Command R+ (08-2024) is Cohere’s August 2024 large text model for complex retrieval-augmented answers and agent tasks. The exact edition has 104 billion parameters and a 128K context, accepting text and producing text. The published maker profile lists a 4,000-token maximum output. R+ is the larger companion to Command R and has a particular emphasis on combining tools across multiple steps.
The model uses an autoregressive transformer with grouped-query attention, supervised fine-tuning and preference training. It is trained on 23 languages and evaluated on ten principal languages. The August refresh improves tool-selection decisions, system-instruction following, structured data analysis and robustness to non-semantic prompt changes. It also supports retrieval workflows without citations, several safety modes and declining questions that cannot be answered.
For multilingual work, specify the output language and the names, codes or source quotations that must remain unchanged. A grounded answer can explain material in a different language, while keeping the evidence available for review. Provide a current source for time-sensitive facts, or obtain one through a suitable application tool.
The producer’s RAG template accepts a conversation and retrieved document snippets, normally around 100–400 words per chunk, with short descriptive keys and text or semi-structured values. The model is specifically trained to answer from those snippets and connect answer spans with the supporting sources. Use the exact edition’s template when that trained grounding behaviour matters.
Accurate citation mode identifies relevant and cited documents, generates an answer and inserts grounding spans. Fast mode generates the cited answer directly, reducing output tokens at a trade-off in grounding accuracy. Keep source identifiers available for both modes. A practical report request can ask the model to compare evidence against named criteria and cite the supporting passage beside each finding.
R+ supports both a single-step function-calling template and multi-step tool use for harder workflows. In the single-step flow, the model selects functions and JSON arguments, the application performs them, and a later model response uses the returned results. The documented directly_answer choice lets the model avoid an unnecessary external operation. In a multi-step agent, subsequent decisions can combine observations from different tools until the task reaches its specified result.
A useful agent brief defines the question, allowed operations and final deliverable. For example, a research assistant may need to retrieve relevant product records, compare the supplied requirements and produce an evidence-backed recommendation. Give each tool a clear purpose and associate its result with the call that produced it. An operation proposed in text is distinct from execution by the application.
Organise the source snippets around the actual question and reserve room for tool history and the answer. If the available evidence conflicts, ask the model to explain the disagreement rather than choose an unsupported value. For structured data work, define the fields, units and representation of missing information.
The final result should be a reviewable answer or report tied to the supplied evidence and actual observations. R+’s size and agent focus do not independently supply a search connection, execute a database action or validate generated code. Those steps belong to the surrounding workflow, and their returned evidence is what makes a conclusion checkable.
", "aboutText": "Command R+ (08-2024) is Cohere’s August 2024 large text model for complex retrieval-augmented answers and agent tasks. The exact edition has 104 billion parameters and a 128K context, accepting text and producing text. The published maker profile lists a 4,000-token maximum output. R+ is the larger companion to Command R and has a particular emphasis on combining tools across multiple steps.\n\n### Model and edition changes\n\nThe model uses an autoregressive transformer with grouped-query attention, supervised fine-tuning and preference training. It is trained on 23 languages and evaluated on ten principal languages. The August refresh improves tool-selection decisions, system-instruction following, structured data analysis and robustness to non-semantic prompt changes. It also supports retrieval workflows without citations, several safety modes and declining questions that cannot be answered.\n\nFor multilingual work, specify the output language and the names, codes or source quotations that must remain unchanged. A grounded answer can explain material in a different language, while keeping the evidence available for review. Provide a current source for time-sensitive facts, or obtain one through a suitable application tool.\n\n### Grounded answers and citations\n\nThe producer’s RAG template accepts a conversation and retrieved document snippets, normally around 100–400 words per chunk, with short descriptive keys and text or semi-structured values. The model is specifically trained to answer from those snippets and connect answer spans with the supporting sources. Use the exact edition’s template when that trained grounding behaviour matters.\n\nAccurate citation mode identifies relevant and cited documents, generates an answer and inserts grounding spans. Fast mode generates the cited answer directly, reducing output tokens at a trade-off in grounding accuracy. Keep source identifiers available for both modes. A practical report request can ask the model to compare evidence against named criteria and cite the supporting passage beside each finding.