Qwen2.5-7B-Instruct is the instruction-tuned 7B text model in Qwen2.5’s general-purpose language-model family. Qwen highlights coding and mathematics, instruction following, long-form writing, table understanding and structured output, especially JSON. It is a dense, decoder-only model with 7.61 billion total parameters, including 6.53 billion non-embedding parameters. It accepts text conversations and generates text. A useful brief can ask it to turn supplied information into a clear explanation, a structured record, a revised draft or a proposed program.
The model supports more than 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai and Arabic. The producer also emphasises resilience to varied system prompts, useful for defining a conversational role and its conditions. Give the role, audience, terminology and output language directly. For example, an editing request can provide a technical passage, specify the intended reader and ask for a shorter explanation that preserves named terms and stated conditions. The instruction-tuned edition is the conversational model, distinct from the corresponding pretrained base model.
Structured-data comprehension and JSON generation are central improvements in Qwen2.5. To extract or transform a table, preserve its headers and units and explain which rows or relationships matter. Define the requested keys, value types and treatment of missing information rather than asking only for a generic summary. An illustrative brief could supply a list of issues and request one object per issue containing the source identifier, category and a short explanation. Such a result remains generated text: checking its structure and values against the input is part of using it in a downstream workflow.
The exact card describes a full context length of 131,072 tokens and generation of up to 8,192 tokens. Its supplied configuration is set to 32,768 context tokens; handling longer material uses the documented YaRN configuration. The producer cautions that static scaling can affect performance on shorter inputs and recommends adding it when long-context processing is needed. Keep the input and requested answer within the relevant configured space. For a long report, retain section titles, prioritise the passages tied to the question and request the required output length, rather than treating the larger context figure as a reason to include unrelated material.
The official quickstart uses system and user messages with the model’s chat template, then decodes the generated continuation. Prepare coding work with the relevant source, language, expected behaviour and constraints. Prepare a mathematical question with the givens and the format of the result. The general model’s documented improvements in these areas do not make it the separately trained Qwen2.5-Coder or Qwen2.5-Math edition. An illustrative coding brief might ask for an explanation of a function, a proposed correction and why that proposal meets the stated conditions; execution and verification remain separate steps.
Qwen’s release documentation also describes tool calling and a tool-aware chat template, including compatibility with Qwen-Agent. Define available tools and their arguments, state the goal and provide returned results for the model to interpret. A document assistant can request relevant information before composing a final answer, while a code assistant can reason about the output of an application-executed check. The useful deliverable is then a response tied to the supplied evidence and requested format. This is a text-model profile; visual, audio or video-generation capabilities of other Qwen models are separate.
", "aboutText": "Qwen2.5-7B-Instruct is the instruction-tuned 7B text model in Qwen2.5’s general-purpose language-model family. Qwen highlights coding and mathematics, instruction following, long-form writing, table understanding and structured output, especially JSON. It is a dense, decoder-only model with 7.61 billion total parameters, including 6.53 billion non-embedding parameters. It accepts text conversations and generates text. A useful brief can ask it to turn supplied information into a clear explanation, a structured record, a revised draft or a proposed program.\n\nThe model supports more than 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai and Arabic. The producer also emphasises resilience to varied system prompts, useful for defining a conversational role and its conditions. Give the role, audience, terminology and output language directly. For example, an editing request can provide a technical passage, specify the intended reader and ask for a shorter explanation that preserves named terms and stated conditions. The instruction-tuned edition is the conversational model, distinct from the corresponding pretrained base model.\n\nStructured-data comprehension and JSON generation are central improvements in Qwen2.5. To extract or transform a table, preserve its headers and units and explain which rows or relationships matter. Define the requested keys, value types and treatment of missing information rather than asking only for a generic summary. An illustrative brief could supply a list of issues and request one object per issue containing the source identifier, category and a short explanation. Such a result remains generated text: checking its structure and values against the input is part of using it in a downstream workflow.