Command R7B is Cohere’s December 2024 compact text model. Its 7-billion-parameter architecture is designed for reasoning, summarisation, question answering, coding and enterprise assistance. The maker also trains it for retrieval-augmented generation and the use of multiple tools across several steps. It accepts text and generates text, with a 128K context and a 4,000-token maximum output in the published producer profile.
The model uses an autoregressive transformer with supervised fine-tuning and preference training. Its attention pattern combines three local sliding-window layers, using a 4,096-token window with rotary position encoding, with a global-attention layer. It is trained on 23 languages, including Russian, English and the languages listed in the exact model card.
The producer distinguishes conversational and instruct preambles. Conversational use adds dialogue behaviour, introductions, follow-up questions and formatting such as Markdown. Instruct use targets concise but complete responses for extraction, summarisation, translation and categorisation, without Markdown or LaTeX by default. The published model is delivered without a system preamble, so the maker recommends supplying the appropriate one for the task.
For a classification workflow, give the category definitions and tell the model how to handle an item that fits none of them. For a support response, provide the question and approved product material, then ask for an answer in the user’s language. A clear output specification prevents conversational additions from becoming accidental fields in a data record.
R7B is trained for the final answer step of retrieval-augmented generation. Its producer template joins the conversation with supplied document snippets. The maker recommends short chunks, typically 100–400 words, represented with short descriptive keys and textual or semi-structured values. It also recommends evaluating whether putting the relevant documents directly in the user message works better for a particular use case.
An illustrative task is: “Use these named passages to answer the question, identify the statement supporting the answer and mark information the passages do not contain.” Keep headings and units intact so an extracted fact can be checked against the original material. The application retrieves or supplies the documents; the model generates an answer from that context.
The maker’s tool-use example supplies JSON-schema function definitions and maintains the selected call, its plan and its result in the conversation. Each result is a dictionary associated with the matching tool-call identifier. This lets the next response use the actual observation. A code assistant can therefore work through a focused task when the surrounding application provides suitable inspection or validation tools.
The model card highlights SQL and code translation alongside snippets, explanations and rewrites. Give the schema, input or source code and the behaviour to preserve. The producer recommends low temperature or greedy decoding for code generation. Treat the generated statement as a proposed artifact until it has been checked in its intended environment.
R7B’s compact role is well suited to clear, repeated tasks and responsive conversation. Reserve room within the context for the answer and useful tool history, and make the completion criterion explicit. For source-dependent claims, provide the relevant evidence rather than expecting model size or a long prompt alone to establish correctness.
", "aboutText": "Command R7B is Cohere’s December 2024 compact text model. Its 7-billion-parameter architecture is designed for reasoning, summarisation, question answering, coding and enterprise assistance. The maker also trains it for retrieval-augmented generation and the use of multiple tools across several steps. It accepts text and generates text, with a 128K context and a 4,000-token maximum output in the published producer profile.\n\n### Model and response behaviour\n\nThe model uses an autoregressive transformer with supervised fine-tuning and preference training. Its attention pattern combines three local sliding-window layers, using a 4,096-token window with rotary position encoding, with a global-attention layer. It is trained on 23 languages, including Russian, English and the languages listed in the exact model card.\n\nThe producer distinguishes conversational and instruct preambles. Conversational use adds dialogue behaviour, introductions, follow-up questions and formatting such as Markdown. Instruct use targets concise but complete responses for extraction, summarisation, translation and categorisation, without Markdown or LaTeX by default. The published model is delivered without a system preamble, so the maker recommends supplying the appropriate one for the task.\n\nFor a classification workflow, give the category definitions and tell the model how to handle an item that fits none of them. For a support response, provide the question and approved product material, then ask for an answer in the user’s language. A clear output specification prevents conversational additions from becoming accidental fields in a data record.\n\n### Answers from documents\n\nR7B is trained for the final answer step of retrieval-augmented generation. Its producer template joins the conversation with supplied document snippets. The maker recommends short chunks, typically 100–400 words, represented with short descriptive keys and textual or semi-structured values. It also recommends evaluating whether putting the relevant documents directly in the user message works better for a particular use case.