{ "aboutHtml": "

Cohere Rerank 4 Pro ialah model yang mengutamakan kualiti dalam keluarga Rerank 4 Cohere. Model ini menilai dokumen calon berbanding pertanyaan dan mengembalikan susunan serta skor kerelevanannya. Model ini ditujukan kepada tugasan dapatan maklumat yang kompleks, termasuk dokumen berbilang bahasa dan maklumat separa berstruktur. Model ini sesuai digunakan dalam carian atau aliran kerja yang diperkaya dengan dapatan carian selepas sistem sedia ada mengumpulkan calon: hasilnya membantu memilih petikan untuk dipaparkan atau dibekalkan sebagai bukti kepada peringkat seterusnya.

Pengenal pengeluar ialah rerank-v4.0-pro, berasingan daripada rerank-v4.0-fast dan rerank-v3.5. Pro ditujukan kepada kes kompleks yang mengutamakan kualiti dapatan maklumat, manakala Fast menyasarkan kependaman lebih rendah dan daya pemprosesan lebih tinggi. Kedua-dua edisi Rerank 4 menggunakan konteks 32,768 token. Pertanyaan dan satu dokumen calon berkongsi bajet itu; ia bukan tetingkap penuh berasingan bagi setiap medan input atau had bagi seluruh korpus carian yang disimpan.

Bekalkan pertanyaan yang jelas dan senarai rentetan teks calon. Setiap hasil yang dikembalikan menyertakan indeks dokumen asal dan skor kerelevanan, dan top_n boleh memilih bilangan hasil berperingkat yang dikembalikan; tanpanya, pengeluar mengembalikan semua hasil. Simpan pengenal sumber di sisi setiap input supaya pemeringkatan boleh dihubungkan semula kepada petikan yang tepat. Untuk soalan sokongan, tanyakan syarat yang diperlukan dan bekalkan bahagian polisi calon, tanpa mengharapkan pemeringkat semula mencari polisi yang tiada dalam senarai.

Model ini menyokong lebih daripada 100 bahasa dan boleh memeringkat semula bahan bahasa Inggeris dan bahasa lain. Untuk pertanyaan dengan beberapa keperluan, nyatakan syarat yang menjadikan petikan berguna, seperti produk, situasi dan prosedur yang diperlukan. Untuk rekod separa berstruktur, Cohere mengesyorkan rentetan YAML dan bukannya objek mentah dalam senarai dokumen V2. Susun medan bermakna seperti tajuk dan kandungan secara sengaja, kerana pemotongan boleh membuang medan yang terkemudian. Penyediaan ini berguna untuk rekod produk, mesej dan metadata dokumen, selain prosa biasa.

Panduan penyediaan khusus model mengehadkan pertanyaan Rerank 4 kepada 16,384 token pertama. Panduan itu menerangkan pemprosesan dokumen lebih panjang melalui cebisan yang berkongsi konteks 32,768 token dengan pertanyaan dan token yang diperuntukkan. Rujukan V2 semasa secara berasingan menetapkan lalai max_tokens_per_doc kepada 4096 dan menyatakan bahawa rentetan dokumen lebih panjang dipotong mengikut tetapan itu. Anggap tetingkap model dan pemotongan dokumen dalam permintaan pengeluar sebagai dua kawalan berbeza. Sediakan petikan ringkas yang relevan atau pilih bajet dokumen yang sesuai, tanpa menganggap setiap sumber panjang sentiasa dibaca sepenuhnya.

Untuk prestasi permintaan yang disyorkan, Cohere menasihatkan agar tidak menghantar lebih daripada 1,000 dokumen sekali gus. Kekalkan pengenal calon apabila membahagikan senarai yang lebih besar kepada kumpulan yang mudah dikendalikan. Skor kerelevanan dinormalkan antara 0 dengan 1, tetapi bergantung pada pertanyaan dan calon; skor itu bukan ukuran linear ketepatan fakta. Gunakan susunan sebagai hasil utama dapatan maklumat. Jika menapis menggunakan ambang skor, tentukannya melalui pertanyaan yang mewakili penggunaan dan petikan yang hampir relevan dalam domain sebenar, tanpa menganggap ada ambang sejagat.

Artifak akhir yang berguna ialah senarai calon berperingkat yang membawa pengenal sumber dan skor, sedia untuk hasil carian atau langkah pemilihan bukti. Dokumen dengan skor kerelevanan tinggi masih boleh mengandungi maklumat lapuk atau tidak betul, jadi kekalkan sumber dan kandungannya untuk peringkat yang menggunakannya. Tujuan Pro ialah penilaian kerelevanan, bukannya penulisan jawapan bebas atau penjanaan embedding. Kekalkan edisi Pro yang tepat bersama hasil apabila membandingkan atau memperhalus aliran kerja dapatan maklumat.

", "aboutText": "Cohere Rerank 4 Pro ialah model yang mengutamakan kualiti dalam keluarga Rerank 4 Cohere. Model ini menilai dokumen calon berbanding pertanyaan dan mengembalikan susunan serta skor kerelevanannya. Model ini ditujukan kepada tugasan dapatan maklumat yang kompleks, termasuk dokumen berbilang bahasa dan maklumat separa berstruktur. Model ini sesuai digunakan dalam carian atau aliran kerja yang diperkaya dengan dapatan carian selepas sistem sedia ada mengumpulkan calon: hasilnya membantu memilih petikan untuk dipaparkan atau dibekalkan sebagai bukti kepada peringkat seterusnya.\n\nPengenal pengeluar ialah rerank-v4.0-pro, berasingan daripada rerank-v4.0-fast dan rerank-v3.5. Pro ditujukan kepada kes kompleks yang mengutamakan kualiti dapatan maklumat, manakala Fast menyasarkan kependaman lebih rendah dan daya pemprosesan lebih tinggi. Kedua-dua edisi Rerank 4 menggunakan konteks 32,768 token. Pertanyaan dan satu dokumen calon berkongsi bajet itu; ia bukan tetingkap penuh berasingan bagi setiap medan input atau had bagi seluruh korpus carian yang disimpan.\n\nBekalkan pertanyaan yang jelas dan senarai rentetan teks calon. Setiap hasil yang dikembalikan menyertakan indeks dokumen asal dan skor kerelevanan, dan top_n boleh memilih bilangan hasil berperingkat yang dikembalikan; tanpanya, pengeluar mengembalikan semua hasil. Simpan pengenal sumber di sisi setiap input supaya pemeringkatan boleh dihubungkan semula kepada petikan yang tepat. Untuk soalan sokongan, tanyakan syarat yang diperlukan dan bekalkan bahagian polisi calon, tanpa mengharapkan pemeringkat semula mencari polisi yang tiada dalam senarai.\n\nModel ini menyokong lebih daripada 100 bahasa dan boleh memeringkat semula bahan bahasa Inggeris dan bahasa lain. Untuk pertanyaan dengan beberapa keperluan, nyatakan syarat yang menjadikan petikan berguna, seperti produk, situasi dan prosedur yang diperlukan. Untuk rekod separa berstruktur, Cohere mengesyorkan rentetan YAML dan bukannya objek mentah dalam senarai dokumen V2. Susun medan bermakna seperti tajuk dan kandungan secara sengaja, kerana pemotongan boleh membuang medan yang terkemudian. Penyediaan ini berguna untuk rekod produk, mesej dan metadata dokumen, selain prosa biasa.\n\nPanduan penyediaan khusus model mengehadkan pertanyaan Rerank 4 kepada 16,384 token pertama. Panduan itu menerangkan pemprosesan dokumen lebih panjang melalui cebisan yang berkongsi konteks 32,768 token dengan pertanyaan dan token yang diperuntukkan. Rujukan V2 semasa secara berasingan menetapkan lalai max_tokens_per_doc kepada 4096 dan menyatakan bahawa rentetan dokumen lebih panjang dipotong mengikut tetapan itu. Anggap tetingkap model dan pemotongan dokumen dalam permintaan pengeluar sebagai dua kawalan berbeza. Sediakan petikan ringkas yang relevan atau pilih bajet dokumen yang sesuai, tanpa menganggap setiap sumber panjang sentiasa dibaca sepenuhnya.\n\nUntuk prestasi permintaan yang disyorkan, Cohere menasihatkan agar tidak menghantar lebih daripada 1,000 dokumen sekali gus. Kekalkan pengenal calon apabila membahagikan senarai yang lebih besar kepada kumpulan yang mudah dikendalikan. Skor kerelevanan dinormalkan antara 0 dengan 1, tetapi bergantung pada pertanyaan dan calon; skor itu bukan ukuran linear ketepatan fakta. Gunakan susunan sebagai hasil utama dapatan maklumat. Jika menapis menggunakan ambang skor, tentukannya melalui pertanyaan yang mewakili penggunaan dan petikan yang hampir relevan dalam domain sebenar, tanpa menganggap ada ambang sejagat.\n\nArtifak akhir yang berguna ialah senarai calon berperingkat yang membawa pengenal sumber dan skor, sedia untuk hasil carian atau langkah pemilihan bukti. Dokumen dengan skor kerelevanan tinggi masih boleh mengandungi maklumat lapuk atau tidak betul, jadi kekalkan sumber dan kandungannya untuk peringkat yang menggunakannya. Tujuan Pro ialah penilaian kerelevanan, bukannya penulisan jawapan bebas atau penjanaan embedding. Kekalkan edisi Pro yang tepat bersama hasil apabila membandingkan atau memperhalus aliran kerja dapatan maklumat.", "editorialOverlay": { "copy_fields": null, "copy_sha256": null, "copy_source": null, "html_sha256": "796bfc13b009ad4252c3235bf6396ddb986953fe6c3aff0238cb7bbf75cb0ad0", "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06", "raw_model_unchanged": true, "schema": "ailoot-native-model-editorial-v1" }, "locale": "ms", "methods": [ { "html": "
•

