modes": [ "conversation" ], "outputs": { "all_results": "Kekalkan setiap pilihan jawapan bersama kandungan, panggilan alat dan penaakulannya. Dalam SSE, gabungkan delta mengikut indeks pilihan jawapan/alat dan kekalkan sebab penamatan akhir serta data penggunaan.", "family": "text", "fields": [ { "description": null, "name": "choices[].index", "type": "integer" }, { "description": null, "name": "choices[].message.content", "type": "string|null" }, { "description": null, "name": "choices[].message.tool_calls", "type": "tool_call[]" }, { "description": null, "name": "choices[].message.reasoning_content", "type": "string" }, { "description": null, "name": "choices[].message.reasoning_details", "type": "object|array" }, { "description": null, "name": "choices[].finish_reason", "type": "string|null" }, { "description": null, "name": "usage", "type": "object" } ], "format": "json atau SSE" }, "parameters": [ { "availability": "source_interface", "default": null, "default_known": false, "enum": [ "deepseek-vl2" ], "enum_known": true, "name": "model", "required": true, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "min_items": 1, "name": "messages", "required": true, "type": "message[]" }, { "availability": "model_dependent", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "messages[].role", "required": true, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "messages[].content", "required": false, "type": "string", "when": "Perbualan teks" }, { "availability": "source_interface", "default": false, "default_known": true, "enum": null, "enum_known": false, "name": "stream", "required": false, "type": "boolean" } ], "profile_binding": "Text conversation at the exact model ID and method path.", "unsupported": [ "additional models/models list", "plugins", "route", "hosted tools; ordinary client function tools remain separate", "non-text output modalities", "provider.options overrides", "one-hour prompt-cache TTL" ] }, "hints": [ "Hantar ID model yang tepat bersama messages.", "Panjang konteks bukan had output.", "Fail teks boleh membekalkan teks literal; input imej/audio/objek fail memerlukan pengikatan tepat." ], "input": { "content_type": "application/json", "control_scope": "Perbualan teks; stream ialah nilai boolean dengan false sebagai lalai.", "file_material": "dokumen UTF-8 sebenar dimasukkan sebagai teks literal; ID fail tidak diandaikan", "hosted_operations": false, "material_types": [ "text" ], "messages": "tatasusunan mesej dengan struktur role/content", "multimodal_input_binding": null, "optional_controls": [ "stream" ], "output_modalities": [ "text" ], "required": [ "model", "messages" ] }, "lifecycle": { "mode": "synchronous_or_streamed", "unknown": "preserve original request id; no generic chat job retrieval path" }, "manufacturer_capability": null, "method": { "http": "POST", "path": "/v1/chat/completions" }, "method_aliases": [ "/chat/completions" ], "operation_modes": [ "text_chat" ], "output": { "content": "choices[].message.content or stream delta", "family": "text", "format": "json or supported SSE", "tool_calls": "retain actual returned function calls" } }, "ctx": null, "declared_pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Kadar asas token", "units": [ { "amount": "0.00033000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000033", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": "0.00033000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.00000033", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": "0", "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": "0", "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" } ] } ], "examples": [ { "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{\"model\":\"deepseek-vl2\",\"messages\":[{\"role\":\"user\",\"content\":\"hello\"}],\"stream\":false}')\n\nPath(\"request.json\").write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/chat/completions \\\n -H \"Authorization: Bearer $AILOOT_API_KEY\" -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\nr=json.loads(Path(\"result.json\").read_text())\nfor x in r.get(\"choices\",[]):\n if x.get(\"message\",{}).get(\"content\"): print(x[\"message\"][\"content\"])\nPY_RESULT\n", "label": "curl · retained exact creation", "language": "bash" }, { "code": "from openai import OpenAI\nimport os\n\nif not os.environ.get(\"AILOOT_API_KEY\", \"\").strip(): raise SystemExit(\"Set AILOOT_API_KEY before making a request\")\nclient=OpenAI(base_url=\"https://api.ailoot.ai/v1\",api_key=os.environ[\"AILOOT_API_KEY\"],max_retries=0)\nresult=client.chat.completions.create(model=\"deepseek-vl2\", messages=[{\"role\":\"user\",\"content\":\"hello\"}], stream=False)\nimport base64, json\nfrom pathlib import Path\n\ndef save_result(data, stem=\"result\"):\n # Keep every field, including usage, IDs, reasoning and tool requests.