\n\nThe exact card describes a full context length of 131,072 tokens and generation of up to 8,192 tokens. Its supplied configuration is set to 32,768 context tokens; handling longer material uses the documented YaRN configuration. The producer cautions that static scaling can affect performance on shorter inputs and recommends adding it when long-context processing is needed. Keep the input and requested answer within the relevant configured space. For a long report, retain section titles, prioritise the passages tied to the question and request the required output length, rather than treating the larger context figure as a reason to include unrelated material.\n\nThe official quickstart uses system and user messages with the model’s chat template, then decodes the generated continuation. Prepare coding work with the relevant source, language, expected behaviour and constraints. Prepare a mathematical question with the givens and the format of the result. The general model’s documented improvements in these areas do not make it the separately trained Qwen2.5-Coder or Qwen2.5-Math edition. An illustrative coding brief might ask for an explanation of a function, a proposed correction and why that proposal meets the stated conditions; execution and verification remain separate steps.\n\nQwen’s release documentation also describes tool calling and a tool-aware chat template, including compatibility with Qwen-Agent. Define available tools and their arguments, state the goal and provide returned results for the model to interpret. A document assistant can request relevant information before composing a final answer, while a code assistant can reason about the output of an application-executed check. The useful deliverable is then a response tied to the supplied evidence and requested format. This is a text-model profile; visual, audio or video-generation capabilities of other Qwen models are separate.", "editorialOverlay": { "copy_fields": { "practical_html": "Technical example in English
Transform a table with units preserved\n\nFrom this text table, report total units and rows needing follow-up: item | ordered units | received units; pens | 40 | 35; folders | 20 | 20; clips | 100 | unknown. Produce a table with item, missing units and source values, then totals that separate known quantities from unknowns. Do not treat unknown as zero.A transformed table, known ordered/received totals and a note explaining which shortage remains undetermined.Pens show a shortage of 5, folders 0, and clips unknown; totals do not silently count unknown received units as zero.qwen/qwen-2.5-7b-instructQwen2.5-7B-Instruct is Qwen’s compact instruction-tuned text model for multilingual writing, coding, mathematics and structured data. Its producer documents up to 131,072 context tokens with the long-context configuration and generation of up to 8,192 tokens.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Qwen2.5-7B-Instruct is the instruction-tuned 7B text model in Qwen2.5’s general-purpose language-model family. Qwen highlights coding and mathematics, instruction following, long-form writing, table understanding and structured output, especially JSON. It is a dense, decoder-only model with 7.61 billion total parameters, including 6.53 billion non-embedding parameters. It accepts text conversations and generates text. A useful brief can ask it to turn supplied information into a clear explanation, a structured record, a revised draft or a proposed program.
The model supports more than 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai and Arabic. The producer also emphasises resilience to varied system prompts, useful for defining a conversational role and its conditions. Give the role, audience, terminology and output language directly. For example, an editing request can provide a technical passage, specify the intended reader and ask for a shorter explanation that preserves named terms and stated conditions. The instruction-tuned edition is the conversational model, distinct from the corresponding pretrained base model.
Structured-data comprehension and JSON generation are central improvements in Qwen2.5. To extract or transform a table, preserve its headers and units and explain which rows or relationships matter. Define the requested keys, value types and treatment of missing information rather than asking only for a generic summary. An illustrative brief could supply a list of issues and request one object per issue containing the source identifier, category and a short explanation. Such a result remains generated text: checking its structure and values against the input is part of using it in a downstream workflow.
The exact card describes a full context length of 131,072 tokens and generation of up to 8,192 tokens. Its supplied configuration is set to 32,768 context tokens; handling longer material uses the documented YaRN configuration. The producer cautions that static scaling can affect performance on shorter inputs and recommends adding it when long-context processing is needed. Keep the input and requested answer within the relevant configured space. For a long report, retain section titles, prioritise the passages tied to the question and request the required output length, rather than treating the larger context figure as a reason to include unrelated material.
The official quickstart uses system and user messages with the model’s chat template, then decodes the generated continuation. Prepare coding work with the relevant source, language, expected behaviour and constraints. Prepare a mathematical question with the givens and the format of the result. The general model’s documented improvements in these areas do not make it the separately trained Qwen2.5-Coder or Qwen2.5-Math edition. An illustrative coding brief might ask for an explanation of a function, a proposed correction and why that proposal meets the stated conditions; execution and verification remain separate steps.
