Models › MiMo-V2.6 family

MiMo-V2.6-Pro-RL

XiaomiMiMo MIT out 34 days ago

1.02T parameters (8 of 384 experts per token), text in, text out, 1M tokens of context. 5 providers serve it from $0.39 per million input tokens.

Verified 2026-10-25 Rank #273 in LLMs · score 84.8 method v1 Sources 4 Downloads 30d 59.3k · #368 of 619 tracked

Changed this week: 1 new quantized build on the Hub (9 in all).2026-10-24

What does it cost?$0.39/1M incheapest of 5DeepInfrafp82026-10-24
Can I use it?MITPermissivelicence file2026-10-24
Do people use it?59.3kdownloads, 30 days#368 of 619 we track · below the median+36 likes, downloads −42% this week773 likesHugging Face2026-10-24

Worth reading

Picked by rule from 17 tracked pieces: the lab's own post, the most shared, a field report, the latest.

Latest · 28 days ago

Diagnosing and Mitigating Tool-Call Repetition in MiMo-v2.6

MiMo-V2.6 models exhibited tool-call repetition where agents issued identical calls repeatedly, wasting context and stalling tasks.

  • The flooding penalty threshold was lowered from 32 calls to eight to suppress abnormal call volume.
  • Applying the stricter flooding penalty at full scale required restarting 20 training steps at an estimated cost of $2.31 million.
mimo.xiaomi.com 2026-09-27 2 HN pts · 1 comments 1.8k words about the MiMo V2.6 family family sells inference

17 best of 17 tracked

The 2 picks above, plus these 15. Top is the radar's score: discussion, source authority and the lab's own posts, fading with age.

  1. ArticleMiMo v2.6mimo.xiaomi.com · 2026-09-21 · 1,130 HN pts · official
  2. ArticleMiMo-v2.6-Pro: Intelligence, Performance and Price Analysisartificialanalysis.ai · 2026-09-22 · 166 HN pts
  3. ArticleXiaomi MiMo v2.6 Pro: New best open-weight LLM with simple designsebastianraschka.com · 2026-09-22 · 3 HN pts
  4. ArticleMiMo-v2.6-Flash: on intelligence/price Pareto frontierartificialanalysis.ai · 2026-09-27
  5. ArticleMiMo-v2.6-Flashmimo.mi.com · 2026-09-22 · official
  6. ArticleXiaomi MiMo-v2.6 Breaks Cover: A 1T-Class Chinese Lab Trains in Publicforkast.news · 2026-09-18 · 3 HN pts
  7. DiscussionMiMo-v2.6 is "simply" the besttwitter.com · 2026-09-22 · 2 HN pts
  8. ArticleHow Xiaomi’s MiMo-V2.6-Pro RL Topped Open Weights Chartsdeeplearning.ai · undated
  9. ArticleMiMo V2.6 Pro Review: Cheapest Open-Weight Model Yetaitooldaily.net · 2026-09-29
  10. ArticleMiMo-V2.6: Scaling Reinforcement Learning from Scratch | Pratham Patelprathamp.com · 2026-09-26
  11. ArticleMiMo-V2.6: Xiaomi's RL Paper, Verified vs Reportedorcarouter.ai · 2026-09-23
  12. ArticleXiaomi MiMo V2.6-Pro Review (2026): Open RL Flagshiptoolworthy.ai · 2026-09-22
  13. ArticleMiMo-V2.6-Pro Review: 1M Context & Top Open Score 2026 | HokAIhokai.io · 2026-09-22
  14. ArticleXiaomiMiMo/MiMo-V2.6-Pro-RL · Paper Feedpaperfeed.app · 2026-09-21
  15. ArticleMiMo-V2.6-Pro Official Technical Report: Architecture, Scaled RL, and Evaluation Conditions | Tabbitgo.tabbit.ai · undated

Where to run it

Live hosted endpoints, cheapest first, USD per million tokens. Ties break on output price, then provider name.

5 providers from $0.39

5 providers with a live price, from $0.39 to $0.52 per million input tokens; the median is $0.43. One dot per provider, cheapest in teal.

