MiMo-V2.6-Pro-UltraSpeed is Xiaomi’s UltraSpeed mode for the MiMo-V2.6-Pro model. The manufacturer introduces it for interactions and workflows that are sensitive to generation time, while describing its output quality as matching Pro. UltraSpeed names a way of running the Pro model rather than the smaller Flash model. Choose it when obtaining the next response promptly matters to an extended task, while keeping the same requirements for the final artifact.
The underlying Pro family is natively omnimodal, understanding text, images, video and audio. Its published Pro model profile describes a sparse mixture-of-experts architecture with 1.02 trillion total parameters, 42 billion activated parameters and a context of one million tokens. Dedicated vision and audio encoders connect the media inputs to the language backbone. The direct result is text or tool directions; any rendered scene, edited file or generated media depends on the tools used by the surrounding application.
Xiaomi’s V2.6 training mixes coding, general agent work, visual tasks and cybersecurity across different environments. Its feedback process compares task trajectories and uses both test outcomes and quality criteria, aiming to develop useful strategies across extended interactions. Prepare a brief that states the goal, the available evidence and what will count as completion. Faster generation is useful when it advances those interactions; it does not replace checking whether each step answers the question or changes the intended artifact.
For a coding assistant, provide the affected files, the requested behavior and the failure or check output that motivates the work. Ask for the next focused change, then return the actual result from any execution tools. A useful final response explains the behavior changed, the files affected and the checks actually performed. Keep proposed operations distinct from completed ones so a quick sequence of responses remains traceable to observable work.
The maker’s V2.6 examples extend coding to games, 3D objects and interactive scenes, with visual feedback used to refine their implementation. For a design task, combine a text brief with reference images or a recording and specify the required controls, layout and visible completion criteria. A scene or interface proposal can then guide the appropriate production tools. Retain the rendered result and its inspection findings rather than treating the initial code or scene instructions as a finished visual artifact.
For presentations and video workflows, define the audience, required content, intended sequence and assets that must remain consistent. Xiaomi describes the Pro family working with design, image, video and narration tools; those are supporting workflows rather than additional native media outputs from the text model. The useful deliverable can be a slide structure, shot sequence or production instruction set with enough detail to carry out and inspect the next step.
For research, supply the question and authoritative material, and ask the model to distinguish facts, hypotheses and calculations. Xiaomi’s Pro examples include literature and patent review, computational materials screening, music scores and MIDI, and mathematical formalization checked with Lean. Preserve the outputs of the actual research or verification tools. A simulated candidate or proposed proof should remain identified as such until the corresponding physical experiment or formal check has been completed.
Keep the Pro-UltraSpeed identity with the result when comparing it with ordinary Pro or Flash. Organize long histories around the goal and the decisions that must remain stable, and leave room for the response and continuing tool feedback. Review important code, observations, calculations and claims against their source evidence. The distinguishing purpose of this mode is quicker Pro generation; the completed artifact still needs to satisfy the same task-specific criteria.
", "aboutText": "MiMo-V2.6-Pro-UltraSpeed is Xiaomi’s UltraSpeed mode for the MiMo-V2.6-Pro model. The manufacturer introduces it for interactions and workflows that are sensitive to generation time, while describing its output quality as matching Pro. UltraSpeed names a way of running the Pro model rather than the smaller Flash model. Choose it when obtaining the next response promptly matters to an extended task, while keeping the same requirements for the final artifact.\n\nThe underlying Pro family is natively omnimodal, understanding text, images, video and audio. Its published Pro model profile describes a sparse mixture-of-experts architecture with 1.02 trillion total parameters, 42 billion activated parameters and a context of one million tokens. Dedicated vision and audio encoders connect the media inputs to the language backbone. The direct result is text or tool directions; any rendered scene, edited file or generated media depends on the tools used by the surrounding application.\n\nXiaomi’s V2.6 training mixes coding, general agent work, visual tasks and cybersecurity across different environments. Its feedback process compares task trajectories and uses both test outcomes and quality criteria, aiming to develop useful strategies across extended interactions. Prepare a brief that states the goal, the available evidence and what will count as completion. Faster generation is useful when it advances those interactions; it does not replace checking whether each step answers the question or changes the intended artifact.\n\nFor a coding assistant, provide the affected files, the requested behavior and the failure or check output that motivates the work. Ask for the next focused change, then return the actual result from any execution tools. A useful final response explains the behavior changed, the files affected and the checks actually performed. Keep proposed operations distinct from completed ones so a quick sequence of responses remains traceable to observable work.