Qwen3-235B-A22B-Instruct-2507 is the updated instruction-following, non-thinking edition of Qwen3’s large mixture-of-experts model. Qwen highlights improvements in text comprehension, logical reasoning, mathematics, science, coding and tool use, together with broader multilingual knowledge and better handling of open-ended writing. Its inputs and outputs are text. It is useful for turning a well-defined brief and supplied material into an answer, an analysis, a program or a polished draft without a separately generated thinking section.
The model has 235 billion total parameters and activates 22 billion for each token. Its native context is 262,144 tokens. A practical long-input request can bring together a product specification, implementation notes and a list of unresolved questions, then ask for a structured comparison or an edited specification. Preserve the source headings and identify the intended audience, the sections to change and the conditions the output must satisfy. For a writing task, include the tone, length and required facts; for a coding task, include the interfaces, expected behaviour and surrounding code.
This edition supports non-thinking mode only and does not produce <think> blocks. It does not need the original hybrid model’s enable_thinking=False switch, and asking for the later Thinking-2507 model’s mode does not change its edition. Direct output still allows an explanation, calculation or reasoned answer when requested. Qwen’s examples standardise a mathematical answer with \\boxed{} and a multiple-choice answer with a specified answer field. Similar explicit instructions can request a short rationale followed by a final recommendation, or defined JSON fields whose meanings are given in the prompt.
For agent workflows, the official card recommends Qwen-Agent, with its tool-call templates and parsers. Tools can be defined individually or through a structured tool configuration, and the producer demonstrates retrieval and a code interpreter. The useful preparation is to describe each tool’s purpose and arguments, supply the material already available and specify the desired stopping point. For example, a documentation assistant can be asked to retrieve named sections, reconcile differences and return an updated explanation. The surrounding application executes requested tools and returns their results to the conversation.
The producer recommends temperature 0.7, top-p 0.8, top-k 20 and min-p 0, with an output allowance of 16,384 tokens for most queries. This is generation guidance rather than a statement that every response can reach a fixed maximum. The card also notes that a presence penalty can reduce repetitive output, while higher values can introduce language mixing and slightly reduce quality. A focused brief and an explicit output structure are useful before changing such controls: ask for the exact sections, fields or examples needed, and reserve sufficient space for them.
Qwen documents an optional extension to 1,010,000 tokens through a separate configuration using Dual Chunk Attention and sparse attention. The native window remains 262,144 tokens; the larger setting depends on the prescribed implementation and memory resources. The card explicitly notes that memory constraints may require a shorter configured context. For extensive material, select the relevant sources, state their priority and ask the model to separate conclusions supported by the supplied text from questions left unresolved. This makes a long-context result actionable without treating input capacity as a substitute for a precise task.
", "aboutText": "Qwen3-235B-A22B-Instruct-2507 is the updated instruction-following, non-thinking edition of Qwen3’s large mixture-of-experts model. Qwen highlights improvements in text comprehension, logical reasoning, mathematics, science, coding and tool use, together with broader multilingual knowledge and better handling of open-ended writing. Its inputs and outputs are text. It is useful for turning a well-defined brief and supplied material into an answer, an analysis, a program or a polished draft without a separately generated thinking section.\n\nThe model has 235 billion total parameters and activates 22 billion for each token. Its native context is 262,144 tokens. A practical long-input request can bring together a product specification, implementation notes and a list of unresolved questions, then ask for a structured comparison or an edited specification. Preserve the source headings and identify the intended audience, the sections to change and the conditions the output must satisfy. For a writing task, include the tone, length and required facts; for a coding task, include the interfaces, expected behaviour and surrounding code.\n\nThis edition supports non-thinking mode only and does not produceTechnical example in English
Update a specification from notes\n\nProduce an updated export specification from these sources. [SPEC] Export all visible rows with headers. [IMPLEMENTATION] Current code exports selected rows only. [DECISION] Users must choose either selected rows or all filtered rows; headers are always included. Decision takes priority. Return the revised requirement, the implementation gap and questions still needed for an empty selection.A revised specification section, a source-labeled gap note and an empty-selection question.Check the two export choices remain distinct and headers stay mandatory.qwen/qwen3-235b-a22b-2507Qwen3-235B-A22B-Instruct-2507 is Qwen’s non-thinking text model for multilingual writing, coding, instruction following and document analysis. It generates direct answers without thinking blocks and has a native 262,144-token context.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Qwen3-235B-A22B-Instruct-2507 is the updated instruction-following, non-thinking edition of Qwen3’s large mixture-of-experts model. Qwen highlights improvements in text comprehension, logical reasoning, mathematics, science, coding and tool use, together with broader multilingual knowledge and better handling of open-ended writing. Its inputs and outputs are text. It is useful for turning a well-defined brief and supplied material into an answer, an analysis, a program or a polished draft without a separately generated thinking section.