\n Path(stem + \".json\").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n print(json.dumps(data, ensure_ascii=False, indent=2))\n def save_blob(encoded, suffix, label):\n raw = base64.b64decode(encoded, altchars=b\"-_\", validate=True)\n ext = \"png\" if raw.startswith(b\"\\x89PNG\\r\\n\\x1a\\n\") else \"jpg\" if raw.startswith(b\"\\xff\\xd8\") else \"webp\" if raw[:4] == b\"RIFF\" and raw[8:12] == b\"WEBP\" else suffix\n file = Path(stem + \"-\" + label + \".\" + ext)\n file.write_bytes(raw)\n print(\"Saved\", file)\n for index, block in enumerate(data.get(\"content\") or []):\n print(\"Content\", index, json.dumps(block, ensure_ascii=False))\n for choice in data.get(\"choices\") or []:\n print(\"Choice\", choice.get(\"index\"), json.dumps(choice.get(\"message\", choice.get(\"delta\", {})), ensure_ascii=False))\n for index, item in enumerate(data.get(\"output\") or []):\n print(\"Output\", index, json.dumps(item, ensure_ascii=False))\n if item.get(\"type\") == \"image_generation_call\" and item.get(\"result\"):\n save_blob(item[\"result\"], item.get(\"output_format\") or \"bin\", \"output-\" + str(index))\n for ci, candidate in enumerate(data.get(\"candidates\") or []):\n for pi, part in enumerate((candidate.get(\"content\") or {}).get(\"parts\") or []):\n print(\"Candidate/part\", ci, pi, json.dumps(part, ensure_ascii=False))\n blob = part.get(\"inlineData\") or part.get(\"inline_data\")\n if blob and blob.get(\"data\"):\n mime = blob.get(\"mimeType\") or blob.get(\"mime_type\")\n suffix = {\"image/png\":\"png\", \"image/jpeg\":\"jpg\", \"image/webp\":\"webp\", \"audio/wav\":\"wav\", \"audio/mpeg\":\"mp3\", \"video/mp4\":\"mp4\"}.get(mime, \"bin\")\n save_blob(blob[\"data\"], suffix, \"candidate-\" + str(ci) + \"-part-\" + str(pi))\n\nsave_result(result.to_dict(mode=\"json\"))\n", "label": "Python SDK · all choices", "language": "python" } ], "family": "openai", "fast_multiplier": null, "flex_multiplier": null, "fmt": "chat", "hints": [ "Send exact model ID with messages.", "Context length is not an output cap.", "A text file may supply literal text; image/audio/file-object inputs need exact binding." ], "http": "POST", "in": 0.33, "input": [ "text" ], "label": "OpenAI Chat", "max_out": null, "max_reasoning_effort_multiplier": null, "method_aliases": [ "/chat/completions" ], "out": 0.33, "output": [ "text" ], "path": "/v1/chat/completions", "price_conditions": [ { "label": "Fast mode", "value": "Not specified", "when": "When fast mode is selected" }, { "label": "Flex mode", "value": "Not specified", "when": "When flex mode is selected" }, { "label": "Maximum reasoning effort", "value": "Not specified", "when": "When maximum reasoning effort uses the default adjustment" } ], "pricing": { "billing_mode": "token", "image_input_price": null, "image_output_price": null, "per_request_price": null }, "pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Base token rates", "units": [ { "amount": "0.00033000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000033", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": "0.00033000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.00000033", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": "0", "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": "0", "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" } ] } ], "request_model_id": "deepseek-vl2", "route_label": "Ultra", "status_reads": [], "tiers": [], "units": [ { "amount": "0.00033000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000033", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": "0.00033000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.00000033", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": "0", "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": "0", "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" } ] }, { "available": null, "cache_read": 