\n\nThe maker’s V2.6 examples extend coding to games, 3D objects and interactive scenes, with visual feedback used to refine their implementation. For a design task, combine a text brief with reference images or a recording and specify the required controls, layout and visible completion criteria. A scene or interface proposal can then guide the appropriate production tools. Retain the rendered result and its inspection findings rather than treating the initial code or scene instructions as a finished visual artifact.\n\nFor presentations and video workflows, define the audience, required content, intended sequence and assets that must remain consistent. Xiaomi describes the Pro family working with design, image, video and narration tools; those are supporting workflows rather than additional native media outputs from the text model. The useful deliverable can be a slide structure, shot sequence or production instruction set with enough detail to carry out and inspect the next step.\n\nFor research, supply the question and authoritative material, and ask the model to distinguish facts, hypotheses and calculations. Xiaomi’s Pro examples include literature and patent review, computational materials screening, music scores and MIDI, and mathematical formalization checked with Lean. Preserve the outputs of the actual research or verification tools. A simulated candidate or proposed proof should remain identified as such until the corresponding physical experiment or formal check has been completed.\n\nKeep the Pro-UltraSpeed identity with the result when comparing it with ordinary Pro or Flash. Organize long histories around the goal and the decisions that must remain stable, and leave room for the response and continuing tool feedback. Review important code, observations, calculations and claims against their source evidence. The distinguishing purpose of this mode is quicker Pro generation; the completed artifact still needs to satisfy the same task-specific criteria.", "editorialOverlay": { "copy_fields": { "practical_html": "Technical example in English
Revise a failing total calculation\n\nUse this code and reported check: def total(rows): return sum(row["price"] for row in rows). Input is [{"price": 3, "qty": 2}, {"price": 5, "qty": 1}]; reported output is 8, required output is 11. Propose the next focused change and checks for multiple quantities and an empty input. Do not claim to run them.A focused Python correction with a diagnosis and two proposed checks tied to the quantity requirement.Verify each unit price is multiplied by its row quantity and proposed checks remain distinct from executed results.xiaomi/mimo-v2.6-pro-ultraspeedMiMo-V2.6-Pro-UltraSpeed is Xiaomi’s faster-generation mode for MiMo-V2.6-Pro. It retains the Pro family’s omnimodal understanding and extended coding, design and research task scope, while making response speed the distinguishing focus of the mode.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
MiMo-V2.6-Pro-UltraSpeed is Xiaomi’s UltraSpeed mode for the MiMo-V2.6-Pro model. The manufacturer introduces it for interactions and workflows that are sensitive to generation time, while describing its output quality as matching Pro. UltraSpeed names a way of running the Pro model rather than the smaller Flash model. Choose it when obtaining the next response promptly matters to an extended task, while keeping the same requirements for the final artifact.
The underlying Pro family is natively omnimodal, understanding text, images, video and audio. Its published Pro model profile describes a sparse mixture-of-experts architecture with 1.02 trillion total parameters, 42 billion activated parameters and a context of one million tokens. Dedicated vision and audio encoders connect the media inputs to the language backbone. The direct result is text or tool directions; any rendered scene, edited file or generated media depends on the tools used by the surrounding application.
Xiaomi’s V2.6 training mixes coding, general agent work, visual tasks and cybersecurity across different environments. Its feedback process compares task trajectories and uses both test outcomes and quality criteria, aiming to develop useful strategies across extended interactions. Prepare a brief that states the goal, the available evidence and what will count as completion. Faster generation is useful when it advances those interactions; it does not replace checking whether each step answers the question or changes the intended artifact.
For a coding assistant, provide the affected files, the requested behavior and the failure or check output that motivates the work. Ask for the next focused change, then return the actual result from any execution tools. A useful final response explains the behavior changed, the files affected and the checks actually performed. Keep proposed operations distinct from completed ones so a quick sequence of responses remains traceable to observable work.
The maker’s V2.6 examples extend coding to games, 3D objects and interactive scenes, with visual feedback used to refine their implementation. For a design task, combine a text brief with reference images or a recording and specify the required controls, layout and visible completion criteria. A scene or interface proposal can then guide the appropriate production tools. Retain the rendered result and its inspection findings rather than treating the initial code or scene instructions as a finished visual artifact.
For presentations and video workflows, define the audience, required content, intended sequence and assets that must remain consistent. Xiaomi describes the Pro family working with design, image, video and narration tools; those are supporting workflows rather than additional native media outputs from the text model. The useful deliverable can be a slide structure, shot sequence or production instruction set with enough detail to carry out and inspect the next step.
For research, supply the question and authoritative material, and ask the model to distinguish facts, hypotheses and calculations. Xiaomi’s Pro examples include literature and patent review, computational materials screening, music scores and MIDI, and mathematical formalization checked with Lean. Preserve the outputs of the actual research or verification tools. A simulated candidate or proposed proof should remain identified as such until the corresponding physical experiment or formal check has been completed.