The model has 235 billion total parameters and activates 22 billion for each token. Its native context is 262,144 tokens. A practical long-input request can bring together a product specification, implementation notes and a list of unresolved questions, then ask for a structured comparison or an edited specification. Preserve the source headings and identify the intended audience, the sections to change and the conditions the output must satisfy. For a writing task, include the tone, length and required facts; for a coding task, include the interfaces, expected behaviour and surrounding code.
This edition supports non-thinking mode only and does not produce <think> blocks. It does not need the original hybrid model’s enable_thinking=False switch, and asking for the later Thinking-2507 model’s mode does not change its edition. Direct output still allows an explanation, calculation or reasoned answer when requested. Qwen’s examples standardise a mathematical answer with \\boxed{} and a multiple-choice answer with a specified answer field. Similar explicit instructions can request a short rationale followed by a final recommendation, or defined JSON fields whose meanings are given in the prompt.
For agent workflows, the official card recommends Qwen-Agent, with its tool-call templates and parsers. Tools can be defined individually or through a structured tool configuration, and the producer demonstrates retrieval and a code interpreter. The useful preparation is to describe each tool’s purpose and arguments, supply the material already available and specify the desired stopping point. For example, a documentation assistant can be asked to retrieve named sections, reconcile differences and return an updated explanation. The surrounding application executes requested tools and returns their results to the conversation.
The producer recommends temperature 0.7, top-p 0.8, top-k 20 and min-p 0, with an output allowance of 16,384 tokens for most queries. This is generation guidance rather than a statement that every response can reach a fixed maximum. The card also notes that a presence penalty can reduce repetitive output, while higher values can introduce language mixing and slightly reduce quality. A focused brief and an explicit output structure are useful before changing such controls: ask for the exact sections, fields or examples needed, and reserve sufficient space for them.
Qwen documents an optional extension to 1,010,000 tokens through a separate configuration using Dual Chunk Attention and sparse attention. The native window remains 262,144 tokens; the larger setting depends on the prescribed implementation and memory resources. The card explicitly notes that memory constraints may require a shorter configured context. For extensive material, select the relevant sources, state their priority and ask the model to separate conclusions supported by the supplied text from questions left unresolved. This makes a long-context result actionable without treating input capacity as a substitute for a precise task.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits qwen/qwen3-235b-a22b-2507 | Default— |
Fieldmessages | Typemessage[] | RequirementRequired | Values & limits Minimum items: 1 | Default— |
Fieldmessages[].role | Typestring | RequirementRequired | Values & limits Examples: system, user, assistant, tool, function Conditions: These roles are explicitly represented by Source compatibility types; the exact model role set remains model-specific. Model-specific | Default— |
Fieldmessages[].content | Typestring|content_part[]|null | RequirementOptional | Values & limits | Default— |
Fieldmessages[].tool_calls | Typetool_call[] | RequirementOptional | Values & limits | Default— |
Fieldmessages[].tool_call_id | Typestring | RequirementOptional | Values & limits Applies when: role=tool; use actual returned ID | Default— |