0, "cache_write": 0, "cache_write_1h": null, "contract": { "data": { "authentication": { "api_key_source": "AILOOT_API_KEY", "headers": [ "Authorization: Bearer", "x-api-key", "anthropic-version" ], "version_example": "2023-06-01" }, "errors": [ { "code": null, "condition": "Medan wajib, perwakilan bahan atau nilai parameter tidak sah", "http_status": 400, "recovery": "Betulkan medan yang ditunjukkan sebelum permintaan penciptaan baharu.", "type": "invalid_request_error" } ], "example_body": { "max_tokens": 128, "messages": [ { "content": "Summarize the incident report.", "role": "user" } ], "model": "deepseek-vl2", "stream": false }, "lifecycle": { "create": { "http": "POST", "path": "/v1/messages" }, "job_read": null, "mode": "synchronous_or_streamed", "unknown": "Kekalkan ID permintaan/respons dan pengepala. Tiada URL umum bagi pengambilan tugas teks dibekalkan." }, "material_combinations": [ { "mode": "text", "representation": "rentetan messages[].content", "required_material": "Teks UTF-8" } ], "operation_modes": [ "synchronous_json", "streamed_SSE" ], "outputs": { "all_results": "Kekalkan semua blok kandungan, tandatangan penaakulan dan ID penggunaan alat sebenar. Gabungkan semua delta blok SSE; blok teks pertama bukan keseluruhan output.", "family": "protocol", "fields": [ { "description": null, "name": "id", "type": "string" }, { "description": null, "name": "model", "type": "string" }, { "description": null, "name": "content[]", "type": "text|thinking|tool_use|other content block" }, { "description": null, "name": "stop_reason", "type": "string|null" }, { "description": null, "name": "usage", "type": "object" } ], "format": "JSON atau SSE" }, "parameters": [ { "availability": "source_interface", "default": null, "default_known": false, "enum": [ "deepseek-vl2" ], "enum_known": true, "name": "model", "required": true, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "messages", "required": true, "type": "message[]" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": [ "user", "assistant" ], "enum_known": true, "name": "messages[].role", "required": true, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "messages[].content", "required": true, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "max_tokens", "required": true, "type": "integer" }, { "availability": "source_interface", "default": false, "default_known": true, "enum": null, "enum_known": false, "name": "stream", "required": false, "type": "boolean" } ], "profile_binding": "Documented current stock Messages bridge; optional model capabilities are separate from this minimal text request.", "request_schema": { "additionalProperties": false, "properties": { "max_tokens": { "minimum": 1, "type": "integer" }, "messages": { "items": { "additionalProperties": false, "properties": { "content": { "oneOf": [ { "type": "string" }, { "items": { "oneOf": [ { "properties": { "text": { "type": "string" }, "type": { "const": "text" } }, "required": [ "type", "text" ], "type": "object" }, { "properties": { "source": { "oneOf": [ { "properties": { "type": { "const": "url" }, "url": { "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "properties": { "data": { "type": "string" }, "media_type": { "type": "string" }, "type": { "const": "base64" } }, "required": [ "type", "media_type", "data" ], "type": "object" } ] }, "type": { "const": "image" } }, "required": [ "type", "source" ], "type": "object" } ] }, "type": "array" } ] }, "role": { "enum": [ "user", "assistant" ], "type": "string" } }, "required": [ "role", "content" ], "type": "object" }, "minItems": 1, "type": "array" }, "model": { "enum": [ "deepseek-vl2" ], "type": "string" }, "stream": { "default": false, "type": "boolean" } }, "required": [ "model", "messages", "max_tokens" ], "type": "object" }, "schema_template": { "max_tokens": 128, "messages": [ { "content": "Summarize the incident report.", "role": "user" } ], "model": "deepseek-vl2", "stream": false }, "unsupported": [ "No query API key.", "Hosted/server-side tools and model-specific optional controls are not part of this minimal family