Keep the Pro-UltraSpeed identity with the result when comparing it with ordinary Pro or Flash. Organize long histories around the goal and the decisions that must remain stable, and leave room for the response and continuing tool feedback. Review important code, observations, calculations and claims against their source evidence. The distinguishing purpose of this mode is quicker Pro generation; the completed artifact still needs to satisfy the same task-specific criteria.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits xiaomi/mimo-v2.6-pro-ultraspeed | 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 $0.0087 / 1K tokens Output · Output tokens $0.0174 / 1K tokens Input · Cache read $0.000072 / 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 $0.0087 / 1K tokens Output · Output tokens $0.0174 / 1K tokens Input · Cache read $0.000072 / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "xiaomi/mimo-v2.6-pro-ultraspeed", "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='xiaomi/mimo-v2.6-pro-ultraspeed',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='xiaomi/mimo-v2.6-pro-ultraspeed',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 xiaomi/mimo-v2.6-pro-ultraspeed
Use the method paths shown above.
xiaomi/mimo-v2.6-pro-ultraspeedMiMo-V2.6-Pro-UltraSpeed is Xiaomi’s faster-generation mode for MiMo-V2.6-Pro. It retains the Pro family’s omnimodal understanding and extended coding, design and research task scope, while making response speed the distinguishing focus of the mode.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
MiMo-V2.6-Pro-UltraSpeed is Xiaomi’s UltraSpeed mode for the MiMo-V2.6-Pro model. The manufacturer introduces it for interactions and workflows that are sensitive to generation time, while describing its output quality as matching Pro. UltraSpeed names a way of running the Pro model rather than the smaller Flash model. Choose it when obtaining the next response promptly matters to an extended task, while keeping the same requirements for the final artifact.
The underlying Pro family is natively omnimodal, understanding text, images, video and audio. Its published Pro model profile describes a sparse mixture-of-experts architecture with 1.02 trillion total parameters, 42 billion activated parameters and a context of one million tokens. Dedicated vision and audio encoders connect the media inputs to the language backbone. The direct result is text or tool directions; any rendered scene, edited file or generated media depends on the tools used by the surrounding application.
Xiaomi’s V2.6 training mixes coding, general agent work, visual tasks and cybersecurity across different environments. Its feedback process compares task trajectories and uses both test outcomes and quality criteria, aiming to develop useful strategies across extended interactions. Prepare a brief that states the goal, the available evidence and what will count as completion. Faster generation is useful when it advances those interactions; it does not replace checking whether each step answers the question or changes the intended artifact.
For a coding assistant, provide the affected files, the requested behavior and the failure or check output that motivates the work. Ask for the next focused change, then return the actual result from any execution tools. A useful final response explains the behavior changed, the files affected and the checks actually performed. Keep proposed operations distinct from completed ones so a quick sequence of responses remains traceable to observable work.
The maker’s V2.6 examples extend coding to games, 3D objects and interactive scenes, with visual feedback used to refine their implementation. For a design task, combine a text brief with reference images or a recording and specify the required controls, layout and visible completion criteria. A scene or interface proposal can then guide the appropriate production tools. Retain the rendered result and its inspection findings rather than treating the initial code or scene instructions as a finished visual artifact.
For presentations and video workflows, define the audience, required content, intended sequence and assets that must remain consistent. Xiaomi describes the Pro family working with design, image, video and narration tools; those are supporting workflows rather than additional native media outputs from the text model. The useful deliverable can be a slide structure, shot sequence or production instruction set with enough detail to carry out and inspect the next step.
For research, supply the question and authoritative material, and ask the model to distinguish facts, hypotheses and calculations. Xiaomi’s Pro examples include literature and patent review, computational materials screening, music scores and MIDI, and mathematical formalization checked with Lean. Preserve the outputs of the actual research or verification tools. A simulated candidate or proposed proof should remain identified as such until the corresponding physical experiment or formal check has been completed.
Keep the Pro-UltraSpeed identity with the result when comparing it with ordinary Pro or Flash. Organize long histories around the goal and the decisions that must remain stable, and leave room for the response and continuing tool feedback. Review important code, observations, calculations and claims against their source evidence. The distinguishing purpose of this mode is quicker Pro generation; the completed artifact still needs to satisfy the same task-specific criteria.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits xiaomi/mimo-v2.6-pro-ultraspeed | 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 $0.0087 / 1K tokens Output · Output tokens $0.0174 / 1K tokens Input · Cache read $0.000072 / 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 $0.0087 / 1K tokens Output · Output tokens $0.0174 / 1K tokens Input · Cache read $0.000072 / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "xiaomi/mimo-v2.6-pro-ultraspeed",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${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': 'xiaomi/mimo-v2.6-pro-ultraspeed', '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': 'xiaomi/mimo-v2.6-pro-ultraspeed', '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 xiaomi/mimo-v2.6-pro-ultraspeed
Use the method paths shown above.
Technical example in English
Revise a failing total calculation\n\nUse this code and reported check: def total(rows): return sum(row["price"] for row in rows). Input is [{"price": 3, "qty": 2}, {"price": 5, "qty": 1}]; reported output is 8, required output is 11. Propose the next focused change and checks for multiple quantities and an empty input. Do not claim to run them.A focused Python correction with a diagnosis and two proposed checks tied to the quantity requirement.Verify each unit price is multiplied by its row quantity and proposed checks remain distinct from executed results.