Fieldmessages[].name | Typestring | RequirementOptional | Values & limits | Default— |
Fieldmessages[].reasoning_content | Typestring | RequirementOptional | Values & limits Applies when: Round-trip actual returned reasoning where this model uses it | Default— |
Fieldmessages[].reasoning_details | Typeobject|array | RequirementOptional | Values & limits Model-specific | Default— |
Fieldmax_tokens | Typeinteger | RequirementOptional | Values & limits Minimum: 1 Model-specific | Default— |
Fieldmax_completion_tokens | Typeinteger | RequirementOptional | Values & limits Minimum: 1 Model-specific | Default— |
Fieldstream | Typeboolean | RequirementOptional | Values & limits | Defaultfalse |
Fieldstream_options.include_usage | Typeboolean | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtemperature | Typenumber | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtop_p | Typenumber | RequirementOptional | Values & limits Model-specific | Default— |
Fieldstop | Typestring|string[] | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtools | Typefunction_tool[] | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtools[].type | Typestring | RequirementOptional | Values & limits function Model-specific | Default— |
Fieldtools[].function.name | Typestring | RequirementRequired | Values & limits Applies when: function tool supplied | Default— |
Fieldtools[].function.description | Typestring | RequirementOptional | Values & limits | Default— |
Fieldtools[].function.parameters | TypeJSON_schema | RequirementOptional | Values & limits | Default— |
Fieldtool_choice | Typestring|object | RequirementOptional | Values & limits Model-specific | Default— |
Fieldparallel_tool_calls | Typeboolean | RequirementOptional | Values & limits Model-specific | Default— |
Fieldresponse_format | Typeobject | RequirementOptional | Values & limits Model-specific | Default— |
Fieldreasoning_effort | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldreasoning | Typeobject | RequirementOptional | Values & limits Model-specific | Default— |
Fieldservice_tier | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldinstructions | Typestring | RequirementOptional | Values & limits Compatibility field | Default— |
Fieldfunctions | Typefunction[] | RequirementOptional | Values & limits Model-specific | Default— |
Fieldfunction_call | Typestring|object | RequirementOptional | Values & limits Model-specific | Default— |
Fieldprompt_cache_options | Typeobject | RequirementOptional | Values & limits Model-specific | Default— |
Fieldmodalities | Typestring[] | RequirementOptional | Values & limits text | Default— |
Fieldprovider | Typeobject | RequirementOptional | Values & limits Conditions: provider.options is rejected on this Ultra text route | Default— |
Fieldcache_control.ttl | Typestring | RequirementOptional | Values & limits Conditions: 1h is rejected in actual prompt-cache locations | Default— |
Text · UTF8 text
messages[].content
| Parameter | Contract |
|---|---|
Parametercontent_type | Contractapplication/json |
Parameterrequired | Contractmodel, messages |
Parametermaterial_types | Contracttext |
Parametermessages | Contractmessage array with role/content shape |
Parameterfile_material | Contractactual UTF-8 document inserted as literal text; no file ID inferred |
Parameteroptional_controls | Contractstream, temperature, top_p, stop, max_tokens, max_completion_tokens, reasoning, response_format, tools, tool_choice |
Parametercontrol_scope | Contractmodel-specific; user function tools differ from hosted tools |
Parameteroutput_modalities | Contracttext |
Parameterhosted_operations | Contractfalse |
Parametermultimodal_input_binding | Contractnull |
Lifecycle: synchronous_or_streamed
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.
Tariff references do not add request options. Supported parameters are listed for the selected method.
