MiMo v2.6 Pro

Confidence: medium — The sample of 10 posts and 286 comments (all-time window) provides a decent volume, but the discussions often concern the whole V2.6 family (Flash, the 9B distill), not just Pro; much of the material is meta-discussions about AA benchmarks. Pro-specific real-world feedback is limited to one detailed negative test and a few positive mentions, and the missing data is for multimodality.

Category scores

Intelligence

72

The highest-rated open-weight model on Artificial Analysis (46 points, 'the best open model') and 'not much behind frontier'; some users believe it has 'sota data and training, so it is as intelligent as other open models'. The counterweight is real-world use: a security task with a trivial bypass and a user's own benchmark (260 questions) on which V2.6 did worse than V2.5.

Speed

62

Praised over API: 'It is really fast too', 'easier to swallow when each round trip feels instant'. Locally clearly slower than the competition: ~76 t/s vs ~290 t/s for GLM 5.3 Flash on the same hardware, a drop past ~100k of context (30-40 t/s at 250k); on M5U 61-90 t/s rated as 'meh, but usable'.

Coding quality

58

Simple and medium tasks worked well ('two fairly complex C++ tasks... worked just fine', 'crushes it'), but in the detailed security test the code had 9 security holes and a trivial bypass, some users confirm 'benchmaxxed for coding', and in direct comparisons DS4 Flash won the same tasks; one user rated V2.6 below V2.5.

Instruction following

62

Pros: the model asks follow-up questions about unclear edge cases on its own ('It stopped to ask me questions as it spotted unclear corner cases') and understands minimal instructions well. Cons: without a filled-in project context it can fall into a shell-command busyloop, at effort=max it 'thinks to infinity', and users report it doing things that were explicitly forbidden.

Long context

58

1M context actually works locally (tests at 5x1M), but the cost is high: the KV cache is ~25x more memory-hungry than Deepseek at the same context, and throughput clearly drops past ~100k (from 76 to 30-40 t/s at 250k).

Price / value

78

The strongest side in the sample: 'It also costs pennies', 'costs less than half on datacenter'; even at ~10x the token usage of a competitor a user concluded 'but still cheaper'. Even quality critics appreciate the price.

Reliability & uptime

48

Repeated reports of instability: hundreds of shell commands in a busyloop with a poorly described project, thought loops (9B distill: 'doom loop'), occasionally invalid tool-call syntax on the plain API, broken out-of-the-box templates (empty responses on vLLM, a hidden 2048-token output limit) — though some of the problems are serving errors, not the model itself.

Creativity & writing

70

Praised persona and tone: 'tone of voice is excellent', 'nice persona', 'mimo has a more connectedness smartness', the model 'excels at creative tasks, roleplay'. Caveat: 'heavily trained on code. I wish it would've been trained a bit more on general / chat data'.

Multimodal & vision

no data

No data in the sample — users only mention the native image/video/audio coders in the Flash variant; there are no vision-quality ratings for Pro at all.

Agentic & tool use

55

After the serving fixes it works better than GLM 5.3 as an agent backend ('yes, better than GLM5.3'), and the 9B distill 'can use tools reliably' with the right template; but users also report invalid syntax in tool calls, busyloops, and the opinion that the model 'doesn't seem particularly strong at coding, tool calling, or orchestration'.

Summary

Pros and cons

Top quotes

• “Might be contrarian but I actually kind of prefer mimo over deepseek. It has a nice persona. Deepseek is super smart and focused whereas mimo has a more "connectedness" smartness.”

• “Ran not quantized version of MiMo-V2.6-Flash on my spark cluster - can confirm it was benchmaxxed for coding tasks for sure.”

• “Wow, this is worthless. You ran one prompt and deemed it a scam. What are you trying to accomplish with this post?”

• “In 3 minutes, it found **NINE** glaring security holes that allow bypassing git and gh restrictions.”

• “I don't agree at all. I tried it yesterday with two fairly complex C++ tasks (not benchmarks, just stuff I needed), and it worked just fine.”

• “There's some sort of tipping point though where it just falls apart in terms of task complexity or prompt length or prompt wording”

• “I expected a major jump in performance compared to the MiMo-V2.5 series. No, it's worse. I am very disappointed because I like the MiMo models a lot”

• “fill its context up to 10% with how the project is and where it is in the pipeline, it will literally perform hundreds of shell commands in a busyloop”

• “GLM maintains over 200t/s at 900k context. Mimo starts to drop off at 100k with MTP enabled. At 250k its down into the 30's. I had to turn off MTP entirely to get a solid ~76t/s all the way to 1m.”

• “Although it makes mistakes and use more tokens, but after some reprompting , It(max) gives really good outputs like on par with 5.6 sol and close to astra High in one task..”

• “Mimo probably has sota data and training, so it is as intelligent as other open models.”

• “yes, better than GLM5.3, but with thinking effort = max it thinks to infinity.”

Top sample posts

Top 5 of 10 posts by score

• Xiaomi MiMo 2.6 Live Training Dashboard ↗

  skeole · 454 points · 82 comments

• MiMo-V2.6 distilled themselves into Qwen 9B! ↗

  Beamsters · 187 points · 43 comments

• Now Opus 5.5 is 58 on Artificial Analysis , how long do you have wait until an open model hits 58? ↗

  power97992 · 153 points · 122 comments

• Mimo v2.6-Flash-RL vs open-weight models ↗

  ababaka · 140 points · 44 comments

• MiMo-V2.6 (both Pro and Flash) is a benchmaxxed scam ↗

  crusaderky · 87 points · 85 comments

FAQ

What is MiMo v2.6 Pro's overall community score?

63 out of 100, with medium confidence: The sample of 10 posts and 286 comments (all-time window) provides a decent volume, but the discussions often concern the whole V2.6 family (Flash, the 9B distill), not just Pro; much of the material is meta-discussions about AA benchmarks. Pro-specific real-world feedback is limited to one detailed negative test and a few positive mentions, and the missing data is for multimodality.

How was the MiMo v2.6 Pro benchmark collected?

From 10 posts and 286 comments on r/LocalLLaMA sampled over the all time window.

Which categories does the MiMo v2.6 Pro report score?

Intelligence, Speed, Coding quality, Instruction following, Long context, Price / value, Reliability & uptime, Creativity & writing, Multimodal & vision, Agentic & tool use.

What are the main community pros and cons for MiMo v2.6 Pro?

Top pros: Best result among open-weight models on Artificial Analysis (46 points) at a very low price ('costs pennies', 'still cheaper' despite 10x the tokens); Fast API and good responsiveness ('It is really fast too'). Main cons: Widespread 'benchmaxxing' accusations — benchmark results don't translate to the real world (9 security holes in one test, a trivial bypass); Agentic reliability problems: shell busyloops, 'thinking to infinity' at effort=max, loops during tool calling, invalid tool-call syntax on the API.

When was the MiMo v2.6 Pro data collected?

Collected on September 27, 2026; the report was generated on September 27, 2026.