Source: https://ailoot.ai/models/en/xiaomimimo-mimo-v2-flash/ Canonical: https://ailoot.ai/models/en/xiaomimimo-mimo-v2-flash/ Language: en # MiMo v2 Flash [Markdown](https://ailoot.ai/models/en/xiaomimimo-mimo-v2-flash/index.md) · [JSON](https://ailoot.ai/models/en/xiaomimimo-mimo-v2-flash/facts.json) ## [MiMo v2 Flash](https://ailoot.ai/models/en/xiaomimimo-mimo-v2-flash/) MiMo-V2-Flash is Xiaomi’s text model for reasoning, coding and tool-assisted agent work. Its 256K context, hybrid attention and distinct post-trained release suit tasks that connect substantial text material with a specific answer, code change or next action. - [**Ailoot Ultra · OpenAI Chat** `POST /v1/chat/completions` `xiaomimimo/mimo-v2-flash`](https://ailoot.ai/models/en/xiaomimimo-mimo-v2-flash/#method-0) - [**Ailoot Ultra · Anthropic Messages** `POST /v1/messages` `xiaomimimo/mimo-v2-flash`](https://ailoot.ai/models/en/xiaomimimo-mimo-v2-flash/#method-1) About the model This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below. Model profile and editions MiMo-V2-Flash is Xiaomi’s language model for reasoning and agent workflows. Its mixture-of-experts architecture has 309 billion total parameters and 15 billion active parameters. The released model lists a context length of 256K tokens; the manufacturer’s inference example configures 262,144 tokens. It receives text and produces text, including explanations, code and the information needed for tool interactions. Flash-Base and the post-trained Flash release are separately named editions with different roles. Hybrid attention and prediction The architecture combines sliding-window and global attention in a 5:1 ratio. The local attention window is 128 tokens, while global attention maintains connections across the longer material. Pre-training uses a native sequence length of 32K, and the released model supports the longer 256K context. A lightweight multi-token prediction module is integrated into training and inference. These are model-design properties; they do not specify how quickly a particular application will return an answer. Post-training for reasoning and agents MiMo-V2-Flash uses Multi-Teacher On-Policy Distillation, with domain-specific teacher models guiding its own generated responses, followed by large-scale agentic reinforcement learning. The training covers code-agent environments and extends to mathematics and general agent tasks. A vision-based verifier is used during web-development training to evaluate executed results. That verifier is part of the training process and does not turn this text model into an image- or video-input model. Coding and document preparation For a coding task, provide the relevant files as text, the observed failure, an input that demonstrates it and the behavior that must remain intact. An illustrative request can ask for a small patch, an explanation of affected dependencies and checks for the expected result. For long text material, identify the sections that matter and ask for a comparison tied to their passages. Keep the output concrete: corrected code, a table of discrepancies, a calculation with assumptions or a proposed next action. Thinking, tools and conversation history The published thinking example enables a reasoning mode, and the manufacturer specifies a dedicated tool-call parser. In multi-turn tool use, the response may include reasoning\_content alongside tool\_calls. The next request must retain the earlier reasoning\_content in the conversation history so the thinking-and-tool sequence can continue correctly. The application supplies and executes the tools; the model chooses how to use their results. This makes history preparation a material integration requirement, rather than treating the last user message as the complete task. Manufacturer recommendations and limits The usage guidance recommends top\_p of 0.95, temperature 0.8 for mathematics, writing and web-development tasks, and temperature 0.3 for agentic tasks. It also recommends a system message identifying MiMo as Xiaomi’s assistant, with the current date and a December 2024 