# mimo-v2.6-flash decision evidence

MiMo-V2.6-Flash is Xiaomi's MIT-licensed open-weight mixture-of-experts model (309B total and 15B active per the model card) with a 1M-token context window and text, image, video and audio input. Reviewed results are split: one-prompt builds rate its output highly at very low cost, but reviewers report long thinking times and stalls on longer agentic work, including an engineer's git task that went badly off course.

Verified: 2026-09-24; snapshot: 2026-09-25.

## Published model card

As the vendor publishes it for the model and its weights; these are not the limits of a hosted endpoint.

- Context window: 1M tokens (1,048,576)
- Maximum output: 128K tokens (131,072, including reasoning tokens)
- Modalities: Text, image, video and audio input; text output
- Parameter count: 309B total, 15B active per the model card; the technical report states 310B total

## Open weights and deployment

Status: Available. Xiaomi published the MiMo-V2.6-Flash-RL weights, which need custom code, under the MIT License declared in the model card, with SGLang and vLLM launch instructions. Xiaomi's pages do not say in so many words that this checkpoint is the one behind the mimo-v2.6-flash API.

### Agent deployment prompt

You are my deployment agent. Deploy the official XiaomiMiMo/MiMo-V2.6-Flash-RL weights as a private, OpenAI-compatible service on this host. Use the publisher's model card as the source of truth: https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL.

Preflight
- Inspect the OS, GPU model and count, free VRAM, driver and CUDA versions, free disk space, and available ports. The model card's launch commands use tensor parallelism but state no minimum GPU; estimate whether the official weights and the requested context fit with safe headroom.
- The repository ships custom model code that needs trust-remote-code. Pin the exact revision you download, list the Python files it would execute, and ask me to approve them before enabling trust-remote-code.
- If the host cannot safely run the official weights, stop before installing or launching anything. Explain the limiting resource and propose a separately reviewed alternative; do not substitute a community quantization automatically.

Deployment
- If preflight passes and I have approved the remote code, create an isolated environment and follow the model card's SGLang or vLLM launch instructions, including its reasoning-parser and tool-call-parser settings. The model card warns that a stable vLLM release may lag behind; use the image or version it names rather than an untested one. Generate an API key and store it in a protected environment variable. Bind to 127.0.0.1 first; do not expose the endpoint publicly.
- Keep the publisher's sampling defaults (temperature 1.0, top_p 0.95). Start with a context length the preflight supports; the model supports up to 1,048,576 tokens.

Verification and handoff
- Send a real request to /v1/chat/completions and confirm that the model responds. Report the exact launch command, installed versions, model revision, local endpoint, test result, measured GPU memory and log location. Identify any reverse-proxy or firewall changes that still need my approval. Never print the API key or other secrets.

- Repository: https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL
- Licence: MIT (declared in the model card metadata)
- Recommended runtime: SGLang or vLLM
- Hardware note: Not published by Xiaomi beyond the tensor-parallel launch settings in the model card; the Agent recipe performs a hardware preflight.

## Real-task reports

### [MiMo-V2.6-Flash, DeepSeek V4.1 Flash and Grok 4.7 on one game prompt](https://x.com/naymur_dev/status/2102456037286801665)

By [Naymur Rahman](https://x.com/naymur_dev) (@naymur_dev). Frontend developer whose X profile lists design-and-code work for Command Code

Published: 2026-09-22; retrieved: 2026-09-24; verified: 2026-09-24.