Match each charge to the conditions shown. Use one rate for each set of alternative configurations.
| Configuration | Rate schedule | Context tokens, more than | Context tokens, up to and including | Charge |
|---|---|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Context tokens, more than— | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.000572 / 1K tokens Output · Output tokens $0.00228 / 1K tokens Input · Cache read $0.0001 / 1K tokens Input · Cache write $0 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
| ConfigurationRequest-length tier 1 | Rate schedule— | Context tokens, more than0 | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.000572 / 1K tokens Output · Output tokens $0.00228 / 1K tokens Input · Cache read $0.0001 / 1K tokens Input · Cache write $0 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "qwen/qwen3-235b-a22b-2507", "messages": [{"role": "user", "content": "Explain the difference between an observation and a hypothesis in three sentences."}]}')\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\nfrom 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='qwen/qwen3-235b-a22b-2507',messages=[{"role":"user","content":"Explain observations and hypotheses in three sentences."}])\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"))\nfrom openai import OpenAI\nimport os\nfrom pathlib import Path\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)\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\nwith client.chat.completions.stream(model='qwen/qwen3-235b-a22b-2507',messages=[{"role":"user","content":"Explain observations and hypotheses in three sentences."}]) as stream:\n with Path("stream-events.jsonl").open("w",encoding="utf-8") as log:\n for event in stream:\n payload=event.to_dict(mode="json")\n log.write(json.dumps(payload,ensure_ascii=False)+"\\n"); log.flush()\n print(json.dumps(payload,ensure_ascii=False))\n final=stream.get_final_completion()\n save_result(final.to_dict(mode="json"),"stream-result")\njson or SSE
Keep every complete returned response, file and content part.
Keep every choice and its content/tool calls/reasoning. In SSE assemble deltas by choice/tool index, retain the final finish reason and usage.
Keep request/response IDs and headers. No generic text job retrieval URL is supplied.
| Field | Type |
|---|---|
Fieldchoices[].index | Typeinteger |
Fieldchoices[].message.content | Typestring|null |
Fieldchoices[].message.tool_calls | Typetool_call[] |
Fieldchoices[].message.reasoning_content | Typestring |
Fieldchoices[].message.reasoning_details | Typeobject|array |
Fieldchoices[].finish_reason | Typestring|null |
Fieldusage | Typeobject |
| Response | Condition | Next step |
|---|---|---|
Response400 · invalid_request_error | ConditionInvalid required fields, material representation or parameter value | Next stepCorrect the indicated fields before a new creation request. |
Ailoot Ultra qwen/qwen3-235b-a22b-2507
Use the method paths shown above.
qwen/qwen3-235b-a22b-2507Qwen3-235B-A22B-Instruct-2507 is Qwen’s non-thinking text model for multilingual writing, coding, instruction following and document analysis. It generates direct answers without thinking blocks and has a native 262,144-token context.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Qwen3-235B-A22B-Instruct-2507 is the updated instruction-following, non-thinking edition of Qwen3’s large mixture-of-experts model. Qwen highlights improvements in text comprehension, logical reasoning, mathematics, science, coding and tool use, together with broader multilingual knowledge and better handling of open-ended writing. Its inputs and outputs are text. It is useful for turning a well-defined brief and supplied material into an answer, an analysis, a program or a polished draft without a separately generated thinking section.
The model has 235 billion total parameters and activates 22 billion for each token. Its native context is 262,144 tokens. A practical long-input request can bring together a product specification, implementation notes and a list of unresolved questions, then ask for a structured comparison or an edited specification. Preserve the source headings and identify the intended audience, the sections to change and the conditions the output must satisfy. For a writing task, include the tone, length and required facts; for a coding task, include the interfaces, expected behaviour and surrounding code.
This edition supports non-thinking mode only and does not produce <think> blocks. It does not need the original hybrid model’s enable_thinking=False switch, and asking for the later Thinking-2507 model’s mode does not change its edition. Direct output still allows an explanation, calculation or reasoned answer when requested. Qwen’s examples standardise a mathematical answer with \\boxed{} and a multiple-choice answer with a specified answer field. Similar explicit instructions can request a short rationale followed by a final recommendation, or defined JSON fields whose meanings are given in the prompt.
For agent workflows, the official card recommends Qwen-Agent, with its tool-call templates and parsers. Tools can be defined individually or through a structured tool configuration, and the producer demonstrates retrieval and a code interpreter. The useful preparation is to describe each tool’s purpose and arguments, supply the material already available and specify the desired stopping point. For example, a documentation assistant can be asked to retrieve named sections, reconcile differences and return an updated explanation. The surrounding application executes requested tools and returns their results to the conversation.