knowledge cutoff. Supply recent facts when the task depends on newer events. The published request’s 4096-token answer budget is an example setting, not a declared maximum output length. MiMo-V2-Flash keeps its own identity alongside Flash-Base, Pro and Omni; their context or modality specifications should be selected with their respective edition. **Ailoot Ultra · OpenAI Chat** `POST /v1/chat/completions` `xiaomimimo/mimo-v2-flash` [`xiaomimimo/mimo-v2-flash`](https://ailoot.ai/models/en/#m=xiaomimimo%2Fmimo-v2-flash&route=Ultra&caller=xiaomimimo%2Fmimo-v2-flash&fmt=chat) Other /`xiaomimimo/mimo-v2-flash` ### Overview MiMo-V2-Flash is Xiaomi’s text model for reasoning, coding and tool-assisted agent work. Its 256K context, hybrid attention and distinct post-trained release suit tasks that connect substantial text material with a specific answer, code change or next action. Accepted inputs Text Results Text ### Available in Ailoot · inputs & methods **Ailoot Ultra · OpenAI Chat** `POST /v1/chat/completions` Input types `text` Output types `text` Output format json or SSE | Field | Type | Requirement | Values & limits | Default | | --- | --- | --- | --- | --- | | Field`model` | Type`string` | RequirementRequired | Values & limits xiaomimimo/mimo-v2-flash | Default— | | Field`messages` | Type`message[]` | RequirementRequired | Values & limits Minimum items: 1 | Default— | | Field`messages[].role` | Type`string` | RequirementRequired | Values & limits Model-specific | Default— | | Field`messages[].content` | Type`string` | RequirementOptional | Values & limits Applies when: Text conversation | Default— | | Field`stream` | Type`boolean` | RequirementOptional | Values & limits | Defaultfalse | Input material combinations **Text** · UTF8 text `messages[].content` Method parameters and lifecycle | Parameter | Contract | | --- | --- | | Parameter`content_type` | Contractapplication/json | | Parameter`required` | Contractmodel, messages | | Parameter`material_types` | Contracttext | | Parameter`messages` | Contractmessage array with role/content shape | | Parameter`file_material` | Contractactual UTF-8 document inserted as literal text; no file ID inferred | | Parameter`optional_controls` | Contractstream | | Parameter`control_scope` | ContractText conversation; stream is a boolean and defaults to false. | | Parameter`output_modalities` | Contracttext | | Parameter`hosted_operations` | Contractfalse | | Parameter`multimodal_input_binding` | Contractnull | Method aliases: `/chat/completions` 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. ### Pricing Tariff references do not add request options. Supported parameters are listed for the selected method. Fast mode Not specified When fast mode is selected Flex mode Not specified When flex mode is selected Maximum reasoning effort Not specified When maximum reasoning effort uses the default adjustment #### Ailoot rates #### Prices by configuration | Configuration | Rate schedule | Charge | | --- | --- | --- | | ConfigurationBase token rates | Rate scheduleBase rate | Charge Input · Input tokens **$0.0002** / 1K tokens Output · Output tokens **$0.0006** / 1K tokens Input · Cache read **$0** / 1K tokens Input · Cache write **$0** / 1K tokens Input · Cache write, 1 hour **—** / 1K tokens | Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. [How reservation and settlement work →](https://ailoot.ai/docs/en/billing/reserve/) ### Example requests #### Ailoot Ultra · OpenAI Chat curlPython SDK ```bash set -eu : "${AILOOT_API_KEY:?Set your Ailoot API key}" python3 - <<'PY_REQUEST' import json,os from pathlib import Path p=json.loads('{"model":"xiaomimimo/mimo-v2-flash","messages":[{"role":"user","content":"hello"}],"stream":false}') Path("request.json").write_text(json.dumps(p,ensure_ascii=False)) PY_REQUEST curl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/chat/completions \ -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \ --data-binary @request.json -D response-headers.txt -o result.json python3 - <<'PY_RESULT' import json from pathlib import Path r=json.loads(Path("result.json").read_text()) for