$0.010$0.10$1.00$10.00DeepInfra: $0.39 in / $0.78 out per 1M · observed 2026-10-24 · https://openrouter.ai/xiaomi/mimo-v2.6-proNovita: $0.43 in / $0.87 out per 1M · observed 2026-10-25 · https://huggingface.co/api/models/XiaomiMiMo/MiMo-V2.6-Pro-RL?expand[]=inferenceProviderMappingGMICloud: $0.43 in / $0.87 out per 1M · observed 2026-10-24 · https://openrouter.ai/xiaomi/mimo-v2.6-proXiaomi: $0.43 in / $0.87 out per 1M · observed 2026-10-24 · https://openrouter.ai/xiaomi/mimo-v2.6-proStreamLake: $0.52 in / $1.04 out per 1M · observed 2026-10-24 · https://openrouter.ai/xiaomi/mimo-v2.6-procheapest $0.39median $0.43
$0.010$0.10$1.00$10.00DeepInfra: $0.39 in / $0.78 out per 1M · observed 2026-10-24 · https://openrouter.ai/xiaomi/mimo-v2.6-proNovita: $0.43 in / $0.87 out per 1M · observed 2026-10-25 · https://huggingface.co/api/models/XiaomiMiMo/MiMo-V2.6-Pro-RL?expand[]=inferenceProviderMappingGMICloud: $0.43 in / $0.87 out per 1M · observed 2026-10-24 · https://openrouter.ai/xiaomi/mimo-v2.6-proXiaomi: $0.43 in / $0.87 out per 1M · observed 2026-10-24 · https://openrouter.ai/xiaomi/mimo-v2.6-proStreamLake: $0.52 in / $1.04 out per 1M · observed 2026-10-24 · https://openrouter.ai/xiaomi/mimo-v2.6-procheapest $0.39 · median $0.43 (white tick)
#Cheapest 5In /1MOut /1MContextQuantSeen
1 DeepInfra $0.39 $0.78 1M fp8 Oct 24
2 GMICloud $0.43 $0.87 1.0M bf16 Oct 24
3 Novita $0.43 $0.87 1M fp8 Oct 25
4 Xiaomi $0.43 $0.87 1M fp8 Oct 24
5 StreamLake $0.52 $1.04 977K unknown Oct 24

All 5 providers →

Where it's used

Hugging Face does not say who downloads a model or with what. These are the proxies: API providers listing it, quantized builds, fine-tunes/adapters/merges, and Spaces that load it.

Widely served (5 API providers); 9 quantized builds for local use.

  • As a service5DeepInfra, GMICloud, Novita, StreamLake +1
  • On own hardware9quantized builds
  • Built on43 fine-tunes · 1 adapters · 0 merges
  • In apps7Hugging Face Spaces

Top Spaces running it: FineEnvs/multi-harness-rl ♥ 272 · FineEnvs/multi-harness-rl-slides ♥ 9 · quantid/measurement-lab ♥ 1

Will it fit?

Weights only, an estimate: leave room for the KV cache. Method

Needs an 8×80 GB node even at INT4

About 615 GB at INT4, 1.23 TB at FP8, 2.46 TB at BF16: weights only, so leave room for the KV cache. Every expert must be resident, so total parameters count.

16 GB card64 GB Mac2×80 GB8×80 GBINT4: about 615 GB of weights (estimate; KV cache and activations extra)INT4615 GBFP8: about 1.23 TB of weights (estimate; KV cache and activations extra)FP81.23 TBBF16: about 2.46 TB of weights (estimate; KV cache and activations extra)BF162.46 TB
16 GB card64 GB Mac2×80 GB8×80 GBINT4: about 615 GB of weights (estimate; KV cache and activations extra)INT4615 GBFP8: about 1.23 TB of weights (estimate; KV cache and activations extra)FP81.23 TBBF16: about 2.46 TB of weights (estimate; KV cache and activations extra)BF162.46 TB

Estimate params × bytes per param × 1.2 · how we estimate

The story so far

Weights, every dated article, and repo commits on one rail.

Coverage peaked at launch and is still arriving

Teal is the weights (2026-09-21), amber triangles are 9 dated articles (each links to it), grey ticks are repo commits.

What people built on it

13 derivatives on the Hub. Which build to run, by runtime: the most downloaded in each class.

13 derivatives on the Hub, most of them quantized

From the Hugging Face model tree; quantized builds in teal, fine-tunes, merges and adapters in greys.

9 quantized (Hugging Face model tree)3 fine-tunes (Hugging Face model tree)1 adapter (Hugging Face model tree)9 quantized3 fine-tunes1 adapter
9 quantized (Hugging Face model tree)3 fine-tunes (Hugging Face model tree)1 adapter (Hugging Face model tree)9 quantized3 fine-tunes1 adapter

All variants →

Reference

Everything else, folded: the full tables, every source.