Cohere: Rerank 4 Pro

Cohere /cohere/rerank-4-pro

Pengenalan

Cohere Rerank 4 Pro menyusun dokumen calon mengikut kerelevanan kepada pertanyaan untuk carian dan aliran kerja yang diperkaya dengan dapatan carian. Model ini ialah edisi Rerank 4 yang mengutamakan kualiti, dengan konteks 32K, liputan berbilang bahasa dan sokongan teks separa berstruktur.

Input yang diterima
Teks
Hasil
Hasil mengikut kedudukan
Tentang model

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

Cohere Rerank 4 Pro ialah model yang mengutamakan kualiti dalam keluarga Rerank 4 Cohere. Model ini menilai dokumen calon berbanding pertanyaan dan mengembalikan susunan serta skor kerelevanannya. Model ini ditujukan kepada tugasan dapatan maklumat yang kompleks, termasuk dokumen berbilang bahasa dan maklumat separa berstruktur. Model ini sesuai digunakan dalam carian atau aliran kerja yang diperkaya dengan dapatan carian selepas sistem sedia ada mengumpulkan calon: hasilnya membantu memilih petikan untuk dipaparkan atau dibekalkan sebagai bukti kepada peringkat seterusnya.

Pengenal pengeluar ialah rerank-v4.0-pro, berasingan daripada rerank-v4.0-fast dan rerank-v3.5. Pro ditujukan kepada kes kompleks yang mengutamakan kualiti dapatan maklumat, manakala Fast menyasarkan kependaman lebih rendah dan daya pemprosesan lebih tinggi. Kedua-dua edisi Rerank 4 menggunakan konteks 32,768 token. Pertanyaan dan satu dokumen calon berkongsi bajet itu; ia bukan tetingkap penuh berasingan bagi setiap medan input atau had bagi seluruh korpus carian yang disimpan.

Bekalkan pertanyaan yang jelas dan senarai rentetan teks calon. Setiap hasil yang dikembalikan menyertakan indeks dokumen asal dan skor kerelevanan, dan top_n boleh memilih bilangan hasil berperingkat yang dikembalikan; tanpanya, pengeluar mengembalikan semua hasil. Simpan pengenal sumber di sisi setiap input supaya pemeringkatan boleh dihubungkan semula kepada petikan yang tepat. Untuk soalan sokongan, tanyakan syarat yang diperlukan dan bekalkan bahagian polisi calon, tanpa mengharapkan pemeringkat semula mencari polisi yang tiada dalam senarai.

Model ini menyokong lebih daripada 100 bahasa dan boleh memeringkat semula bahan bahasa Inggeris dan bahasa lain. Untuk pertanyaan dengan beberapa keperluan, nyatakan syarat yang menjadikan petikan berguna, seperti produk, situasi dan prosedur yang diperlukan. Untuk rekod separa berstruktur, Cohere mengesyorkan rentetan YAML dan bukannya objek mentah dalam senarai dokumen V2. Susun medan bermakna seperti tajuk dan kandungan secara sengaja, kerana pemotongan boleh membuang medan yang terkemudian. Penyediaan ini berguna untuk rekod produk, mesej dan metadata dokumen, selain prosa biasa.

Panduan penyediaan khusus model mengehadkan pertanyaan Rerank 4 kepada 16,384 token pertama. Panduan itu menerangkan pemprosesan dokumen lebih panjang melalui cebisan yang berkongsi konteks 32,768 token dengan pertanyaan dan token yang diperuntukkan. Rujukan V2 semasa secara berasingan menetapkan lalai max_tokens_per_doc kepada 4096 dan menyatakan bahawa rentetan dokumen lebih panjang dipotong mengikut tetapan itu. Anggap tetingkap model dan pemotongan dokumen dalam permintaan pengeluar sebagai dua kawalan berbeza. Sediakan petikan ringkas yang relevan atau pilih bajet dokumen yang sesuai, tanpa menganggap setiap sumber panjang sentiasa dibaca sepenuhnya.

Untuk prestasi permintaan yang disyorkan, Cohere menasihatkan agar tidak menghantar lebih daripada 1,000 dokumen sekali gus. Kekalkan pengenal calon apabila membahagikan senarai yang lebih besar kepada kumpulan yang mudah dikendalikan. Skor kerelevanan dinormalkan antara 0 dengan 1, tetapi bergantung pada pertanyaan dan calon; skor itu bukan ukuran linear ketepatan fakta. Gunakan susunan sebagai hasil utama dapatan maklumat. Jika menapis menggunakan ambang skor, tentukannya melalui pertanyaan yang mewakili penggunaan dan petikan yang hampir relevan dalam domain sebenar, tanpa menganggap ada ambang sejagat.

Artifak akhir yang berguna ialah senarai calon berperingkat yang membawa pengenal sumber dan skor, sedia untuk hasil carian atau langkah pemilihan bukti. Dokumen dengan skor kerelevanan tinggi masih boleh mengandungi maklumat lapuk atau tidak betul, jadi kekalkan sumber dan kandungannya untuk peringkat yang menggunakannya. Tujuan Pro ialah penilaian kerelevanan, bukannya penulisan jawapan bebas atau penjanaan embedding. Kekalkan edisi Pro yang tepat bersama hasil apabila membandingkan atau memperhalus aliran kerja dapatan maklumat.