example." ] }, "input": { "content_type": "application/json", "material_types": [ "text" ], "optional_controls": [ "stream" ], "required": [ "model", "messages", "max_tokens" ], "text_file_material": "Baca dokumen UTF-8 sebenar dan letakkan teksnya dalam rentetan input atau kandungan mesej yang ditunjukkan." }, "lifecycle": { "create": { "http": "POST", "path": "/v1/messages" }, "job_read": null, "mode": "synchronous_or_streamed", "unknown": "Keep request/response IDs and headers. No generic text job retrieval URL is supplied." }, "manufacturer_capability": null, "method": { "http": "POST", "path": "/v1/messages" }, "output": { "all_results": "Keep all content blocks, thinking signatures and actual tool-use IDs. Assemble all SSE block deltas; a first text block is not the complete output.", "family": "protocol", "format": "JSON or SSE" } }, "ctx": null, "declared_pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Kadar asas token", "units": [ { "amount": "0.00033000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000033", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": "0.00033000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.00000033", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": "0", "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": "0", "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" } ] } ], "examples": [ { "code": "{\n \"model\": \"deepseek-vl2\",\n \"max_tokens\": 128,\n \"messages\": [\n {\n \"role\": \"user\",\n \"content\": \"Summarize the incident report.\"\n }\n ],\n \"stream\": false\n}", "label": "Complete minimal JSON body", "language": "json" }, { "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\n: \"${AILOOT_TEXT_FILE:?Set your actual UTF-8 document}\"\nexport AILOOT_TEXT_FILE\npython3 - <<'PY_REQUEST'\nimport json, os\nfrom pathlib import Path\n\nsource_file = Path(os.environ['AILOOT_TEXT_FILE'])\nif not source_file.is_file():\n raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')\ntext = source_file.read_text(encoding='utf-8')\nif not text.strip():\n raise SystemExit('Choose a document containing text')\npayload = {'model': 'deepseek-vl2', 'max_tokens': 128, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]}\npayload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\\n\\n' + text\nPath('request.json').write_text(json.dumps(payload, ensure_ascii=False), encoding='utf-8')\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/messages \\\n -H \"x-api-key: $AILOOT_API_KEY\" -H 'anthropic-version: 2023-06-01' -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\n\nresult = json.loads(Path('result.json').read_text(encoding='utf-8'))\nprint(json.dumps(result, ensure_ascii=False, indent=2))\nPY_RESULT\n", "label": "curl · use your UTF-8 document", "language": "bash" }, { "code": "import json, os\nfrom pathlib import Path\n\nsource_file = Path(os.environ['AILOOT_TEXT_FILE'])\nif not source_file.is_file():\n raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')\ntext = source_file.read_text(encoding='utf-8')\nif not text.strip():\n raise SystemExit('Choose a document containing text')\npayload = {'model': 'deepseek-vl2', 'max_tokens': 128, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]}\npayload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\\n\\n' + text\nfrom urllib.request import Request, urlopen\nfrom urllib.error import HTTPError\n\napi_key = os.environ['AILOOT_API_KEY']\nif not api_key.strip():\n raise SystemExit('Set AILOOT_API_KEY before making a request')\nheaders = {'x-api-key': 'PLACEHOLDER_KEY', 'anthropic-version': '2023-06-01', 'Content-Type': 'application/json'}\nheaders['x-api-key'] = api_key\nrequest = Request('https://api.ailoot.ai/v1/messages', data=json.dumps(payload, ensure_ascii=False).encode('utf-8'), headers=headers, method='POST')\ntry:\n with urlopen(request, timeout=120) as response:\n raw = response.read()\n Path('response-headers.txt').write_text(str(response.headers), encoding='utf-8')\nexcept HTTPError as error:\n Path('result.json').write_bytes(error.read())\n Path('response-headers.txt').write_text(str(error.headers), encoding='utf-8')\n raise\nPath('result.json').write_bytes(raw)\nimport json\nfrom pathlib import Path\n\nresult = json.loads(Path('result.json').read_text(encoding='utf-8'))\nprint(json.dumps(result, ensure_ascii=False, indent=2))\n", "label": "Python · save the complete result", "language": "python" } ], "family": "anthropic", "fast_multiplier": null, "flex_multiplier": null, "fmt": "messages", "http": "POST", "in": 0.33, "input": [ "text" ], "label": "Anthropic Messages", "max_out": null, "max_reasoning_effort_multiplier": null, "method_aliases": [], "out": 0.33, "output": [ "json", "sse" ], "path": "/v1/messages", "price_conditions": [ { "label": "Fast mode", "value": "Not specified", "when": "When fast mode is selected" }, { "label": "Flex mode", "value": "Not specified", "when": "When flex mode is selected" }, { "label": "Maximum reasoning effort", "value": "Not specified", "when": "When maximum reasoning effort uses the default adjustment" } ], "pricing": { "billing_mode": "token", "image_input_price": null, "image_output_price": null, "per_request_price": null }, "pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Base token rates", "units": [ { "amount": "0.00033000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000033", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": "0.00033000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.00000033", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": "0", "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": "0", "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" } ] } ], "request_model_id": "deepseek-vl2", "route_label": "Ultra", "status_reads": [], "tiers": [], "units": [ { "amount": "0.00033000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000033", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": "0.00033000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.00000033", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": "0", "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": "0", "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" } ] } ], "model_aliases": [ { "canonical_model_id": "deepseek-vl2", "request_model_id": "deepseek-vl2", "route_label": "Ultra" } ], "name": "deepseek-ai/deepseek-vl2", "price": { "in": 0.33, "in_label": null, "out": 0.33, "out_label": null, "scope_label": "Token rates", "sort_in": 0.33, "sort_out": 0.33, "varies": false }, "short": "DeepSeek VL2 ialah model penglihatan-bahasa untuk soalan visual, OCR, dokumen, jadual, carta dan pemautan kepada sumber visual. Edisi penuh dibina berasaskan DeepSeekMoE-27B dengan 4.5B parameter diaktifkan; profil dihoskan ini menyenaraikan konteks 4,000 token untuk tugasan imej dan teks yang berfokus." }, "renderer_schema": { "method_order": "Original DTO methods order; every exact route/format/path/caller survives.", "prices": "Original public DTO fields and original native display HTML; no flattened or normalized offers." }, "representation": "exact-public-dto-and-native-renderer-html", "representationNotes": { "current_reference": { "about_json_pointer": "/aboutHtml", "methods": [ { "contract_json_pointer": "/methods/0/rawMethod/contract", "html_json_pointer": "/methods/0/html", "method_ordinal": 0 }, { "contract_json_pointer": "/methods/1/rawMethod/contract", "html_json_pointer": "/methods/1/html", "method_ordinal": 1 } ], "role": "current-semantic-reference" }, "raw_input": { "examples_role": "historical-input-not-current-ready-examples", "json_pointers": [ "/rawModel", "/methods/0/rawMethod", "/methods/1/rawMethod" ], "role": "historical-owner-input" }, "schema": "ailoot-native-model-representations-v1" }, "schema": "ailoot-static-model-facts-v1", "schema_version": 1, "shortText": "DeepSeek VL2 ialah model penglihatan-bahasa untuk soalan visual, OCR, dokumen, jadual, carta dan pemautan kepada sumber visual. Edisi penuh dibina berasaskan DeepSeekMoE-27B dengan 4.5B parameter diaktifkan; profil dihoskan ini menyenaraikan konteks 4,000 token untuk tugasan imej dan teks yang berfokus.", "source": { "audit_sha256": "2e00086bcbfc28e64d0cebe97aa47c17d8d5618963027f8ffb9dc6123d7d25b1", "bridge_map_sha256": "1125275768b9636fc8461bc7da5061239a81ddfe508e2557843ca42c58ab0036", "raw_model_json_sha256": "4bea3e19b76980d3c9853f97e429101827349b77e61f6e44a0a194bcc8b2f098", "renderer_sha256": "1dab5ba62521e9568c8b7c82005622cabbf330a075df7cce1f83274a04009460" }, "sourceRevision": "29232d22aae4a83547ca56618f5d563b5ec218c80a4ff935bbf39e3b8cf61231" }