Benchmarks

17 rows

Benchmarks 17 rows

measured third-party leaderboard rowauthor-reported number from the model card or eval files (17)No confidence intervals unless the source gave them; settings differ between rows, so compare within a benchmark, not across. Date = the benchmark's own date when it gives one, else when we read it.
BenchmarkValueSetting / notesReported byDate
author-reported Agents’ Last Exam31.6MiMo-V2.6 Promodel author2026-10-07
author-reported AutomationBench v1.0.653.1MiMo-V2.6 Promodel author2026-10-07
author-reported CyberGym94MiMo-V2.6 Promodel author2026-10-07
author-reported DeepSWE · Write code with agents71.9MiMo-V2.6 Promodel author2026-10-07
author-reported ExploitGym17.8MiMo-V2.6 Promodel author2026-10-07
author-reported GDPval-AA 2.11673MiMo-V2.6 Promodel author2026-10-07
author-reported JobBench62MiMo-V2.6 Promodel author2026-10-07
author-reported MiMo Code Bench63.2MiMo-V2.6 Promodel author2026-10-07
author-reported MiMo Cyber Bench80.2MiMo-V2.6 Promodel author2026-10-07
author-reported MiMo VisualCoding72.3MiMo-V2.6 Promodel author2026-10-07
author-reported OSWorld-Verified82MiMo-V2.6 Promodel author2026-10-07
author-reported ProgramBench26.5MiMo-V2.6 Promodel author2026-10-07
author-reported SEC Bench Pro66.3MiMo-V2.6 Promodel author2026-10-07
author-reported Terminal-Bench · Write code with agents34.9MiMo-V2.6 Promodel author2026-10-07
author-reported Terminal-Bench · Write code with agents89.9MiMo-V2.6 Promodel author2026-10-07
author-reported Terminal-Bench · Write code with agents47.9MiMo-V2.6 Promodel author2026-10-07
author-reported Toolathlon · Call tools and APIs76.9MiMo-V2.6 Promodel author2026-10-07

What the card says

1 cited sentence

Summary generated, cited

The architecture supports a 1M token context length and utilizes 8 experts per token.[1][2]

  1. “Context Length: 1M tokens”
  2. experts per token

Generated 2026-10-07 15:04 UTC by parasail-gemma-4-26b-a4b-it; each numeral points at the fact it rests on. Not a verdict.

Use-case evidence

6 use cases

What it is good at 6 use cases

Labels are evidence, not verdicts: measured a third-party leaderboard row for this model; author-reported a number the model author published in the card or eval files; structural a deterministic signal from the name, config or chat template, or a decision-model estimate. Each chip links to its source. All use cases.

Facts

22 fields

Listed in LLMs · MoE

Facts

Pipeline tagtext-generation
Librarytransformers
ArchitecturesMiMoV2ForCausalLM
Model typemimo_v2
Dtypebfloat16
Params by dtypeBF16: 10,647,286,656 · F32: 26,496 · F8_E4M3: 13,378,781,184 · U8: 1,000,190,509,056
Size bandXXL
MoEyes
Experts384
Experts per token8
Context length1,048,576 (config.json max_position_embeddings)
Languagesen, zh
LicenseMIT (mit)
Hub snapshot2026-10-25

Servability

vLLM · SGLang

Servability

vLLM: supported · MiMoV2ForCausalLM · 7050ef881ff0835f6890a47db1859a24c65481d6SGLang: supported · MiMoV2ForCausalLM · 773cb938fe9484a8f7e7e46fb1b1fe96412c59ed

vLLM registry (7050ef881ff0835f6890a47db1859a24c65481d6) · SGLang registry (773cb938fe9484a8f7e7e46fb1b1fe96412c59ed) · Matched by architecture class name against each project's registry on main; 'supported' means the architecture is registered, not that this checkpoint was tested.

Hardware fit

3 estimates

Hardware fit ESTIMATE

Weights-only memory for MiMo-V2.6-Pro-RL: params × bytes/param × 1.2 overhead. KV cache and activations are extra. How this is computed.

BF16 (2 bytes/param)2.46 TB · Cluster (≥640 GB)
FP8 (1 byte/param)1.23 TB · Cluster (≥640 GB)
INT4 (0.5 bytes/param)615 GB · Multi-GPU node (<640 GB)

Weights only (params × bytes/param × 1.2 overhead); KV cache and activations extra. MoE: all experts must be resident, so total parameters count.