- Task: Build a playable browser game from a single prompt with Command Code's /design command
- Method: Same prompt for all three models; the author rated gameplay UX/UI out of 10 and reported the cost of each run.
- Finding: MiMo-V2.6-Flash scored 9/10 for $0.005, the cheapest run, with a playable one-shot game. DeepSeek V4.1 Flash also scored 9/10 at $0.0089; Grok 4.7 scored 8/10 at $0.20 and needed iterations.
- Limitation: One prompt rated by the author, whose profile lists work for Command Code, the tool that ran the test; there is no rubric and no repeated run.
- Author rating: 9/10
- Run cost: $0.005

### [A senior engineer's security and git tasks with MiMo-V2.6 Pro and Flash](https://www.reddit.com/r/LocalLLaMA/comments/1woa5d3/mimov26_both_pro_and_flash_is_a_benchmaxxed_scam/)

By [crusaderky](https://www.reddit.com/user/crusaderky/) (u/crusaderky). r/LocalLLaMA member who presents the post as a senior software engineer's impressions

Published: 2026-09-23; retrieved: 2026-09-24; verified: 2026-09-24.

- Task: Create a branch and cherry-pick one commit in a sandbox whose git worktree turned out to be corrupted
- Method: Self-reported impression that the author says is not a benchmark; the same post gives MiMo-V2.6-Pro a sandbox security task and has GLM-5.3 review the result.
- Finding: MiMo-V2.6-Flash spent about 80k tokens on risky workarounds such as tampering with /tmp and mount --bind instead of diagnosing or recovering the worktree; the author concludes it falls well short of its benchmark placement and stays with DeepSeek V4.1 Flash and Qwen3.8-Flash.
- Limitation: One task per model, with the serving provider, quantisation and settings not stated; the post's title calls the series benchmaxxed.
- Tokens spent on the detour: ~80k

### [Three Flash models build the same koi pond locally](https://x.com/WescheNex1q/status/2102527793057661386)

By [Wësche](https://x.com/WescheNex1q) (@WescheNex1q). Artist and AI enthusiast on X who builds and benchmarks open models on DGX Spark clusters

Published: 2026-09-22; retrieved: 2026-09-24; verified: 2026-09-24.

- Task: A koi pond build, run as a local head-to-head
- Method: The same task on GLM-5.3-Flash, MiMo-V2.6-Flash and DeepSeek V4.1 Flash, each on two DGX Sparks; the author reported time, tokens and fixes needed and left the visual verdict partly to readers.
- Finding: All three finished cleanly with zero fixes; MiMo-V2.6-Flash took 30 minutes and 75.5K tokens, between DeepSeek V4.1 Flash (21 minutes, 53.3K) and GLM-5.3-Flash (59 minutes, 90.9K).
- Limitation: One local run per model; the post states neither the prompt nor the quantisation or runtime used.
- Time: 30 min
- Tokens: 75.5K

### [MiMo-V2.6-Flash on page and SVG tasks and a real app bug fix](https://www.youtube.com/watch?v=LTHLzLotZsU)

By [抡锤者](https://www.youtube.com/@%E6%8A%A1%E9%94%A4%E8%80%85) (@抡锤者). Chinese YouTube channel that tests models on real tasks and posts full transcripts on its own forum

Published: 2026-09-22; retrieved: 2026-09-24; verified: 2026-09-24.

- Task: Build pages explaining Chinese bagua culture and how a thorium molten-salt reactor works, draw Schrödinger's cat as an SVG, and fix a real bug in the author's app
- Method: Run through OpenRouter, both inside the DeepSeek harness and in direct chat; the author scored the outputs out of 100 and compared speed with earlier DeepSeek V4.1 Flash runs of the same tasks. The full transcript is at https://lcz.me/topic/1885.
- Finding: The pages and SVGs were the best the author had seen, scoring 85–95 (the bagua page 95, against about 90 for DeepSeek's page in an earlier test), and all the tasks together cost about $0.50; but the bagua page took at least five times as long as DeepSeek's, the first attempt at it failed after 10–20 minutes, and a simple app bug fix stalled for an hour on tool-call errors.
- Limitation: One author's scores on a handful of tasks; the DeepSeek comparison rests on the author's earlier runs and estimates, and the author expects Xiaomi may fix the speed and tool-call problems.
- Bagua page (author): 95/100
- App bug fix: Stalled after 1 hour
- Total cost: About $0.50

### [A MiMo-V2.6-Flash game build that needed steering](https://x.com/SimonasLTU1/status/2102304299803889801)

By [Simonas](https://x.com/SimonasLTU1) (@SimonasLTU1). X user who builds in public and posts hands-on model tests

Published: 2026-09-22; retrieved: 2026-09-24; verified: 2026-09-24.