\n\nAn illustrative task is: “Use these named passages to answer the question, identify the statement supporting the answer and mark information the passages do not contain.” Keep headings and units intact so an extracted fact can be checked against the original material. The application retrieves or supplies the documents; the model generates an answer from that context.\n\n### Tools and coding\n\nThe maker’s tool-use example supplies JSON-schema function definitions and maintains the selected call, its plan and its result in the conversation. Each result is a dictionary associated with the matching tool-call identifier. This lets the next response use the actual observation. A code assistant can therefore work through a focused task when the surrounding application provides suitable inspection or validation tools.\n\nThe model card highlights SQL and code translation alongside snippets, explanations and rewrites. Give the schema, input or source code and the behaviour to preserve. The producer recommends low temperature or greedy decoding for code generation. Treat the generated statement as a proposed artifact until it has been checked in its intended environment.\n\nR7B’s compact role is well suited to clear, repeated tasks and responsive conversation. Reserve room within the context for the answer and useful tool history, and make the completion criterion explicit. For source-dependent claims, provide the relevant evidence rather than expecting model size or a long prompt alone to establish correctness.", "editorialOverlay": { "copy_fields": { "practical_html": "Technical example in English
Answer from support snippets\n\nAnswer the customer using only these support snippets. [Returns] Unused items can be returned within 14 days of receipt. [Shipping] Return shipping is paid by the buyer unless the item is defective. Question: "My unused item arrived 10 days ago. Can I return it, and who pays postage?" Cite the relevant snippet names.A concise customer answer citing Returns and Shipping, including the defect exception.Ensure the answer uses receipt time and preserves the buyer-paid postage exception.cohere/command-r7b-12-2024Command R7B is Cohere’s compact 7B text model for chat, extraction, code assistance and tool-guided agents. The December 2024 edition is trained on 23 languages, supports a 128K context and is designed for focused, responsive enterprise tasks with supplied documents or tool results.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Command R7B is Cohere’s December 2024 compact text model. Its 7-billion-parameter architecture is designed for reasoning, summarisation, question answering, coding and enterprise assistance. The maker also trains it for retrieval-augmented generation and the use of multiple tools across several steps. It accepts text and generates text, with a 128K context and a 4,000-token maximum output in the published producer profile.
The model uses an autoregressive transformer with supervised fine-tuning and preference training. Its attention pattern combines three local sliding-window layers, using a 4,096-token window with rotary position encoding, with a global-attention layer. It is trained on 23 languages, including Russian, English and the languages listed in the exact model card.
The producer distinguishes conversational and instruct preambles. Conversational use adds dialogue behaviour, introductions, follow-up questions and formatting such as Markdown. Instruct use targets concise but complete responses for extraction, summarisation, translation and categorisation, without Markdown or LaTeX by default. The published model is delivered without a system preamble, so the maker recommends supplying the appropriate one for the task.
For a classification workflow, give the category definitions and tell the model how to handle an item that fits none of them. For a support response, provide the question and approved product material, then ask for an answer in the user’s language. A clear output specification prevents conversational additions from becoming accidental fields in a data record.
R7B is trained for the final answer step of retrieval-augmented generation. Its producer template joins the conversation with supplied document snippets. The maker recommends short chunks, typically 100–400 words, represented with short descriptive keys and textual or semi-structured values. It also recommends evaluating whether putting the relevant documents directly in the user message works better for a particular use case.
An illustrative task is: “Use these named passages to answer the question, identify the statement supporting the answer and mark information the passages do not contain.” Keep headings and units intact so an extracted fact can be checked against the original material. The application retrieves or supplies the documents; the model generates an answer from that context.
The maker’s tool-use example supplies JSON-schema function definitions and maintains the selected call, its plan and its result in the conversation. Each result is a dictionary associated with the matching tool-call identifier. This lets the next response use the actual observation. A code assistant can therefore work through a focused task when the surrounding application provides suitable inspection or validation tools.
The model card highlights SQL and code translation alongside snippets, explanations and rewrites. Give the schema, input or source code and the behaviour to preserve. The producer recommends low temperature or greedy decoding for code generation. Treat the generated statement as a proposed artifact until it has been checked in its intended environment.