Tersedia di Ailoot · input & kaedah

Ailoot Ultra · Pemeringkatan semula POST /v1/rerank
Input
text
Output
rerank
Format output
json
MedanJenisKeperluanNilai & hadLalai
MedandocumentsJenisarrayKeperluanDiperlukanNilai & hadLalai—
MedanmodelJenisstringKeperluanDiperlukanNilai & had
cohere/rerank-4-pro
Lalai—
MedanproviderJenisschemaKeperluanPilihanNilai & hadLalai—
MedanqueryJenisstringKeperluanDiperlukanNilai & hadLalai—
Medansession_idJenisstringKeperluanPilihanNilai & hadLalai—
Medantop_nJenisintegerKeperluanPilihanNilai & had
Minimum: 1
Lalai—
MedantraceJenisschemaKeperluanPilihanNilai & hadLalai—
MedanuserJenisstringKeperluanPilihanNilai & hadLalai—
Medandocuments[].textJenisstringKeperluanPilihanNilai & hadLalai—
Medandocuments[].imageJenisstringKeperluanPilihanNilai & hadLalai—
Gabungan bahan input

text_rerank · Diperlukan: query, documents array with minItems1

Parameter kaedah dan kitar hayat
ParameterKontrak
Parametercontent_typeKontrakapplication/json
ParameterrequiredKontrakmodel, query, documents
Parametermaterial_typesKontraktext
Parameteroptional_controlsKontrakprovider, session_id, top_n, trace, user
Parametermultimodal_input_bindingKontraknull

Kitar hayat: synchronous

Hantar ID model yang tepat dan simpan respons natif yang lengkap.

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

Padankan setiap caj dengan syarat yang dipaparkan. Gunakan satu kadar bagi setiap set konfigurasi alternatif.

KonfigurasiJadual kadarUnit pengebilanCaj
KonfigurasiKadar asas tokenJadual kadarKadar asasUnit pengebilan—Caj
Input · Token input — / 1K token
Output · Token output — / 1K token
Input · Bacaan cache — / 1K token
Input · Penulisan cache — / 1K token
Input · Penulisan cache, 1 jam — / 1K token
KonfigurasiUltraJadual kadar—Unit pengebilanbagi setiap unit carianCaj
total · unit carian $0.005 bagi setiap unit carian

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 · Pemeringkatan semula

{\n  "documents": [\n    "Paris is the capital of France.",\n    "Berlin is the capital of Germany."\n  ],\n  "model": "cohere/rerank-4-pro",\n  "query": "What is the capital of France?",\n  "top_n": 3\n}

Terima dan simpan hasil

Ailoot Ultra · Pemeringkatan semula

json

Simpan semua hasil yang selesai

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

Setiap indeks hasil sebenar, relevance_score terhingga dan objek dokumen asal dikekalkan. Indeks mengikat tatasusunan dokumen asal. Keseluruhan JSON asal disimpan dan hanya dikembalikan selepas penyelesaian caj.

Jika respons asal perlu disemak

Simpan operasi asal dan balasan penuh/separa; jangan ulang POST.

Medan respons
MedanJenis
MedanmodelJenisstring
MedanidJenisstring
MedanproviderJenisstring
Medanresults[].indexJenisinteger
Medanresults[].relevance_scoreJenisnumber
Medanresults[].document.textJenisstring
Medanresults[].document.imageJenisstring
Medanusage.search_unitsJenisinteger
Medanusage.total_tokensJenisinteger
Medanusage.costJenisnumber
Ralat dan langkah seterusnya
ResponsSyaratLangkah seterusnya
Respons400 · invalid_request_errorSyaratMedan wajib, bentuk perwakilan atau nilai tidak sahLangkah seterusnyaBetulkan medan permintaan yang dinyatakan.

Alias

Alias model

Ailoot Ultra cohere/rerank-4-pro

Alias laluan kaedah

Gunakan laluan kaedah yang dipaparkan di atas.