- Task: A browser game build the author meant to run zero-shot
- Method: The author ran the task in their own coding setup (OpenCode or ZCode, per a reply), steered the model when it stalled, and reported thinking time, cost and a personal score.
- Finding: The model thought for more than 11 minutes and got stuck in an overthinking loop while writing the file, so the author had to steer it several times; the result was solid, cost about $0.05, and scored 9.5/10 from the author, who said it did not feel like a Flash model for speed.
- Limitation: One task with several manual interventions, which the author says means it may not reflect what the model would produce on its own; the score is the author's.
- Thinking time: Over 11 min
- Cost: About $0.05
- Score (author): 9.5/10

### [kzhu's six-question LLM test leaderboard](https://www.techgogogo.com/llm-benchmark/)

By [AI产品狙击手 kzhu](https://www.youtube.com/@kevinzhu9305) (@kevinzhu9305). Chinese YouTube reviewer who scores models on a six-question test and publishes every score with per-question notes

Published: 2026-09-24; retrieved: 2026-09-24; verified: 2026-09-24.

- Task: Six questions scored out of 10: constrained sentence writing, two reasoning puzzles (replaced by pelican and compound-bow SVG animations in version 2.2), a browser OS with a space shooter, a 3D racing game and a Trello-style board
- Method: Each row names the harness the model ran in (Pi, Claude Code, WorkBuddy and others) and the date; the author scores every question with a note, and warns that totals are only comparable within one test version.
- Finding: MiMo-V2.6-Flash scored 40/60 on version 2.2 in Pi, well above the Pro model's 28 in the same batch: only six of ten constrained sentences were right, but its compound bow was the most realistic the author had seen and its 3D racing game was rated top-notch (8).
- Limitation: One author's scores on six questions; rows use different harnesses and test versions (2.1 or 2.2), and a few rows' table total differs by one point from the author's note.
- Score: 40 / 60 (v2.2, Pi)

## Public leaderboard coverage

A board missing from this list has no figure for this model in this snapshot; missing coverage is not a zero.

- LMArena Text Arena — Arena score (Elo): 1452 ±8 (retrieved 2026-10-05) [source](https://arena.ai/leaderboard/text)
- Vals Index — Accuracy (GDP-weighted finance, coding, legal and tax): 54.83 ±1.11 (retrieved 2026-10-23) [source](https://www.vals.ai/benchmarks/vals_index)
- LMArena Text Arena · Chinese — Arena score (Elo): 1502 ±27 (retrieved 2026-10-05) [source](https://arena.ai/leaderboard/text/chinese)
- LMArena Text Arena · Coding — Arena score (Elo): 1521 ±15 (retrieved 2026-10-05) [source](https://arena.ai/leaderboard/text/coding)

## Curated comparisons

- [deepseek-flash vs mimo-v2.6-flash](https://dash.chinaapi.ai/models/compare/deepseek-flash-vs-mimo-v2.6-flash)
- [glm-5.3-flash vs mimo-v2.6-flash](https://dash.chinaapi.ai/models/compare/glm-5.3-flash-vs-mimo-v2.6-flash)

## Official sources

- [MiMo-V2.6: Scaling Up Reinforcement Learning for Self-Improvement](https://mimo.mi.com/docs/en-US/news/latest/v2-6) — Xiaomi MiMo, retrieved 2026-09-24
- [MiMo-V2.6-Flash model page](https://mimo.mi.com/models/en-US/mimo-v2.6-flash) — Xiaomi MiMo, retrieved 2026-09-24
- [MiMo-V2.6-Flash-RL model card](https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL) — Xiaomi MiMo, retrieved 2026-09-24