Mistral Large 2407 is the 24.07 edition of Mistral AI’s Mistral Large 2. It is a dense language model with 123 billion parameters and a 128K context window. The model processes written instructions and conversation to generate text, code or JSON. This fixed edition remains separate from the later multimodal Large generations and from a moving Large-family selection.
Multilingual work is central to the release. Its producer describes support for dozens of languages, including English, French, German, Spanish, Italian, Portuguese, Dutch, Russian, Chinese, Japanese, Korean, Arabic and Hindi. For a document task, specify the source language, the language of the answer and any terminology that should remain unchanged. A useful result can compare two passages, summarize a long record or extract the information needed for a business decision.
Coding is another major focus: the training covers more than 80 programming languages, including Python, Java, C, C++, JavaScript, Bash, Swift and Fortran. Prepare a software assignment with the relevant code, the expected behavior and the constraint to preserve. Ask for the proposed change and the reasoning behind it, followed by checks that address the observed failure. This ties the generated code to a defined result rather than leaving the task at a plausible snippet.
The release emphasizes mathematical reasoning, precise instruction following and long multi-turn conversations. Its training encourages concise responses and acknowledging missing information when a confident solution is not supported. A problem-solving prompt can supply the premises and request the conclusion, the intermediate steps that matter and any missing input. Follow-up turns can refine a constraint while keeping the established conversation available.
Native function calling supports both parallel and successive calls, and JSON output can pass information to an application. Describe the available functions and their input fields, then provide the user’s objective. The model proposes the function calls; the application executes them and returns their results before the model continues. Preserve the call-to-result association throughout that exchange. The exact edition’s published examples use tool-call identifiers of nine alphanumeric characters in the producer’s protocol.
For retrieval-based work, supply the relevant material or let the surrounding application retrieve it. Ask the answer to identify the passage supporting each conclusion. For example, a policy assistant can compare a user’s situation with the supplied policy and return the applicable rule, the evidence and the next question to resolve. A JSON extraction task should define the required fields and the treatment of information absent from the source. JSON generation is a model capability; the application still checks whether the result matches its required structure.
The published instruct checkpoint has no built-in moderation mechanisms, as its model card explicitly notes. An application requiring moderated output needs its own controls. The model’s long context also remains a finite resource: prepare the relevant document sections and conversation so that the requested answer has room alongside the input.
", "aboutText": "Mistral Large 2407 is the 24.07 edition of Mistral AI’s Mistral Large 2. It is a dense language model with 123 billion parameters and a 128K context window. The model processes written instructions and conversation to generate text, code or JSON. This fixed edition remains separate from the later multimodal Large generations and from a moving Large-family selection.\n\nMultilingual work is central to the release. Its producer describes support for dozens of languages, including English, French, German, Spanish, Italian, Portuguese, Dutch, Russian, Chinese, Japanese, Korean, Arabic and Hindi. For a document task, specify the source language, the language of the answer and any terminology that should remain unchanged. A useful result can compare two passages, summarize a long record or extract the information needed for a business decision.\n\nCoding is another major focus: the training covers more than 80 programming languages, including Python, Java, C, C++, JavaScript, Bash, Swift and Fortran. Prepare a software assignment with the relevant code, the expected behavior and the constraint to preserve. Ask for the proposed change and the reasoning behind it, followed by checks that address the observed failure. This ties the generated code to a defined result rather than leaving the task at a plausible snippet.\n\nThe release emphasizes mathematical reasoning, precise instruction following and long multi-turn conversations. Its training encourages concise responses and acknowledging missing information when a confident solution is not supported. A problem-solving prompt can supply the premises and request the conclusion, the intermediate steps that matter and any missing input. Follow-up turns can refine a constraint while keeping the established conversation available.