x in r.get("choices",[]): if x.get("message",{}).get("content"): print(x["message"]["content"]) PY_RESULT ``` ```python from openai import OpenAI import os if not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request") client=OpenAI(base_url="https://api.ailoot.ai/v1",api_key=os.environ["AILOOT_API_KEY"],max_retries=0) result=client.chat.completions.create(model="xiaomimimo/mimo-v2-flash", messages=[{"role":"user","content":"hello"}], stream=False) import base64, json from pathlib import Path def save_result(data, stem="result"): # Keep every field, including usage, IDs, reasoning and tool requests. Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8") print(json.dumps(data, ensure_ascii=False, indent=2)) def save_blob(encoded, suffix, label): raw = base64.b64decode(encoded, altchars=b"-_", validate=True) 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 file = Path(stem + "-" + label + "." + ext) file.write_bytes(raw) print("Saved", file) for index, block in enumerate(data.get("content") or []): print("Content", index, json.dumps(block, ensure_ascii=False)) for choice in data.get("choices") or []: print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False)) for index, item in enumerate(data.get("output") or []): print("Output", index, json.dumps(item, ensure_ascii=False)) if item.get("type") == "image_generation_call" and item.get("result"): save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index)) for ci, candidate in enumerate(data.get("candidates") or []): for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []): print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False)) blob = part.get("inlineData") or part.get("inline_data") if blob and blob.get("data"): mime = blob.get("mimeType") or blob.get("mime_type") suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin") save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi)) save_result(result.to_dict(mode="json")) ``` ### Receive and save results #### Ailoot Ultra · OpenAI Chat json or SSE ##### Keep all completed results 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. ##### If the original response needs checking Keep request/response IDs and headers. No generic text job retrieval URL is supplied. Response fields | Field | Type | | --- | --- | | Field`choices[].index` | Typeinteger | | Field`choices[].message.content` | Typestring\|null | | Field`choices[].message.tool_calls` | Typetool\_call\[\] | | Field`choices[].message.reasoning_content` | Typestring | | Field`choices[].message.reasoning_details` | Typeobject\|array | | Field`choices[].finish_reason` | Typestring\|null | | Field`usage` | Typeobject | Errors and next steps | 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. | ### Aliases #### Model aliases **Ailoot Ultra** [`xiaomimimo/mimo-v2-flash`](https://ailoot.ai/models/en/#m=xiaomimimo%2Fmimo-v2-flash&route=Ultra&caller=xiaomimimo%2Fmimo-v2-flash&fmt=chat) #### Method path aliases `/chat/completions` #### Related caller IDs [`xiaomimimo/mimo-v2-flash`](https://ailoot.ai/models/en/#m=xiaomimimo%2Fmimo-v2-flash&route=Ultra&caller=xiaomimimo%2Fmimo-v2-flash&fmt=chat) **Ailoot Ultra · Anthropic Messages** `POST /v1/messages` `xiaomimimo/mimo-v2-flash` [`xiaomimimo/mimo-v2-flash`](https://ailoot.ai/models/en/#m=xiaomimimo%2Fmimo-v2-flash&route=Ultra&caller=xiaomimimo%2Fmimo-v2-flash&fmt=messages) Other /`xiaomimimo/mimo-v2-flash` ### Overview MiMo-V2-Flash is Xiaomi’s text model for reasoning, coding and tool-assisted agent work. Its 256K context, hybrid attention and distinct post-trained release suit tasks that connect substantial text material with a specific answer, code change or next action. Accepted inputs Text Results json · sse ### Available in Ailoot · inputs & methods **Ailoot Ultra · Anthropic Messages** `POST /v1/messages` Input types `text` Output types `json`, `sse` Output format JSON or SSE | Field | Type | Requirement | Values & limits | Default | | --- | --- | --- | --- | --- | | Field`model` | Type`string` | RequirementRequired | Values & limits xiaomimimo/mimo-v2-flash | Default— | | Field`messages` | Type`message[]` | RequirementRequired | Values & limits | Default— | | Field`messages[].role` | Type`string` | RequirementRequired | Values & limits user, assistant | Default— | | Field`messages[].content` | Type`string` | RequirementRequired | Values & limits | Default— | | Field`max_tokens` | Type`integer` | RequirementRequired | Values & limits | Default— | | Field`stream` | Type`boolean` | RequirementOptional | Values & limits | Defaultfalse | Input material combinations **Text** · UTF-8 text `messages[].content string` Method parameters and lifecycle | Parameter | Contract | | --- | --- | | Parameter`content_type` | Contractapplication/json | | Parameter`required` | Contractmodel, messages, max\_tokens | | Parameter`optional_controls` | Contractstream | | Parameter`material_types` | Contracttext | | Parameter`text_file_material` | ContractRead the actual UTF-8 document and put its text in the indicated input or message content string. | Lifecycle: `synchronous_or_streamed` ### Pricing Tariff references do not add request options. Supported parameters are listed for the selected method. Fast mode Not specified When fast mode is selected Flex mode Not specified When flex mode is selected Maximum reasoning effort Not specified When maximum reasoning effort uses the default adjustment #### Ailoot rates #### Prices by configuration | Configuration | Rate schedule | Charge | | --- | --- | --- | | ConfigurationBase token rates | Rate scheduleBase rate | Charge Input · Input tokens **$0.0002** / 1K tokens Output · Output tokens **$0.0006** / 1K tokens Input · Cache read **$0** / 1K tokens Input · Cache write **$0** / 1K tokens Input · Cache write, 1 hour **—** / 1K tokens | Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. [How reservation and settlement work →](https://ailoot.ai/docs/en/billing/reserve/) ### Example requests #### Ailoot Ultra · Anthropic Messages Complete minimal JSON bodycurl · use your UTF-8 documentPython · save the complete result ```json { "model": "xiaomimimo/mimo-v2-flash", "max_tokens": 128, "messages": [ { "role": "user", "content": "Summarize the incident report." } ], "stream": false } ``` ```bash set -eu : "${AILOOT_API_KEY:?Set your Ailoot API key}" : "${AILOOT_TEXT_FILE:?Set your actual UTF-8 document}" export AILOOT_TEXT_FILE python3 - <<'PY_REQUEST' import json, os from pathlib import Path source_file = Path(os.environ['AILOOT_TEXT_FILE']) if not source_file.is_file(): raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document') text = source_file.read_text(encoding='utf-8') if not text.strip(): raise SystemExit('Choose a document containing text') payload = {'model': 'xiaomimimo/mimo-v2-flash', 'max_tokens': 128, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]} payload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\n\n' + text Path('request.json').write_text(json.dumps(payload, ensure_ascii=False), encoding='utf-8') PY_REQUEST curl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/messages \ -H "x-api-key: $AILOOT_API_KEY" -H 'anthropic-version: 2023-06-01' -H 'Content-Type: application/json' \ --data-binary @request.json -D response-headers.txt -o result.json python3 - <<'PY_RESULT' import json from pathlib import Path result = json.loads(Path('result.json').read_text(encoding='utf-8')) print(json.dumps(result, ensure_ascii=False, indent=2)) PY_RESULT ``` ```python import json, os from pathlib import Path source_file = Path(os.environ['AILOOT_TEXT_FILE']) if not source_file.is_file(): raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document') text = source_file.read_text(encoding='utf-8') if not text.strip(): raise SystemExit('Choose a document containing text') payload = {'model': 'xiaomimimo/mimo-v2-flash', 'max_tokens': 128, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]} payload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\n\n' + text from urllib.request import Request, urlopen from urllib.error import HTTPError api_key = os.environ['AILOOT_API_KEY'] if not api_key.strip(): raise SystemExit('Set AILOOT_API_KEY before making a request') headers = {'x-api-key': 'PLACEHOLDER_KEY', 'anthropic-version': '2023-06-01', 'Content-Type': 'application/json'} headers['x-api-key'] = api_key request = Request('https://api.ailoot.ai/v1/messages', data=json.dumps(payload, ensure_ascii=False).encode('utf-8'), headers=headers, method='POST') try: with urlopen(request, timeout=120) as response: raw = response.read() Path('response-headers.txt').write_text(str(response.headers), encoding='utf-8') except HTTPError as error: Path('result.json').write_bytes(error.read()) Path('response-headers.txt').write_text(str(error.headers), encoding='utf-8') raise Path('result.json').write_bytes(raw) import json from pathlib import Path result = json.loads(Path('result.json').read_text(encoding='utf-8')) print(json.dumps(result, ensure_ascii=False, indent=2)) ``` ### Receive and save results #### Ailoot Ultra · Anthropic Messages JSON or SSE ##### Keep all completed results 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. ##### If the original response needs checking Keep request/response IDs and headers. No generic text job retrieval URL is supplied. Response fields | Field | Type | | --- | --- | | Field`id` | Typestring | | Field`model` | Typestring | | Field`content[]` | Typetext\|thinking\|tool\_use\|other content block | | Field`stop_reason` | Typestring\|null | | Field`usage` | Typeobject | Errors and next steps | 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. | ### Aliases #### Model aliases **Ailoot Ultra** [`xiaomimimo/mimo-v2-flash`](https://ailoot.ai/models/en/#m=xiaomimimo%2Fmimo-v2-flash&route=Ultra&caller=xiaomimimo%2Fmimo-v2-flash&fmt=messages) #### Method path aliases `/chat/completions` #### Related caller IDs [`xiaomimimo/mimo-v2-flash`](https://ailoot.ai/models/en/#m=xiaomimimo%2Fmimo-v2-flash&route=Ultra&caller=xiaomimimo%2Fmimo-v2-flash&fmt=messages) FAQ · xiaomimimo/mimo-v2-flash What inputs and results are available? `POST /v1/chat/completions` · `xiaomimimo/mimo-v2-flash`: `text` → `text` `POST /v1/messages` · `xiaomimimo/mimo-v2-flash`: `text` → `json`, `sse` Which fields does the request use? `POST /v1/chat/completions` · `xiaomimimo/mimo-v2-flash`: `model`, `messages`, `stream` `POST /v1/messages` · `xiaomimimo/mimo-v2-flash`: `model`, `messages`, `max_tokens`, `stream` What limits apply? `POST /v1/chat/completions` · `xiaomimimo/mimo-v2-flash`: `messages.min_items`: `1` Connect Ailoot to Claude Code, OpenCode, Hermes or your own app. ## The company and its project portfolio The company and its project portfolio: millions of customers; monthly sales in the billions. ## Buy tokens at a discount. Subscription discounts for regular use. Promo codes offer up to 50% off token purchases. Subscription Choose a discounted plan for your regular requests. Promo code: up to 50% off Enter your code at checkout. The price updates to reflect the current promotion. Spending in your history The model, tokens and amount are recorded for each request. [Choose a subscription](https://ailoot.ai/docs/en/billing/subscriptions/) · [View model prices](https://ailoot.ai/models/en/) ## Connect the API to a product you sell. Use Ailoot in SaaS, apps and bots under its official service offer and licence agreement. Request a reseller licence for resale. Commercial application Add text, image, video and audio generation to paid features in your service. Reseller licence Send a description of your project and how you provide access. Request written terms for use and resale. Documents on the website The service offer, licence agreement and data processing policy are available before you connect. [Request a reseller licence](mailto:support@ailoot.ai) · [Service offer and licence agreement](https://ailoot.ai/legal/offer?lang=en) ## Ailoot · The company and its project portfolio Ailoot combines established operating experience, a large client base, stable service and ongoing KPI control. The company and its project portfolio: millions of customers; monthly sales in the billions. Ailoot brings that operating experience to API access for AI features in customer products, agents, applications and automations.