Qwen’s release documentation also describes tool calling and a tool-aware chat template, including compatibility with Qwen-Agent. Define available tools and their arguments, state the goal and provide returned results for the model to interpret. A document assistant can request relevant information before composing a final answer, while a code assistant can reason about the output of an application-executed check. The useful deliverable is then a response tied to the supplied evidence and requested format. This is a text-model profile; visual, audio or video-generation capabilities of other Qwen models are separate.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits qwen/qwen-2.5-7b-instruct | 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 $0.0002 / 1K tokens Output · Output tokens $0.0004 / 1K tokens Input · Cache read $0 / 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 $0.0002 / 1K tokens Output · Output tokens $0.0004 / 1K tokens Input · Cache read $0 / 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/qwen-2.5-7b-instruct", "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/qwen-2.5-7b-instruct',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/qwen-2.5-7b-instruct',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/qwen-2.5-7b-instruct
Use the method paths shown above.
qwen/qwen-2.5-7b-instructQwen2.5-7B-Instruct is Qwen’s compact instruction-tuned text model for multilingual writing, coding, mathematics and structured data. Its producer documents up to 131,072 context tokens with the long-context configuration and generation of up to 8,192 tokens.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Qwen2.5-7B-Instruct is the instruction-tuned 7B text model in Qwen2.5’s general-purpose language-model family. Qwen highlights coding and mathematics, instruction following, long-form writing, table understanding and structured output, especially JSON. It is a dense, decoder-only model with 7.61 billion total parameters, including 6.53 billion non-embedding parameters. It accepts text conversations and generates text. A useful brief can ask it to turn supplied information into a clear explanation, a structured record, a revised draft or a proposed program.
The model supports more than 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai and Arabic. The producer also emphasises resilience to varied system prompts, useful for defining a conversational role and its conditions. Give the role, audience, terminology and output language directly. For example, an editing request can provide a technical passage, specify the intended reader and ask for a shorter explanation that preserves named terms and stated conditions. The instruction-tuned edition is the conversational model, distinct from the corresponding pretrained base model.
Structured-data comprehension and JSON generation are central improvements in Qwen2.5. To extract or transform a table, preserve its headers and units and explain which rows or relationships matter. Define the requested keys, value types and treatment of missing information rather than asking only for a generic summary. An illustrative brief could supply a list of issues and request one object per issue containing the source identifier, category and a short explanation. Such a result remains generated text: checking its structure and values against the input is part of using it in a downstream workflow.
The exact card describes a full context length of 131,072 tokens and generation of up to 8,192 tokens. Its supplied configuration is set to 32,768 context tokens; handling longer material uses the documented YaRN configuration. The producer cautions that static scaling can affect performance on shorter inputs and recommends adding it when long-context processing is needed. Keep the input and requested answer within the relevant configured space. For a long report, retain section titles, prioritise the passages tied to the question and request the required output length, rather than treating the larger context figure as a reason to include unrelated material.
The official quickstart uses system and user messages with the model’s chat template, then decodes the generated continuation. Prepare coding work with the relevant source, language, expected behaviour and constraints. Prepare a mathematical question with the givens and the format of the result. The general model’s documented improvements in these areas do not make it the separately trained Qwen2.5-Coder or Qwen2.5-Math edition. An illustrative coding brief might ask for an explanation of a function, a proposed correction and why that proposal meets the stated conditions; execution and verification remain separate steps.
Qwen’s release documentation also describes tool calling and a tool-aware chat template, including compatibility with Qwen-Agent. Define available tools and their arguments, state the goal and provide returned results for the model to interpret. A document assistant can request relevant information before composing a final answer, while a code assistant can reason about the output of an application-executed check. The useful deliverable is then a response tied to the supplied evidence and requested format. This is a text-model profile; visual, audio or video-generation capabilities of other Qwen models are separate.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits qwen/qwen-2.5-7b-instruct | 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 $0.0002 / 1K tokens Output · Output tokens $0.0004 / 1K tokens Input · Cache read $0 / 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 $0.0002 / 1K tokens Output · Output tokens $0.0004 / 1K tokens Input · Cache read $0 / 1K tokens Input · Cache write $0 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "qwen/qwen-2.5-7b-instruct",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${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/qwen-2.5-7b-instruct', '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/qwen-2.5-7b-instruct', '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/qwen-2.5-7b-instruct
Use the method paths shown above.
Technical example in English
Transform a table with units preserved\n\nFrom this text table, report total units and rows needing follow-up: item | ordered units | received units; pens | 40 | 35; folders | 20 | 20; clips | 100 | unknown. Produce a table with item, missing units and source values, then totals that separate known quantities from unknowns. Do not treat unknown as zero.A transformed table, known ordered/received totals and a note explaining which shortage remains undetermined.Pens show a shortage of 5, folders 0, and clips unknown; totals do not silently count unknown received units as zero.