ID pemanggil berkaitan

cohere/rerank-4-pro

", "method_identity": { "effective_request_model_id": "cohere/rerank-4-pro", "fmt": "rerank", "http": "POST", "path": "/v1/rerank", "request_model_id": "cohere/rerank-4-pro", "route_label": "Ultra" }, "method_key": "Ultra|rerank|/v1/rerank|cohere%2Frerank-4-pro", "ordinal": 0, "rawMethod": { "available": null, "cache_read": null, "cache_write": null, "cache_write_1h": null, "contract": { "data": { "all_results": "Every actual result index, finite relevance_score and original document object is retained. Indices bind the original document array. Entire original JSON is stored and returned only after settlement.", "errors": [ { "code": null, "condition": "Medan wajib, bentuk perwakilan atau nilai tidak sah", "http_status": 400, "recovery": "Betulkan medan permintaan yang dinyatakan.", "type": "invalid_request_error" } ], "hints": [], "ignored_fields": [], "lifecycle": { "cancel": null, "idempotency_header": null, "job_retrieve": null, "mode": "synchronous", "streaming": false, "unknown": "Simpan operasi asal dan balasan penuh/separa; jangan ulang POST." }, "material_combinations": [ { "accepted": [ "string documents", "objects with text; image only for separately exact-supported multimodal model" ], "mode": "text_rerank", "required": [ "query", "documents array with minItems1" ] } ], "operation_modes": [ "rerank" ], "outputs": { "all_results": "Setiap indeks hasil sebenar, relevance_score terhingga dan objek dokumen asal dikekalkan. Indeks mengikat tatasusunan dokumen asal. Keseluruhan JSON asal disimpan dan hanya dikembalikan selepas penyelesaian caj.", "family": "rerank", "fields": [ { "description": null, "name": "model", "type": "string" }, { "description": null, "name": "id", "type": "string" }, { "description": null, "name": "provider", "type": "string" }, { "description": null, "name": "results[].index", "type": "integer" }, { "description": null, "name": "results[].relevance_score", "type": "number" }, { "description": null, "name": "results[].document.text", "type": "string" }, { "description": null, "name": "results[].document.image", "type": "string" }, { "description": null, "name": "usage.search_units", "type": "integer" }, { "description": null, "name": "usage.total_tokens", "type": "integer" }, { "description": null, "name": "usage.cost", "type": "number" } ], "format": "json", "schema": { "description": "Rerank response containing ranked results", "example": { "id": "gen-rerank-1234567890-abc", "model": "cohere/rerank-v3.5", "results": [ { "document": { "text": "Paris is the capital of France." }, "index": 0, "relevance_score": 0.98 } ], "usage": { "search_units": 1, "total_tokens": 150 } }, "properties": { "id": { "description": "Unique identifier for the rerank response (ORID format)", "example": "gen-rerank-1234567890-abc", "type": "string" }, "model": { "description": "The model used for reranking", "example": "cohere/rerank-v3.5", "type": "string" }, "provider": { "description": "The provider that served the rerank request", "example": "Cohere", "type": "string" }, "results": { "description": "List of rerank results sorted by relevance", "example": [ { "document": { "text": "Paris is the capital of France." }, "index": 0, "relevance_score": 0.98 } ], "items": { "description": "A single rerank result", "example": { "document": { "text": "Paris is the capital of France." }, "index": 0, "relevance_score": 0.98 }, "properties": { "document": { "description": "The document object echoing the original input (text and/or image)", "properties": { "image": { "description": "The image (URL or data URI) from the original document", "example": "https://example.com/image.png", "type": "string" }, "text": { "description": "The document text", "example": "Paris is the capital of France.", "type": "string" } }, "type": "object" }, "index": { "description": "Index of the document in the original input list", "example": 0, "type": "integer" }, "relevance_score": { "description": "Relevance score of the document to the query", "example": 0.98, "format": "double", "type": "number" } }, "required": [ "index", "relevance_score", "document" ], "type": "object" }, "type": "array" }, "usage": { "description": "Usage statistics", "example": { "search_units": 1, "total_tokens": 150 }, "properties": { "cost": { "description": "Cost of the request in credits", "example": 0.001, "format": "double", "type": "number" }, "search_units": { "description": "Number of search units consumed (Cohere billing)", "example": 1, "type": "integer" }, "total_tokens": { "description": "Total number of tokens used", "example": 150, "type": "integer" } }, "type": "object" } }, "required": [ "model", "results" ], "type": "object" } }, "parameters": [ { "availability": "source_interface", "default": null, "default_known": false, "description": "The list of documents to rerank. Documents may be plain strings, or structured objects with `text` and/or `image` for multimodal models.", "enum": null, "enum_known": false, "minItems": 1, "name": "documents", "required": true, "type": "array" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": [ "cohere/rerank-4-pro" ], "enum_known": true, "name": "model", "required": true, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "provider", "required": false, "type": "schema" }, { "availability": "source_interface", "default": null, "default_known": false, "description": "The search query to rerank documents against", "enum": null, "enum_known": false, "name": "query", "required": true, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "description": "A unique identifier for grouping related requests (e.g., a conversation or agent workflow). Used for observability grouping in Broadcast and private logging; never sent to the provider. If provided in both the request body and the x-session-id header, the body value takes precedence. Maximum of 256 characters.", "enum": null, "enum_known": false, "maxLength": 256, "name": "session_id", "required": false, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "description": "Number of most relevant documents to return", "enum": null, "enum_known": false, "minimum": 1, "name": "top_n", "required": false, "type": "integer" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "trace", "required": false, "type": "schema" }, { "availability": "source_interface", "default": null, "default_known": false, "description": "A unique identifier representing your end-user. Forwarded to Broadcast and private logging as the end-user id; never sent to the provider.", "enum": null, "enum_known": false, "maxLength": 256, "name": "user", "required": false, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "documents[].text", "required": false, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "description": "Official family image carrier is remote http/https URL or data:image URI; only for exact multimodal-model bindings.", "enum": null, "enum_known": false, "name": "documents[].image", "required": false, "type": "string" } ], "unsupported": [ "stream=true" ] }, "hints": [ "Hantar ID model yang tepat dan simpan respons natif yang lengkap." ], "input": { "content_type": "application/json", "material_types": [ "text" ], "multimodal_input_binding": null, "optional_controls": [ "provider", "session_id", "top_n", "trace", "user" ], "required": [ "model", "query", "documents" ] }, "lifecycle": { "cancel": null, "idempotency_header": null, "job_retrieve": null, "mode": "synchronous", "streaming": false, "unknown": "Preserve original operation and full/partial reply; do not replay POST." }, "manufacturer_capability": { "context_length": 32768, "manufacturer_input": [ "text" ], "manufacturer_output": [ "rerank" ] }, "method": { "http": "POST", "path": "/v1/rerank" }, "method_aliases": [], "operation_modes": [ "rerank" ], "output": { "all_results": "Every actual result index, finite relevance_score and original document object is retained. Indices bind the original document array. Entire original JSON is stored and returned only after settlement.", "family": "rerank", "fields": [ { "description": null, "name": "model", "type": "string" }, { "description": null, "name": "id", "type": "string" }, { "description": null, "name": "provider", "type": "string" }, { "description": null, "name": "results[].index", "type": "integer" }, { "description": null, "name": "results[].relevance_score", "type": "number" }, { "description": null, "name": "results[].document.text", "type": "string" }, { "description": null, "name": "results[].document.image", "type": "string" }, { "description": null, "name": "usage.search_units", "type": "integer" }, { "description": null, "name": "usage.total_tokens", "type": "integer" }, { "description": null, "name": "usage.cost", "type": "number" } ], "format": "json", "schema": { "description": "Rerank response containing ranked results", "example": { "id": "gen-rerank-1234567890-abc", "model": "cohere/rerank-v3.5", "results": [ { "document": { "text": "Paris is the capital of France." }, "index": 0, "relevance_score": 0.98 } ], "usage": { "search_units": 1, "total_tokens": 150 } }, "properties": { "id": { "description": "Unique identifier for the rerank response (ORID format)", "example": "gen-rerank-1234567890-abc", "type": "string" }, "model": { "description": "The model used for reranking", "example": "cohere/rerank-v3.5", "type": "string" }, "provider": { "description": "The provider that served the rerank request", "example": "Cohere", "type": "string" }, "results": { "description": "List of rerank results sorted by relevance", "example": [ { "document": { "text": "Paris is the capital of France." }, "index": 0, "relevance_score": 0.98 } ], "items": { "description": "A single rerank result", "example": { "document": { "text": "Paris is the capital of France." }, "index": 0, "relevance_score": 