All variants

13 variants

Lineage

Original release (shipped quantized by the lab) Family: XiaomiMiMo/MiMo-V2.6 13 derivatives on the Hub

quantized: 9finetune: 3adapter: 1

Top variants (13 shown, ranked by downloads)

Relation
Quant
Size
13 rows
Variant Relation Quant Params Band License Author Likes Downloads
ggml-org/MiMo-V2.6-Pro-RL-GGUF quantized GGUF — — mit ggml-org 7 5.6k
mlx-community/MiMo-V2.6-Pro-RL-mxfp4-q8 quantized mxfp4 1.02T XXL mit mlx-community 4 1.8k
inferencerlabs/MiMo-V2.6-Pro-RL-MLX-Q9 quantized MLX — — mit inferencerlabs 1 1.2k
jarrelscy/MiMo-V2.6-Pro-RL-ARVQ-hybrid quantized — 119B XXL mit jarrelscy 3 1k
DevQuasar/XiaomiMiMo.MiMo-V2.6-Pro-RL-GGUF quantized GGUF — — — DevQuasar 1 1.1k
kernelpool/MiMo-V2.6-Pro-RL-MXFP4-GGUF quantized MXFP4 — — mit kernelpool 1 331
Aidev2006/Inquisitive-V2.6-Pro-RL quantized — 1.02T XXL mit Aidev2006 0 280
Aidev2006/Inquisitive-V1-Pro-RL quantized — 1.02T XXL mit Aidev2006 0 259
jarrelscy/MiMo-V2.6-Pro-EXL3 finetune EXL3 — — — jarrelscy 3 0
smalinin/MiMo-V2.6-Pro-RL-GGUF quantized GGUF — — mit smalinin 0 119
gghfexp/MiMo-V2.6-Pro-RL-control-vectors-v1 adapter GGUF — — cc-by-nc-4.0 gghfexp 0 117
immanuelpeter/MiMoViT-2.6-Pro finetune — 739M S mit immanuelpeter 1 14
jarrelscy/MiMo-V2.6-Pro-EXL3-FP4 finetune EXL3 — — — jarrelscy 0 0

Repository changelog

11 commits
changed in the last 7 daysOther: 2026-10-24all changes

Repository changelog 11 commits

DateCommitSHA
2026-10-09 Align preprocessor config to training values (min/max_pixels, image_mean/std) b61f218
2026-09-22 Fix typo in report 73875d0
2026-09-22 Add ModelScope links 438e9ce
2026-09-21 Add assets/architecture.png 54b1049
2026-09-21 Add model card c165b4c
2026-09-21 Add files using upload-large-folder tool a949623
2026-09-21 Upload MiMo_V2_6_technical_report.pdf 7b08b5a
2026-09-21 Add files using upload-large-folder tool ff3db61
2026-09-21 Add files using upload-large-folder tool 6ab99ec
2026-09-21 Add files using upload-large-folder tool d1838a9
2026-09-21 initial commit c25d97f

All providers

5 providers · 5 endpoints

Where to run 5 providers · 5 endpoints

Hosted endpoints observed for MiMo-V2.6-Pro-RL, sorted by input price (USD per million tokens). Each row names its observer and date; prices change daily, so check the source before quoting. Cheapest observed input price: $0.39/M.

ProviderModel refContextQuant$/M in$/M outtok/sTTFTToolsStatusObserved
DeepInfra
openrouter
xiaomi/mimo-v2.6-pro 1M fp8 $0.39 $0.78 — — yes live 2026-10-24
Novita
hf, openrouter
xiaomimimo/mimo-v2.6-pro 1M fp8 $0.43 $0.87 29.1 1614 ms yes live 2026-10-25
GMICloud
openrouter
xiaomi/mimo-v2.6-pro 1.0M bf16 $0.43 $0.87 — — yes live 2026-10-24
Xiaomi
openrouter
xiaomi/mimo-v2.6-pro 1M fp8 $0.43 $0.87 — — yes live 2026-10-24
StreamLake
openrouter
xiaomi/mimo-v2.6-pro 977K unknown $0.52 $1.04 — — yes live 2026-10-24

Articles and coverage

17 pieces

Ranked by source signals (HN, Lobsters, authority domains, official posts) and recency. Summaries are generated from a stored copy of the article text; every sentence cites a span of that text, and nothing is shown that failed the citation check.