\n\nNative function calling supports both parallel and successive calls, and JSON output can pass information to an application. Describe the available functions and their input fields, then provide the user’s objective. The model proposes the function calls; the application executes them and returns their results before the model continues. Preserve the call-to-result association throughout that exchange. The exact edition’s published examples use tool-call identifiers of nine alphanumeric characters in the producer’s protocol.\n\nFor retrieval-based work, supply the relevant material or let the surrounding application retrieve it. Ask the answer to identify the passage supporting each conclusion. For example, a policy assistant can compare a user’s situation with the supplied policy and return the applicable rule, the evidence and the next question to resolve. A JSON extraction task should define the required fields and the treatment of information absent from the source. JSON generation is a model capability; the application still checks whether the result matches its required structure.\n\nThe published instruct checkpoint has no built-in moderation mechanisms, as its model card explicitly notes. An application requiring moderated output needs its own controls. The model’s long context also remains a finite resource: prepare the relevant document sections and conversation so that the requested answer has room alongside the input.", "editorialOverlay": { "copy_fields": { "practical_html": "Technical example in English
Reconcile bilingual product instructions\n\nCompare these instructions and answer in English. French source: 'Nettoyer avec un chiffon humide. Ne pas immerger. Sécher avant rangement.' English draft: 'Wash in water and store immediately.' Identify each meaning that the draft changes or omits, then provide a corrected English version. Preserve the source's sequence and avoid adding cleaning products or temperature limits.A short discrepancy list and a corrected English instruction preserving all three source directions.The correction retains wiping, the prohibition on immersion and drying before storage.mistralai/mistral-large-2407Mistral Large 2407 is the dense 123B Mistral Large 2 edition for multilingual text, coding and reasoning over a 128K context. Native function calling, JSON output and multi-turn instruction following support document and business workflows.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Mistral Large 2407 is the 24.07 edition of Mistral AI’s Mistral Large 2. It is a dense language model with 123 billion parameters and a 128K context window. The model processes written instructions and conversation to generate text, code or JSON. This fixed edition remains separate from the later multimodal Large generations and from a moving Large-family selection.
Multilingual work is central to the release. Its producer describes support for dozens of languages, including English, French, German, Spanish, Italian, Portuguese, Dutch, Russian, Chinese, Japanese, Korean, Arabic and Hindi. For a document task, specify the source language, the language of the answer and any terminology that should remain unchanged. A useful result can compare two passages, summarize a long record or extract the information needed for a business decision.
Coding is another major focus: the training covers more than 80 programming languages, including Python, Java, C, C++, JavaScript, Bash, Swift and Fortran. Prepare a software assignment with the relevant code, the expected behavior and the constraint to preserve. Ask for the proposed change and the reasoning behind it, followed by checks that address the observed failure. This ties the generated code to a defined result rather than leaving the task at a plausible snippet.
The release emphasizes mathematical reasoning, precise instruction following and long multi-turn conversations. Its training encourages concise responses and acknowledging missing information when a confident solution is not supported. A problem-solving prompt can supply the premises and request the conclusion, the intermediate steps that matter and any missing input. Follow-up turns can refine a constraint while keeping the established conversation available.
Native function calling supports both parallel and successive calls, and JSON output can pass information to an application. Describe the available functions and their input fields, then provide the user’s objective. The model proposes the function calls; the application executes them and returns their results before the model continues. Preserve the call-to-result association throughout that exchange. The exact edition’s published examples use tool-call identifiers of nine alphanumeric characters in the producer’s protocol.
For retrieval-based work, supply the relevant material or let the surrounding application retrieve it. Ask the answer to identify the passage supporting each conclusion. For example, a policy assistant can compare a user’s situation with the supplied policy and return the applicable rule, the evidence and the next question to resolve. A JSON extraction task should define the required fields and the treatment of information absent from the source. JSON generation is a model capability; the application still checks whether the result matches its required structure.