0.98 }, "properties": { "document": { "description": "The document object echoing the original input (text and/or image)", "properties": { "image": { "description": "The image (URL or data URI) from the original document", "example": "https://example.com/image.png", "type": "string" }, "text": { "description": "The document text", "example": "Paris is the capital of France.", "type": "string" } }, "type": "object" }, "index": { "description": "Index of the document in the original input list", "example": 0, "type": "integer" }, "relevance_score": { "description": "Relevance score of the document to the query", "example": 0.98, "format": "double", "type": "number" } }, "required": [ "index", "relevance_score", "document" ], "type": "object" }, "type": "array" }, "usage": { "description": "Usage statistics", "example": { "search_units": 1, "total_tokens": 150 }, "properties": { "cost": { "description": "Cost of the request in credits", "example": 0.001, "format": "double", "type": "number" }, "search_units": { "description": "Number of search units consumed (Cohere billing)", "example": 1, "type": "integer" }, "total_tokens": { "description": "Total number of tokens used", "example": 150, "type": "integer" } }, "type": "object" } }, "required": [ "model", "results" ], "type": "object" } }, "schema": { "request": { "description": "Rerank request input", "example": { "documents": [ "Paris is the capital of France.", "Berlin is the capital of Germany." ], "model": "cohere/rerank-v3.5", "query": "What is the capital of France?", "top_n": 3 }, "properties": { "documents": { "description": "The list of documents to rerank. Documents may be plain strings, or structured objects with `text` and/or `image` for multimodal models.", "example": [ "Paris is the capital of France.", "Berlin is the capital of Germany." ], "items": { "anyOf": [ { "type": "string" }, { "description": "A structured document with optional text and/or image content. At least one of `text` or `image` must be provided.", "properties": { "image": { "description": "An image associated with the document, as a remote URL (http/https) or a base64-encoded data URI (data:image/...).", "example": "https://upload.wikimedia.org/wikipedia/commons/thumb/8/8b/Phytogenic.png", "type": "string" }, "text": { "description": "The document text", "example": "AI enables robots to perceive, plan, and act autonomously.", "type": "string" } }, "type": "object" } ], "description": "A document to rerank. Either a plain string, or a structured object with optional `text` and/or `image`." }, "minItems": 1, "type": "array" }, "model": { "enum": [ "cohere/rerank-4-pro" ], "type": "string" }, "provider": { "allOf": [ { "$ref": "#/components/schemas/ProviderPreferences" }, { "description": "Provider routing preferences for the request." } ] }, "query": { "description": "The search query to rerank documents against", "example": "What is the capital of France?", "type": "string" }, "session_id": { "description": "A unique identifier for grouping related requests (e.g., a conversation or agent workflow). Used for observability grouping in Broadcast and private logging; never sent to the provider. If provided in both the request body and the x-session-id header, the body value takes precedence. Maximum of 256 characters.", "example": "session-1234", "maxLength": 256, "type": "string" }, "top_n": { "description": "Number of most relevant documents to return", "example": 3, "minimum": 1, "type": "integer" }, "trace": { "$ref": "#/components/schemas/TraceConfig" }, "user": { "description": "A unique identifier representing your end-user. Forwarded to Broadcast and private logging as the end-user id; never sent to the provider.", "example": "user-1234", "maxLength": 256, "type": "string" } }, "required": [ "model", "query", "documents" ], "type": "object" }, "response": { "description": "Rerank response containing ranked results", "example": { "id": "gen-rerank-1234567890-abc", "model": "cohere/rerank-v3.5", "results": [ { "document": { "text": "Paris is the capital of France." }, "index": 0, "relevance_score": 0.98 } ], "usage": { "search_units": 1, "total_tokens": 150 } }, "properties": { "id": { "description": "Unique identifier for the rerank response (ORID format)", "example": "gen-rerank-1234567890-abc", "type": "string" }, "model": { "description": "The model used for reranking", "example": "cohere/rerank-v3.5", "type": "string" }, "provider": { "description": "The provider that served the rerank request", "example": "Cohere", "type": "string" }, "results": { "description": "List of rerank results sorted by relevance", "example": [ { "document": { "text": "Paris is the capital of France." }, "index": 0, "relevance_score": 0.98 } ], "items": { "description": "A single rerank result", "example": { "document": { "text": "Paris is the capital of France." }, "index": 0, "relevance_score": 0.98 }, "properties": { "document": { "description": "The document object echoing the original input (text and/or image)", "properties": { "image": { "description": "The image (URL or data URI) from the original document", "example": "https://example.com/image.png", "type": "string" }, "text": { "description": "The document text", "example": "Paris is the capital of France.", "type": "string" } }, "type": "object" }, "index": { "description": "Index of the document in the original input list", "example": 0, "type": "integer" }, "relevance_score": { "description": "Relevance score of the document to the query", "example": 0.98, "format": "double", "type": "number" } }, "required": [ "index", "relevance_score", "document" ], "type": "object" }, "type": "array" }, "usage": { "description": "Usage statistics", "example": { "search_units": 1, "total_tokens": 150 }, "properties": { "cost": { "description": "Cost of the request in credits", "example": 0.001, "format": "double", "type": "number" }, "search_units": { "description": "Number of search units consumed (Cohere billing)", "example": 1, "type": "integer" }, "total_tokens": { "description": "Total number of tokens used", "example": 150, "type": "integer" } }, "type": "object" } }, "required": [ "model", "results" ], "type": "object" } } }, "ctx": null, "declared_pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Base token rates", "units": [ { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": null, "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": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": null, "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": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": null, "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": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": null, "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" } ] }, { "dimensions": { "native_endpoint": "rerank", "quoted_unit": "per search unit" }, "label": "Ultra", "units": [ { "amount": "0.005", "amount_unit": "usd_per_declared_unit", "billing_input": true, "estimate_only": false, "key": "search_unit", "label": "search unit", "native_amount_usd_per_unit": "0.005", "native_quantity": 1, "public_unit_label": "per search unit", "quantity": "1", "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "total", "unit": "search_unit" } ] } ], "examples": [ { "code": "{\n \"documents\": [\n \"Paris is the capital of France.\",\n \"Berlin is the capital of Germany.\"\n ],\n \"model\": \"cohere/rerank-4-pro\",\n \"query\": \"What is the capital of France?\",\n \"top_n\": 3\n}", "label": "Retained official request structure; documentary example", "language": "json" }, { "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\n: \"${AILOOT_REQUEST_FILE:?Set your actual JSON request file}\"\nexport AILOOT_REQUEST_FILE\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads(Path(os.environ['AILOOT_REQUEST_FILE']).read_text(encoding='utf-8'))\nif not isinstance(p,dict): raise SystemExit('Request must be a JSON object')\np['model']='cohere/rerank-4-pro'\nPath('request.json').write_text(json.dumps(p,ensure_ascii=False),encoding='utf-8')\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/rerank \\\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\nprint(json.dumps(json.loads(Path('result.json').read_text()),ensure_ascii=False,indent=2))\nPY_RESULT\n", "label": "Use your actual request material and retain the complete result", "language": "bash", "material_policy": "Use real owner-supplied state/questions or query/documents. No invented file ID, document, image URL, job ID or returned result." } ], "family": "rerank", "fast_multiplier": null, "flex_multiplier": null, "fmt": "rerank", "http": "POST", "in": null, "input": [ "text" ], "label": "Pemeringkatan semula", "max_out": null, "max_reasoning_effort_multiplier": null, "method_aliases": [], "native_endpoint": "rerank", "out": null, "output": [ "rerank" ], "path": "/v1/rerank", "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": "per_request", "image_input_price": null, "image_output_price": null, "per_request_price": null, "unit_prices": { "search_unit": 0.005 } }, "pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Base token rates", "units": [ { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": null, "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": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": null, "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": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": null, "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": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": null, "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" } ] }, { "dimensions": { "native_endpoint": "rerank", "quoted_unit": "per search unit" }, "label": "Ultra", "units": [ { "amount": "0.005", "amount_unit": "usd_per_declared_unit", "billing_input": true, "estimate_only": false, "key": "search_unit", "label": "search