R7B’s compact role is well suited to clear, repeated tasks and responsive conversation. Reserve room within the context for the answer and useful tool history, and make the completion criterion explicit. For source-dependent claims, provide the relevant evidence rather than expecting model size or a long prompt alone to establish correctness.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits cohere/command-r7b-12-2024 | 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 $0.000075 / 1K tokens Output · Output tokens $0.0003 / 1K tokens Input · Cache read — / 1K tokens Input · Cache write — / 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 $0.000075 / 1K tokens Output · Output tokens $0.0003 / 1K tokens Input · Cache read — / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "cohere/command-r7b-12-2024", "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='cohere/command-r7b-12-2024',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='cohere/command-r7b-12-2024',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 cohere/command-r7b-12-2024
Use the method paths shown above.
cohere/command-r7b-12-2024Command R7B is Cohere’s compact 7B text model for chat, extraction, code assistance and tool-guided agents. The December 2024 edition is trained on 23 languages, supports a 128K context and is designed for focused, responsive enterprise tasks with supplied documents or tool results.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Command R7B is Cohere’s December 2024 compact text model. Its 7-billion-parameter architecture is designed for reasoning, summarisation, question answering, coding and enterprise assistance. The maker also trains it for retrieval-augmented generation and the use of multiple tools across several steps. It accepts text and generates text, with a 128K context and a 4,000-token maximum output in the published producer profile.
The model uses an autoregressive transformer with supervised fine-tuning and preference training. Its attention pattern combines three local sliding-window layers, using a 4,096-token window with rotary position encoding, with a global-attention layer. It is trained on 23 languages, including Russian, English and the languages listed in the exact model card.
The producer distinguishes conversational and instruct preambles. Conversational use adds dialogue behaviour, introductions, follow-up questions and formatting such as Markdown. Instruct use targets concise but complete responses for extraction, summarisation, translation and categorisation, without Markdown or LaTeX by default. The published model is delivered without a system preamble, so the maker recommends supplying the appropriate one for the task.
For a classification workflow, give the category definitions and tell the model how to handle an item that fits none of them. For a support response, provide the question and approved product material, then ask for an answer in the user’s language. A clear output specification prevents conversational additions from becoming accidental fields in a data record.
R7B is trained for the final answer step of retrieval-augmented generation. Its producer template joins the conversation with supplied document snippets. The maker recommends short chunks, typically 100–400 words, represented with short descriptive keys and textual or semi-structured values. It also recommends evaluating whether putting the relevant documents directly in the user message works better for a particular use case.
An illustrative task is: “Use these named passages to answer the question, identify the statement supporting the answer and mark information the passages do not contain.” Keep headings and units intact so an extracted fact can be checked against the original material. The application retrieves or supplies the documents; the model generates an answer from that context.
The maker’s tool-use example supplies JSON-schema function definitions and maintains the selected call, its plan and its result in the conversation. Each result is a dictionary associated with the matching tool-call identifier. This lets the next response use the actual observation. A code assistant can therefore work through a focused task when the surrounding application provides suitable inspection or validation tools.
The model card highlights SQL and code translation alongside snippets, explanations and rewrites. Give the schema, input or source code and the behaviour to preserve. The producer recommends low temperature or greedy decoding for code generation. Treat the generated statement as a proposed artifact until it has been checked in its intended environment.
R7B’s compact role is well suited to clear, repeated tasks and responsive conversation. Reserve room within the context for the answer and useful tool history, and make the completion criterion explicit. For source-dependent claims, provide the relevant evidence rather than expecting model size or a long prompt alone to establish correctness.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits cohere/command-r7b-12-2024 | 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 $0.000075 / 1K tokens Output · Output tokens $0.0003 / 1K tokens Input · Cache read — / 1K tokens Input · Cache write — / 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 $0.000075 / 1K tokens Output · Output tokens $0.0003 / 1K tokens Input · Cache read — / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "cohere/command-r7b-12-2024",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${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': 'cohere/command-r7b-12-2024', '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': 'cohere/command-r7b-12-2024', '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 cohere/command-r7b-12-2024
Use the method paths shown above.
Technical example in English
Answer from support snippets\n\nAnswer the customer using only these support snippets. [Returns] Unused items can be returned within 14 days of receipt. [Shipping] Return shipping is paid by the buyer unless the item is defective. Question: "My unused item arrived 10 days ago. Can I return it, and who pays postage?" Cite the relevant snippet names.A concise customer answer citing Returns and Shipping, including the defect exception.Ensure the answer uses receipt time and preserves the buyer-paid postage exception.