\n\n### Tool-assisted work\n\nR+ supports both a single-step function-calling template and multi-step tool use for harder workflows. In the single-step flow, the model selects functions and JSON arguments, the application performs them, and a later model response uses the returned results. The documented directly_answer choice lets the model avoid an unnecessary external operation. In a multi-step agent, subsequent decisions can combine observations from different tools until the task reaches its specified result.\n\nA useful agent brief defines the question, allowed operations and final deliverable. For example, a research assistant may need to retrieve relevant product records, compare the supplied requirements and produce an evidence-backed recommendation. Give each tool a clear purpose and associate its result with the call that produced it. An operation proposed in text is distinct from execution by the application.\n\n### Preparing and reviewing the result\n\nOrganise the source snippets around the actual question and reserve room for tool history and the answer. If the available evidence conflicts, ask the model to explain the disagreement rather than choose an unsupported value. For structured data work, define the fields, units and representation of missing information.\n\nThe final result should be a reviewable answer or report tied to the supplied evidence and actual observations. R+’s size and agent focus do not independently supply a search connection, execute a database action or validate generated code. Those steps belong to the surrounding workflow, and their returned evidence is what makes a conclusion checkable.", "editorialOverlay": { "copy_fields": { "practical_html": "Technical example in English
Answer from product evidence\n\nUse only these snippets to answer whether Team Plan supports both requested features. S1: Team Plan includes shared folders and 30-day activity history. S2: Single sign-on is available on Enterprise Plan. Request: We need shared folders and single sign-on. State what is supported, cite each source identifier and leave unsupported assumptions unresolved.A feature-by-feature answer with a source identifier beside each finding and a concise conclusion about the two requirements.Check that S1 supports shared folders and S2 does not establish single sign-on for Team Plan.cohere/command-r-plus-08-2024Command R+ (08-2024) is Cohere’s 104B text model for complex document-grounded answers and multi-step tool use. It combines a 128K context with multilingual generation, source-span citations and agent workflows that use the application’s actual tool observations.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Command R+ (08-2024) is Cohere’s August 2024 large text model for complex retrieval-augmented answers and agent tasks. The exact edition has 104 billion parameters and a 128K context, accepting text and producing text. The published maker profile lists a 4,000-token maximum output. R+ is the larger companion to Command R and has a particular emphasis on combining tools across multiple steps.
The model uses an autoregressive transformer with grouped-query attention, supervised fine-tuning and preference training. It is trained on 23 languages and evaluated on ten principal languages. The August refresh improves tool-selection decisions, system-instruction following, structured data analysis and robustness to non-semantic prompt changes. It also supports retrieval workflows without citations, several safety modes and declining questions that cannot be answered.
For multilingual work, specify the output language and the names, codes or source quotations that must remain unchanged. A grounded answer can explain material in a different language, while keeping the evidence available for review. Provide a current source for time-sensitive facts, or obtain one through a suitable application tool.
The producer’s RAG template accepts a conversation and retrieved document snippets, normally around 100–400 words per chunk, with short descriptive keys and text or semi-structured values. The model is specifically trained to answer from those snippets and connect answer spans with the supporting sources. Use the exact edition’s template when that trained grounding behaviour matters.
Accurate citation mode identifies relevant and cited documents, generates an answer and inserts grounding spans. Fast mode generates the cited answer directly, reducing output tokens at a trade-off in grounding accuracy. Keep source identifiers available for both modes. A practical report request can ask the model to compare evidence against named criteria and cite the supporting passage beside each finding.
R+ supports both a single-step function-calling template and multi-step tool use for harder workflows. In the single-step flow, the model selects functions and JSON arguments, the application performs them, and a later model response uses the returned results. The documented directly_answer choice lets the model avoid an unnecessary external operation. In a multi-step agent, subsequent decisions can combine observations from different tools until the task reaches its specified result.
A useful agent brief defines the question, allowed operations and final deliverable. For example, a research assistant may need to retrieve relevant product records, compare the supplied requirements and produce an evidence-backed recommendation. Give each tool a clear purpose and associate its result with the call that produced it. An operation proposed in text is distinct from execution by the application.
Organise the source snippets around the actual question and reserve room for tool history and the answer. If the available evidence conflicts, ask the model to explain the disagreement rather than choose an unsupported value. For structured data work, define the fields, units and representation of missing information.