The producer recommends temperature 0.7, top-p 0.8, top-k 20 and min-p 0, with an output allowance of 16,384 tokens for most queries. This is generation guidance rather than a statement that every response can reach a fixed maximum. The card also notes that a presence penalty can reduce repetitive output, while higher values can introduce language mixing and slightly reduce quality. A focused brief and an explicit output structure are useful before changing such controls: ask for the exact sections, fields or examples needed, and reserve sufficient space for them.
Qwen documents an optional extension to 1,010,000 tokens through a separate configuration using Dual Chunk Attention and sparse attention. The native window remains 262,144 tokens; the larger setting depends on the prescribed implementation and memory resources. The card explicitly notes that memory constraints may require a shorter configured context. For extensive material, select the relevant sources, state their priority and ask the model to separate conclusions supported by the supplied text from questions left unresolved. This makes a long-context result actionable without treating input capacity as a substitute for a precise task.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits qwen/qwen3-235b-a22b-2507 | Default— |
Fieldmessages | Typemessage[] | RequirementRequired | Values & limits | Default— |
Fieldmessages[].role | Typestring | RequirementRequired | Values & limits user, assistant | Default— |
Fieldmessages[].content | Typestring | RequirementRequired | Values & limits | Default— |
Fieldmax_tokens | Typeinteger | RequirementRequired | Values & limits | Default— |
Fieldstream | Typeboolean | RequirementOptional | Values & limits | Defaultfalse |
Text · UTF-8 text
messages[].content string
| Parameter | Contract |
|---|---|
Parametercontent_type | Contractapplication/json |
Parameterrequired | Contractmodel, messages, max_tokens |
Parameteroptional_controls | Contractstream |
Parametermaterial_types | Contracttext |
Parametertext_file_material | ContractRead the actual UTF-8 document and put its text in the indicated input or message content string. |
Lifecycle: synchronous_or_streamed
Tariff references do not add request options. Supported parameters are listed for the selected method.
Match each charge to the conditions shown. Use one rate for each set of alternative configurations.
| Configuration | Rate schedule | Context tokens, more than | Context tokens, up to and including | Charge |
|---|---|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Context tokens, more than— | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.000572 / 1K tokens Output · Output tokens $0.00228 / 1K tokens Input · Cache read $0.0001 / 1K tokens Input · Cache write $0 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
| ConfigurationRequest-length tier 1 | Rate schedule— | Context tokens, more than0 | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.000572 / 1K tokens Output · Output tokens $0.00228 / 1K tokens Input · Cache read $0.0001 / 1K tokens Input · Cache write $0 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "qwen/qwen3-235b-a22b-2507",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${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': 'qwen/qwen3-235b-a22b-2507', '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\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': 'qwen/qwen3-235b-a22b-2507', '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))\nJSON or SSE
Keep every complete returned response, file and content part.
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.
Keep request/response IDs and headers. No generic text job retrieval URL is supplied.
| Field | Type |
|---|---|
Fieldid | Typestring |
Fieldmodel | Typestring |
Fieldcontent[] | Typetext|thinking|tool_use|other content block |
Fieldstop_reason | Typestring|null |
Fieldusage | Typeobject |
| Response | Condition | Next step |
|---|---|---|
Response400 · invalid_request_error | ConditionInvalid required fields, material representation or parameter value | Next stepCorrect the indicated fields before a new creation request. |
Ailoot Ultra qwen/qwen3-235b-a22b-2507
Use the method paths shown above.
Technical example in English
Update a specification from notes\n\nProduce an updated export specification from these sources. [SPEC] Export all visible rows with headers. [IMPLEMENTATION] Current code exports selected rows only. [DECISION] Users must choose either selected rows or all filtered rows; headers are always included. Decision takes priority. Return the revised requirement, the implementation gap and questions still needed for an empty selection.A revised specification section, a source-labeled gap note and an empty-selection question.Check the two export choices remain distinct and headers stay mandatory.