unit", "native_amount_usd_per_unit": "0.005", "native_quantity": 1, "public_unit_label": "per search unit", "quantity": "1", "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "total", "unit": "search_unit" } ] } ], "request_model_id": "cohere/rerank-4-pro", "route_label": "Ultra", "status_reads": [], "tiers": [], "units": [ { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": null, "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": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": null, "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": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": null, "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": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": null, "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" }, { "amount": "0.005", "amount_unit": "usd_per_declared_unit", "billing_input": true, "estimate_only": false, "key": "search_unit", "label": "search unit", "native_amount_usd_per_unit": "0.005", "native_quantity": 1, "public_unit_label": "per search unit", "quantity": "1", "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "total", "unit": "search_unit" } ] } } ], "model_id": "cohere/rerank-4-pro", "nativeProvenance": { "input_facts_canonical_sha256": "24c73a08abcbfb16afd47a31d4c584cc32caf6ce69633165993ed02b4e5454af", "input_html_sha256": "ca152dea9bebe50ab417fe45c5b3626578fa016c466f73219d07243f7ae49d2b", "private_audit_sha256": "7df88be5d90c1269d8df190b646ed5aff9ac437b3d59cce8b5d40abdbf968294", "role": "historical-normalized-input-audit-hashes", "schema": "ailoot-native-model-provenance-v2" }, "page": { "canonical": "https://ailoot.ai/models/ms/cohere-rerank-4-pro/", "description": "Cohere Rerank 4 Pro menyusun dokumen calon mengikut kerelevanan kepada pertanyaan untuk carian dan aliran kerja yang diperkaya dengan dapatan carian. Model ini ialah edisi Rerank 4 yang mengutamakan kualiti, dengan konteks 32K, liputan berbilang bahasa dan sokongan teks separa berstruktur.", "locale": "ms", "md_url": "https://ailoot.ai/models/ms/cohere-rerank-4-pro/index.md", "title": "Cohere: Rerank 4 Pro — Ailoot", "url": "https://ailoot.ai/models/ms/cohere-rerank-4-pro/" }, "rawModel": { "about": "Cohere Rerank 4 Pro ialah model yang mengutamakan kualiti dalam keluarga Rerank 4 Cohere. Model ini menilai dokumen calon berbanding pertanyaan dan mengembalikan susunan serta skor kerelevanannya. Model ini ditujukan kepada tugasan dapatan maklumat yang kompleks, termasuk dokumen berbilang bahasa dan maklumat separa berstruktur. Model ini sesuai digunakan dalam carian atau aliran kerja yang diperkaya dengan dapatan carian selepas sistem sedia ada mengumpulkan calon: hasilnya membantu memilih petikan untuk dipaparkan atau dibekalkan sebagai bukti kepada peringkat seterusnya.\n\nPengenal pengeluar ialah rerank-v4.0-pro, berasingan daripada rerank-v4.0-fast dan rerank-v3.5. Pro ditujukan kepada kes kompleks yang mengutamakan kualiti dapatan maklumat, manakala Fast menyasarkan kependaman lebih rendah dan daya pemprosesan lebih tinggi. Kedua-dua edisi Rerank 4 menggunakan konteks 32,768 token. Pertanyaan dan satu dokumen calon berkongsi bajet itu; ia bukan tetingkap penuh berasingan bagi setiap medan input atau had bagi seluruh korpus carian yang disimpan.\n\nBekalkan pertanyaan yang jelas dan senarai rentetan teks calon. Setiap hasil yang dikembalikan menyertakan indeks dokumen asal dan skor kerelevanan, dan top_n boleh memilih bilangan hasil berperingkat yang dikembalikan; tanpanya, pengeluar mengembalikan semua hasil. Simpan pengenal sumber di sisi setiap input supaya pemeringkatan boleh dihubungkan semula kepada petikan yang tepat. Untuk soalan sokongan, tanyakan syarat yang diperlukan dan bekalkan bahagian polisi calon, tanpa mengharapkan pemeringkat semula mencari polisi yang tiada dalam senarai.\n\nModel ini menyokong lebih daripada 100 bahasa dan boleh memeringkat semula bahan bahasa Inggeris dan bahasa lain. Untuk pertanyaan dengan beberapa keperluan, nyatakan syarat yang menjadikan petikan berguna, seperti produk, situasi dan prosedur yang diperlukan. Untuk rekod separa berstruktur, Cohere mengesyorkan rentetan YAML dan bukannya objek mentah dalam senarai dokumen V2. Susun medan bermakna seperti tajuk dan kandungan secara sengaja, kerana pemotongan boleh membuang medan yang terkemudian. Penyediaan ini berguna untuk rekod produk, mesej dan metadata dokumen, selain prosa biasa.\n\nPanduan penyediaan khusus model mengehadkan pertanyaan Rerank 4 kepada 16,384 token pertama. Panduan itu menerangkan pemprosesan dokumen lebih panjang melalui cebisan yang berkongsi konteks 32,768 token dengan pertanyaan dan token yang diperuntukkan. Rujukan V2 semasa secara berasingan menetapkan lalai max_tokens_per_doc kepada 4096 dan menyatakan bahawa rentetan dokumen lebih panjang dipotong mengikut tetapan itu. Anggap tetingkap model dan pemotongan dokumen dalam permintaan pengeluar sebagai dua kawalan berbeza. Sediakan petikan ringkas yang relevan atau pilih bajet dokumen yang sesuai, tanpa menganggap setiap sumber panjang sentiasa dibaca sepenuhnya.\n\nUntuk prestasi permintaan yang disyorkan, Cohere menasihatkan agar tidak menghantar lebih daripada 1,000 dokumen sekali gus. Kekalkan pengenal calon apabila membahagikan senarai yang lebih besar kepada kumpulan yang mudah dikendalikan. Skor kerelevanan dinormalkan antara 0 dengan 1, tetapi bergantung pada pertanyaan dan calon; skor itu bukan ukuran linear ketepatan fakta. Gunakan susunan sebagai hasil utama dapatan maklumat. Jika menapis menggunakan ambang skor, tentukannya melalui pertanyaan yang mewakili penggunaan dan petikan yang hampir relevan dalam domain sebenar, tanpa menganggap ada ambang sejagat.\n\nArtifak akhir yang berguna ialah senarai calon berperingkat yang membawa pengenal sumber dan skor, sedia untuk hasil carian atau langkah pemilihan bukti. Dokumen dengan skor kerelevanan tinggi masih boleh mengandungi maklumat lapuk atau tidak betul, jadi kekalkan sumber dan kandungannya untuk peringkat yang menggunakannya. Tujuan Pro ialah penilaian kerelevanan, bukannya penulisan jawapan bebas atau penjanaan embedding. Kekalkan edisi Pro yang tepat bersama hasil apabila membandingkan atau memperhalus aliran kerja dapatan maklumat.", "alias_of": null, "aliases": [], "author": "Cohere", "available": null, "canonical_model": "cohere/rerank-4-pro", "capabilities": { "context_length": 32768, "manufacturer_input": [ "text" ], "manufacturer_output": [ "rerank" ] }, "card_routes": [ { "label": "Ultra", "rates": [ { "amount": "0.005", "amount_unit": "usd_per_declared_unit", "billing_input": true, "estimate_only": false, "key": "search_unit", "label": "search unit", "native_amount_usd_per_unit": "0.005", "native_quantity": 1, "public_unit_label": "per search unit", "quantity": "1", "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "total", "unit": "search_unit" } ], "summary": "Maklumat lengkap tentang harga konfigurasi terdapat dalam butiran." } ], "ctx": null, "entity_type": "model", "formats": [ "rerank" ], "id": "cohere/rerank-4-pro", "input": [ "text" ], "kind": "rerank", "max_out": null, "methods": [ { "available": null, "cache_read": null, "cache_write": null, "cache_write_1h": null, "contract": { "data": { "all_results": "Every actual result index, finite relevance_score and original document object is retained. Indices bind the original document array. Entire original JSON is stored and returned only after settlement.", "errors": [ { "code": null, "condition": "Medan wajib, bentuk perwakilan atau nilai tidak sah", "http_status": 400, "recovery": "Betulkan medan permintaan yang dinyatakan.", "type": "invalid_request_error" } ], "hints": [], "ignored_fields": [], "lifecycle": { "cancel": null, "idempotency_header": null, "job_retrieve": null, "mode": "synchronous", "streaming": false, "unknown": "Simpan operasi asal dan balasan penuh/separa; jangan ulang POST." }, "material_combinations": [ { "accepted": [ "string documents", "objects with text; image only for separately exact-supported multimodal model" ], "mode": "text_rerank", "required": [ "query", "documents array with minItems1" ] } ], "operation_modes": [ "rerank" ], "outputs": { "all_results": "Setiap indeks hasil sebenar, relevance_score terhingga dan objek dokumen asal dikekalkan. Indeks mengikat tatasusunan dokumen asal. Keseluruhan JSON asal disimpan dan hanya dikembalikan selepas penyelesaian caj.", "family": "rerank", "fields": [ { "description": null, "name": "model", "type": "string" }, { "description": null, "name": "id", "type": "string" }, { "description": null, "name": "provider", "type": "string" }, { "description": null, "name": "results[].index", "type": "integer" }, { "description": null, "name": "results[].relevance_score", "type": "number" }, { "description": null, "name": "results[].document.text", "type": "string" }, { "description": null, "name": "results[].document.image", "type": "string" }, { "description": null, "name": "usage.search_units", "type": "integer" }, { "description": null, "name": "usage.total_tokens", "type": "integer" }, { "description": null, "name": "usage.cost", "type": "number" } ], "format": "json", "schema": { "description": "Rerank response containing ranked results", "example": { "id": "gen-rerank-1234567890-abc", "model": "cohere/rerank-v3.5", "results": [ { "document": { "text": "Paris is the capital of France." }, "index": 0, "relevance_score": 0.98 } ], "usage": { "search_units": 1, "total_tokens": 150 } }, "properties": { "id": { "description": "Unique identifier for the rerank response (ORID format)", "example": "gen-rerank-1234567890-abc", "type": "string" }, "model": { "description": "The model used for reranking", "example": "cohere/rerank-v3.5", "type": "string" }, "provider": { "description": "The provider