The final result should be a reviewable answer or report tied to the supplied evidence and actual observations. R+’s size and agent focus do not independently supply a search connection, execute a database action or validate generated code. Those steps belong to the surrounding workflow, and their returned evidence is what makes a conclusion checkable.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits cohere/command-r-plus-08-2024 | 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 $0.005 / 1K tokens Output · Output tokens $0.02 / 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 $0.005 / 1K tokens Output · Output tokens $0.02 / 1K tokens Input · Cache read — / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "cohere/command-r-plus-08-2024", "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-r-plus-08-2024',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-r-plus-08-2024',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-r-plus-08-2024
Use the method paths shown above.
cohere/command-r-plus-08-2024Command R+ (08-2024) is Cohere’s 104B text model for complex document-grounded answers and multi-step tool use. It combines a 128K context with multilingual generation, source-span citations and agent workflows that use the application’s actual tool observations.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Command R+ (08-2024) is Cohere’s August 2024 large text model for complex retrieval-augmented answers and agent tasks. The exact edition has 104 billion parameters and a 128K context, accepting text and producing text. The published maker profile lists a 4,000-token maximum output. R+ is the larger companion to Command R and has a particular emphasis on combining tools across multiple steps.
The model uses an autoregressive transformer with grouped-query attention, supervised fine-tuning and preference training. It is trained on 23 languages and evaluated on ten principal languages. The August refresh improves tool-selection decisions, system-instruction following, structured data analysis and robustness to non-semantic prompt changes. It also supports retrieval workflows without citations, several safety modes and declining questions that cannot be answered.
For multilingual work, specify the output language and the names, codes or source quotations that must remain unchanged. A grounded answer can explain material in a different language, while keeping the evidence available for review. Provide a current source for time-sensitive facts, or obtain one through a suitable application tool.
The producer’s RAG template accepts a conversation and retrieved document snippets, normally around 100–400 words per chunk, with short descriptive keys and text or semi-structured values. The model is specifically trained to answer from those snippets and connect answer spans with the supporting sources. Use the exact edition’s template when that trained grounding behaviour matters.
Accurate citation mode identifies relevant and cited documents, generates an answer and inserts grounding spans. Fast mode generates the cited answer directly, reducing output tokens at a trade-off in grounding accuracy. Keep source identifiers available for both modes. A practical report request can ask the model to compare evidence against named criteria and cite the supporting passage beside each finding.
R+ supports both a single-step function-calling template and multi-step tool use for harder workflows. In the single-step flow, the model selects functions and JSON arguments, the application performs them, and a later model response uses the returned results. The documented directly_answer choice lets the model avoid an unnecessary external operation. In a multi-step agent, subsequent decisions can combine observations from different tools until the task reaches its specified result.
A useful agent brief defines the question, allowed operations and final deliverable. For example, a research assistant may need to retrieve relevant product records, compare the supplied requirements and produce an evidence-backed recommendation. Give each tool a clear purpose and associate its result with the call that produced it. An operation proposed in text is distinct from execution by the application.
Organise the source snippets around the actual question and reserve room for tool history and the answer. If the available evidence conflicts, ask the model to explain the disagreement rather than choose an unsupported value. For structured data work, define the fields, units and representation of missing information.
The final result should be a reviewable answer or report tied to the supplied evidence and actual observations. R+’s size and agent focus do not independently supply a search connection, execute a database action or validate generated code. Those steps belong to the surrounding workflow, and their returned evidence is what makes a conclusion checkable.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits cohere/command-r-plus-08-2024 | 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 $0.005 / 1K tokens Output · Output tokens $0.02 / 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 $0.005 / 1K tokens Output · Output tokens $0.02 / 1K tokens Input · Cache read — / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "cohere/command-r-plus-08-2024",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${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-r-plus-08-2024', '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-r-plus-08-2024', '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-r-plus-08-2024
Use the method paths shown above.
Technical example in English
Answer from product evidence\n\nUse only these snippets to answer whether Team Plan supports both requested features. S1: Team Plan includes shared folders and 30-day activity history. S2: Single sign-on is available on Enterprise Plan. Request: We need shared folders and single sign-on. State what is supported, cite each source identifier and leave unsupported assumptions unresolved.A feature-by-feature answer with a source identifier beside each finding and a concise conclusion about the two requirements.Check that S1 supports shared folders and S2 does not establish single sign-on for Team Plan.