The published instruct checkpoint has no built-in moderation mechanisms, as its model card explicitly notes. An application requiring moderated output needs its own controls. The model’s long context also remains a finite resource: prepare the relevant document sections and conversation so that the requested answer has room alongside the input.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits mistralai/mistral-large-2407 | 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 $0.01 / 1K tokens Output · Output tokens $0.02 / 1K tokens Input · Cache read $0.0004 / 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 $0.01 / 1K tokens Output · Output tokens $0.02 / 1K tokens Input · Cache read $0.0004 / 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": "mistralai/mistral-large-2407", "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='mistralai/mistral-large-2407',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='mistralai/mistral-large-2407',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 mistralai/mistral-large-2407
Use the method paths shown above.
mistralai/mistral-large-2407Mistral Large 2407 is the dense 123B Mistral Large 2 edition for multilingual text, coding and reasoning over a 128K context. Native function calling, JSON output and multi-turn instruction following support document and business workflows.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Mistral Large 2407 is the 24.07 edition of Mistral AI’s Mistral Large 2. It is a dense language model with 123 billion parameters and a 128K context window. The model processes written instructions and conversation to generate text, code or JSON. This fixed edition remains separate from the later multimodal Large generations and from a moving Large-family selection.
Multilingual work is central to the release. Its producer describes support for dozens of languages, including English, French, German, Spanish, Italian, Portuguese, Dutch, Russian, Chinese, Japanese, Korean, Arabic and Hindi. For a document task, specify the source language, the language of the answer and any terminology that should remain unchanged. A useful result can compare two passages, summarize a long record or extract the information needed for a business decision.
Coding is another major focus: the training covers more than 80 programming languages, including Python, Java, C, C++, JavaScript, Bash, Swift and Fortran. Prepare a software assignment with the relevant code, the expected behavior and the constraint to preserve. Ask for the proposed change and the reasoning behind it, followed by checks that address the observed failure. This ties the generated code to a defined result rather than leaving the task at a plausible snippet.
The release emphasizes mathematical reasoning, precise instruction following and long multi-turn conversations. Its training encourages concise responses and acknowledging missing information when a confident solution is not supported. A problem-solving prompt can supply the premises and request the conclusion, the intermediate steps that matter and any missing input. Follow-up turns can refine a constraint while keeping the established conversation available.
Native function calling supports both parallel and successive calls, and JSON output can pass information to an application. Describe the available functions and their input fields, then provide the user’s objective. The model proposes the function calls; the application executes them and returns their results before the model continues. Preserve the call-to-result association throughout that exchange. The exact edition’s published examples use tool-call identifiers of nine alphanumeric characters in the producer’s protocol.
For retrieval-based work, supply the relevant material or let the surrounding application retrieve it. Ask the answer to identify the passage supporting each conclusion. For example, a policy assistant can compare a user’s situation with the supplied policy and return the applicable rule, the evidence and the next question to resolve. A JSON extraction task should define the required fields and the treatment of information absent from the source. JSON generation is a model capability; the application still checks whether the result matches its required structure.
The published instruct checkpoint has no built-in moderation mechanisms, as its model card explicitly notes. An application requiring moderated output needs its own controls. The model’s long context also remains a finite resource: prepare the relevant document sections and conversation so that the requested answer has room alongside the input.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits mistralai/mistral-large-2407 | 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 $0.01 / 1K tokens Output · Output tokens $0.02 / 1K tokens Input · Cache read $0.0004 / 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 $0.01 / 1K tokens Output · Output tokens $0.02 / 1K tokens Input · Cache read $0.0004 / 1K tokens Input · Cache write $0 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "mistralai/mistral-large-2407",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${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': 'mistralai/mistral-large-2407', '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': 'mistralai/mistral-large-2407', '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 mistralai/mistral-large-2407
Use the method paths shown above.
Technical example in English
Reconcile bilingual product instructions\n\nCompare these instructions and answer in English. French source: 'Nettoyer avec un chiffon humide. Ne pas immerger. Sécher avant rangement.' English draft: 'Wash in water and store immediately.' Identify each meaning that the draft changes or omits, then provide a corrected English version. Preserve the source's sequence and avoid adding cleaning products or temperature limits.A short discrepancy list and a corrected English instruction preserving all three source directions.The correction retains wiping, the prohibition on immersion and drying before storage.