that served the rerank request", "example": "Cohere", "type": "string" }, "results": { "description": "List of rerank results sorted by relevance", "example": [ { "document": { "text": "Paris is the capital of France." }, "index": 0, "relevance_score": 0.98 } ], "items": { "description": "A single rerank result", "example": { "document": { "text": "Paris is the capital of France." }, "index": 0, "relevance_score": 0.98 }, "properties": { "document": { "description": "The document object echoing the original input (text and/or image)", "properties": { "image": { "description": "The image (URL or data URI) from the original document", "example": "https://example.com/image.png", "type": "string" }, "text": { "description": "The document text", "example": "Paris is the capital of France.", "type": "string" } }, "type": "object" }, "index": { "description": "Index of the document in the original input list", "example": 0, "type": "integer" }, "relevance_score": { "description": "Relevance score of the document to the query", "example": 0.98, "format": "double", "type": "number" } }, "required": [ "index", "relevance_score", "document" ], "type": "object" }, "type": "array" }, "usage": { "description": "Usage statistics", "example": { "search_units": 1, "total_tokens": 150 }, "properties": { "cost": { "description": "Cost of the request in credits", "example": 0.001, "format": "double", "type": "number" }, "search_units": { "description": "Number of search units consumed (Cohere billing)", "example": 1, "type": "integer" }, "total_tokens": { "description": "Total number of tokens used", "example": 150, "type": "integer" } }, "type": "object" } }, "required": [ "model", "results" ], "type": "object" } }, "parameters": [ { "availability": "source_interface", "default": null, "default_known": false, "description": "The list of documents to rerank. Documents may be plain strings, or structured objects with `text` and/or `image` for multimodal models.", "enum": null, "enum_known": false, "minItems": 1, "name": "documents", "required": true, "type": "array" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": [ "cohere/rerank-4-pro" ], "enum_known": true, "name": "model", "required": true, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "provider", "required": false, "type": "schema" }, { "availability": "source_interface", "default": null, "default_known": false, "description": "The search query to rerank documents against", "enum": null, "enum_known": false, "name": "query", "required": true, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "description": "A unique identifier for grouping related requests (e.g., a conversation or agent workflow). Used for observability grouping in Broadcast and private logging; never sent to the provider. If provided in both the request body and the x-session-id header, the body value takes precedence. Maximum of 256 characters.", "enum": null, "enum_known": false, "maxLength": 256, "name": "session_id", "required": false, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "description": "Number of most relevant documents to return", "enum": null, "enum_known": false, "minimum": 1, "name": "top_n", "required": false, "type": "integer" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "trace", "required": false, "type": "schema" }, { "availability": "source_interface", "default": null, "default_known": false, "description": "A unique identifier representing your end-user. Forwarded to Broadcast and private logging as the end-user id; never sent to the provider.", "enum": null, "enum_known": false, "maxLength": 256, "name": "user", "required": false, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "documents[].text", "required": false, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "description": "Official family image carrier is remote http/https URL or data:image URI; only for exact multimodal-model bindings.", "enum": null, "enum_known": false, "name": "documents[].image", "required": false, "type": "string" } ], "unsupported": [ "stream=true" ] }, "hints": [ "Hantar ID model yang tepat dan simpan respons natif yang lengkap." ], "input": { "content_type": "application/json", "material_types": [ "text" ], "multimodal_input_binding": null, "optional_controls": [ "provider", "session_id", "top_n", "trace", "user" ], "required": [ "model", "query", "documents" ] }, "lifecycle": { "cancel": null, "idempotency_header": null, "job_retrieve": null, "mode": "synchronous", "streaming": false, "unknown": "Preserve original operation and full/partial reply; do not replay POST." }, "manufacturer_capability": { "context_length": 32768, "manufacturer_input": [ "text" ], "manufacturer_output": [ "rerank" ] }, "method": { "http": "POST", "path": "/v1/rerank" }, "method_aliases": [], "operation_modes": [ "rerank" ], "output": { "all_results": "Every actual result index, finite relevance_score and original document object is retained. Indices bind the original document array. Entire original JSON is stored and returned only after settlement.", "family": "rerank", "fields": [ { "description": null, "name": "model", "type": "string" }, { "description": null, "name": "id", "type": "string" }, { "description": null, "name": "provider", "type": "string" }, { "description": null, "name": "results[].index", "type": "integer" }, { "description": null, "name": "results[].relevance_score", "type": "number" }, { "description": null, "name": "results[].document.text", "type": "string" }, { "description": null, "name": "results[].document.image", "type": "string" }, { "description": null, "name": "usage.search_units", "type": "integer" }, { "description": null, "name": "usage.total_tokens", "type": "integer" }, { "description": null, "name": "usage.cost", "type": "number" } ], "format": "json", "schema": { "description": "Rerank response containing ranked results", "example": { "id": "gen-rerank-1234567890-abc", "model": "cohere/rerank-v3.5", "results": [ { "document": { "text": "Paris is the capital of France." }, "index": 0, "relevance_score": 0.98 } ], "usage": { "search_units": 1, "total_tokens": 150 } }, "properties": { "id": { "description": "Unique identifier for the rerank response (ORID format)", "example": "gen-rerank-1234567890-abc", "type": "string" }, "model": { "description": "The model used for reranking", "example": "cohere/rerank-v3.5", "type": "string" }, "provider": { "description": "The provider that served the rerank request", "example": "Cohere", "type": "string" }, "results": { "description": "List of rerank results sorted by relevance", "example": [ { "document": { "text": "Paris is the capital of France." }, "index": 0, "relevance_score": 0.98 } ], "items": { "description": "A single rerank result", "example": { "document": { "text": "Paris is the capital of France." }, "index": 0, "relevance_score": 0.98 }, "properties": { "document": { "description": "The document object echoing the original input (text and/or image)", "properties": { "image": { "description": "The image (URL or data URI) from the original document", "example": "https://example.com/image.png", "type": "string" }, "text": { "description": "The document text", "example": "Paris is the capital of France.", "type": "string" } }, "type": "object" }, "index": { "description": "Index of the document in the original input list", "example": 0, "type": "integer" }, "relevance_score": { "description": "Relevance score of the document to the query", "example": 0.98, "format": "double", "type": "number" } }, "required": [ "index", "relevance_score", "document" ], "type": "object" }, "type": "array" }, "usage": { "description": "Usage statistics", "example": { "search_units": 1, "total_tokens": 150 }, "properties": { "cost": { "description": "Cost of the request in credits", "example": 0.001, "format": "double", "type": "number" }, "search_units": { "description": "Number of search units consumed (Cohere billing)", "example": 1, "type": "integer" }, "total_tokens": { "description": "Total number of tokens used", "example": 150, "type": "integer" } }, "type": "object" } }, "required": [ "model", "results" ], "type": "object" } }, "schema": { "request": { "description": "Rerank request input", "example": { "documents": [ "Paris is the capital of France.", "Berlin is the capital of Germany." ], "model": "cohere/rerank-v3.5", "query": "What is the capital of France?", "top_n": 3 }, "properties": { "documents": { "description": "The list of documents to rerank. Documents may be plain strings, or structured objects with `text` and/or `image` for multimodal models.", "example": [ "Paris is the capital of France.", "Berlin is the capital of Germany." ], "items": { "anyOf": [ { "type": "string" }, { "description": "A structured document with optional text and/or image content. At least one of `text` or `image` must be provided.", "properties": { "image": { "description": "An image associated with the document, as a remote URL (http/https) or a base64-encoded data URI (data:image/...).", "example": "https://upload.wikimedia.org/wikipedia/commons/thumb/8/8b/Phytogenic.png", "type": "string" }, "text": { "description": "The document text", "example": "AI enables robots to perceive, plan, and act autonomously.", "type": "string" } }, "type": "object" } ], "description": "A document to rerank. Either a plain string, or a structured object with optional `text` and/or `image`." }, "minItems": 1, "type": "array" }, "model": { "enum": [ "cohere/rerank-4-pro" ], "type": "string" }, "provider": { "allOf": [ { "$ref": "#/components/schemas/ProviderPreferences" }, { "description": "Provider routing preferences for the request." } ] }, "query": { "description": "The search query to rerank documents against", "example": "What is the capital of France?", "type": "string" }, "session_id": { "description": "A unique identifier for grouping related requests (e.g., a conversation or agent workflow). Used for observability grouping in Broadcast and private logging; never sent to the provider. If provided in both the request body and the x-session-id header, the body value takes precedence. Maximum of 256 characters.", "example": "session-1234", "maxLength": 256, "type": "string" }, "top_n": { "description": "Number of most relevant documents to return", "example": 3, "minimum": 1, "type": "integer" }, "trace": { "$ref": "#/components/schemas/TraceConfig" }, "user": { "description": "A unique identifier representing your end-user. Forwarded to Broadcast and private logging as the end-user id; never sent to the provider.", "example": "user-1234", "maxLength": 256, "type": "string" } }, "required": [ "model", "query", "documents" ], "type": "object" }, "response": { "description": "Rerank response containing ranked results", "example": { "id": "gen-rerank-1234567890-abc", "model": "cohere/rerank-v3.5", "results": [ { "document": { "text": "Paris is the capital of France." }, "index": 0, "relevance_score": 0.98 } ], "usage": { "search_units": 1, "total_tokens": 150 } }, "properties": { "id": { "description": "Unique identifier for the rerank response (ORID format)", "example": "gen-rerank-1234567890-abc", "type": "string" }, "model": { "description": "The model used for reranking", "example": "cohere/rerank-v3.5", "type": "string" }, "provider": { "description": "The provider that served the rerank request", "example": "Cohere", "type": "string" }, "results": { "description": "List of rerank results sorted by relevance", "example": [ { "document": { "text": "Paris is the capital of France." }, "index": 0, "relevance_score": 0.98 } ], "items": { "description": "A single rerank result", "example": { "document": { "text": "Paris is the capital of France." }, "index": 0, "relevance_score": 0.98 }, "properties": { "document": { "description": "The document object echoing the original input (text and/or image)", "properties": { "image": { "description": "The image (URL or data URI) from the original document", "example": "https://example.com/image.png", "type": "string" }, "text": { "description": "The document text", "example": "Paris is the capital of France.", "type": "string" } }, "type": "object" }, "index": { "description": "Index of the document in the original input list", "example": 0, "type": "integer" }, "relevance_score": { "description": "Relevance score of the document to the query", "example": 0.98, "format": "double", "type": "number" } }, "required": [ "index", "relevance_score", "document" ], "type": "object" }, "type": "array" }, "usage": { "description": "Usage statistics", "example": { "search_units": 1, "total_tokens": 150 }, "properties": { "cost": { "description": "Cost of the request in credits", "example": 0.001, "format": "double", "type": "number" }, "search_units": { "description": "Number of search units consumed (Cohere billing)", "example": 1, "type": "integer" }, "total_tokens": { "description": "Total number of tokens used", "example": 150, "type": "integer" } }, "type": "object" } }, "required": [ "model", "results" ], "type": "object" } } }, "ctx": null, "declared_pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Base token rates", "units": [ { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": null, "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": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": null, "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": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": null, "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": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": null, "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" } ] }, { "dimensions": { "native_endpoint": "rerank", "quoted_unit": "per search unit" }, "label": "Ultra", "units": [ { "amount": "0.005", "amount_unit": "usd_per_declared_unit", "billing_input": true, "estimate_only": false, "key": "search_unit", "label": "search unit", "native_amount_usd_per_unit": "0.005", "native_quantity": 1, "public_unit_label": "per search unit", "quantity": "1", "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "total", "unit": "search_unit" } ] } ], "examples": [ { "code": "{\n \"documents\": [\n \"Paris is the capital of France.\",\n \"Berlin is the capital of Germany.\"\n ],\n \"model\": \"cohere/rerank-4-pro\",\n \"query\": \"What is the capital of France?\",\n \"top_n\": 3\n}", "label": "Retained official request structure; documentary example", "language": "json" }, { "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\n: \"${AILOOT_REQUEST_FILE:?Set your actual JSON request file}\"\nexport AILOOT_REQUEST_FILE\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads(Path(os.environ['AILOOT_REQUEST_FILE']).read_text(encoding='utf-8'))\nif not isinstance(p,dict): raise SystemExit('Request must be a JSON object')\np['model']='cohere/rerank-4-pro'\nPath('request.json').write_text(json.dumps(p,ensure_ascii=False),encoding='utf-8')\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/rerank \\\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\nprint(json.dumps(json.loads(Path('result.json').read_text()),ensure_ascii=False,indent=2))\nPY_RESULT\n", "label": "Use your actual request material and retain the complete result", "language": "bash", "material_policy": "Use real owner-supplied state/questions or query/documents. No invented file ID, document, image URL, job ID or returned result." } ], "family": "rerank", "fast_multiplier": null, "flex_multiplier": null, "fmt": "rerank", "http": "POST", "in": null, "input": [ "text" ], "label": "Pemeringkatan semula", "max_out": null, "max_reasoning_effort_multiplier": null, "method_aliases": [], "native_endpoint": "rerank", "out": null, "output": [ "rerank" ], "path": "/v1/rerank", "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": "per_request", "image_input_price": null, "image_output_price": null, "per_request_price": null, "unit_prices": { "search_unit": 0.005 } }, "pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Base token rates", "units": [ { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": null, "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": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": null, "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": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": null, "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": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": null, "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" } ] }, { "dimensions": { "native_endpoint": "rerank", "quoted_unit": "per search unit" }, "label": "Ultra", "units": [ { "amount": "0.005", "amount_unit": "usd_per_declared_unit", "billing_input": true, "estimate_only": false, "key": "search_unit", "label": "search unit", "native_amount_usd_per_unit": "0.005", "native_quantity": 1, "public_unit_label": "per search unit", "quantity": "1", "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "total", "unit": "search_unit" } ] } ], "request_model_id": "cohere/rerank-4-pro", "route_label": "Ultra", "status_reads": [], "tiers": [], "units": [ { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": null, "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": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": null, "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": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": null, "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": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": null, "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" }, { "amount": "0.005", "amount_unit": "usd_per_declared_unit", "billing_input": true, "estimate_only": false, "key": "search_unit", "label": "search unit", "native_amount_usd_per_unit": "0.005", "native_quantity": 1, "public_unit_label": "per search unit", "quantity": "1", "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "total", "unit": "search_unit" } ] } ], "model_aliases": [ { "canonical_model_id": "cohere/rerank-4-pro", "request_model_id": "cohere/rerank-4-pro", "route_label": "Ultra" } ], "name": "Cohere: Rerank 4 Pro", "price": { "in": null, "in_label": null, "out": null, "out_label": null, "sort_in": null, "sort_out": null, "varies": true }, "public_id": "cohere/rerank-4-pro", "short": "Cohere Rerank 4 Pro menyusun dokumen calon mengikut kerelevanan kepada pertanyaan untuk carian dan aliran kerja yang diperkaya dengan dapatan carian. Model ini ialah edisi Rerank 4 yang mengutamakan kualiti, dengan konteks 32K, liputan berbilang bahasa dan sokongan teks separa berstruktur." }, "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 } ], "role": "current-semantic-reference" }, "raw_input": { "examples_role": "historical-input-not-current-ready-examples", "json_pointers": [ "/rawModel", "/methods/0/rawMethod" ], "role": "historical-owner-input" }, "schema": "ailoot-native-model-representations-v1" }, "schema": "ailoot-static-model-facts-v1", "schema_version": 1, "shortText": "Cohere Rerank 4 Pro menyusun dokumen calon mengikut kerelevanan kepada pertanyaan untuk carian dan aliran kerja yang diperkaya dengan dapatan carian. Model ini ialah edisi Rerank 4 yang mengutamakan kualiti, dengan konteks 32K, liputan berbilang bahasa dan sokongan teks separa berstruktur.", "source": { "audit_sha256": "2e00086bcbfc28e64d0cebe97aa47c17d8d5618963027f8ffb9dc6123d7d25b1", "bridge_map_sha256": "1125275768b9636fc8461bc7da5061239a81ddfe508e2557843ca42c58ab0036", "raw_model_json_sha256": "60b63a56d742db63d0a42d94171187724ed88447b80fe555d27391bdc4ce70c0", "renderer_sha256": "1dab5ba62521e9568c8b7c82005622cabbf330a075df7cce1f83274a04009460" }, "sourceRevision": "64972e82e2adf8aa6b0b063a6dd5804ac00c7faf8d1eedff155b7d36dfad096c" }