Official2

mimo.xiaomi.com2026-09-21officialHN front pageabout the MiMo V2.6 familyHN 1,130 pts · 483 comments3,621 words
Xiaomi releases the MiMo-V2.6 series on September 22nd, 2026, introducing MiMo-V2.6-Pro, MiMo-V2.6-Flash, and the new MiMo-V2.6-Pro-UltraSpeed variant delivering up to 20x faster output speed at the same quality. The models completed 30 RL steps over roughly 750k trajectories at costs of about $0.85M and $2.62M respectively, with DeepSWE v1.1 scores rising from 48.8 to 65.68 for the Pro model. This launch marks a key step in scaling RL compute on verifiable, complex tasks.written, reviewed, cited
  • MiMo-V2.6-Pro-UltraSpeed delivers up to 20x faster output speed at the same quality for users requiring extreme generation speed.
  • The training run completed in under six days with MiMo-V2.6-Pro costing about $2.62M and MiMo-V2.6-Flash costing about $0.85M.
mimo.xiaomi.com2026-09-27officialabout the MiMo V2.6 familyHN 2 pts · 1 comments1,793 words
MiMo-V2.6 models exhibited tool-call repetition where agents issued identical calls repeatedly, wasting context and stalling tasks. The team defined exact within-turn repetition as (N-U)/N and distinguished it from flooding, then adopted an MOPD-based approach to fix the issue. This solution cost approximately $90,000, significantly less than the $2.31 million estimated for the penalty-threshold fix, and reduced repetition rates substantially.written, reviewed, cited
  • The flooding penalty threshold was lowered from 32 calls to eight to suppress abnormal call volume.
  • Applying the stricter flooding penalty at full scale required restarting 20 training steps at an estimated cost of $2.31 million.

Analysis7

artificialanalysis.ai2026-09-22authorityHN front pageabout the MiMo V2.6 familyHN 166 pts · 68 comments973 words
MiMo-V2.6-Pro is a multimodal open-weight model from Xiaomi released on September 21, 2026, that scores 46 on the Artificial Analysis Intelligence Index. It costs $0.43 per 1M input tokens and $0.87 per 1M output tokens while generating output at 52 tokens per second. The model operates under the MIT license with 1.0 trillion total parameters and 42 billion active parameters during inference.written, reviewed, cited
  • The model uses a 7:2:1 cache hit/input/output ratio to calculate a blended rate of $0.18 per 1M tokens.
  • The model generates 140M output tokens on the Intelligence Index, which is described as somewhat higher than average.
sebastianraschka.com2026-09-22authorityabout the MiMo V2.6 familyHN 3 pts · 1 comments202 words
The model uses Grouped Query Attention with Sliding Window Attention at a 128-token window size. DeepSWE pass@1 accuracy on held-out harnesses improved from approximately 50% to 66%. Training employed large RL batches of 1,568 prompts with 16 rollouts generating 25,088 trajectories.generated, cited
forkast.news2026-09-18about the MiMo V2.6 familyHN 3 pts · 0 comments643 words
deeplearning.aiauthority1,313 words
aitooldaily.net2026-09-29about the MiMo V2.6 family1,339 words
prathamp.com2026-09-26about the MiMo V2.6 family8,707 words
mindstudio.ai2026-09-25

Community1

twitter.com2026-09-22about the MiMo V2.6 familyHN 2 pts · 0 comments
All coverage tracked for this model (17) — append-only, nothing is ever removed
TitleSourcePublishedFirst seenSignals
How Xiaomi’s MiMo-V2.6-Pro RL Topped Open Weights Chartsdeeplearning.ai—2026-10-07exa
MiMo-V2.6-Pro Official Technical Report: Architecture, Scaled RL, and Evaluation Conditions | Tabbitgo.tabbit.ai—2026-10-07exa
MiMo V2.6 Pro Review: Cheapest Open-Weight Model Yetaitooldaily.net2026-09-292026-10-07exa
Diagnosing and Mitigating Tool-Call Repetition in MiMo-v2.6mimo.xiaomi.com official2026-09-272026-10-07hn
MiMo-v2.6-Flash: on intelligence/price Pareto frontierartificialanalysis.ai2026-09-272026-10-07hn
MiMo-V2.6: Scaling Reinforcement Learning from Scratch | Pratham Patelprathamp.com2026-09-262026-10-07exa
MiMo-V2.6-Pro-RL: Xiaomi's 1T-Parameter Agentic Model, Explained | MindStudiomindstudio.ai2026-09-252026-10-07exa
MiMo-V2.6: Xiaomi's RL Paper, Verified vs Reportedorcarouter.ai2026-09-232026-10-07exa
MiMo-v2.6-Flashmimo.mi.com official2026-09-222026-10-07hn
MiMo-v2.6 is "simply" the besttwitter.com2026-09-222026-10-07hn
Xiaomi MiMo v2.6 Pro: New best open-weight LLM with simple designsebastianraschka.com2026-09-222026-10-07hn
MiMo-v2.6-Pro: Intelligence, Performance and Price Analysisartificialanalysis.ai2026-09-222026-10-07hn
Xiaomi MiMo V2.6-Pro Review (2026): Open RL Flagshiptoolworthy.ai2026-09-222026-10-07exa
MiMo-V2.6-Pro Review: 1M Context & Top Open Score 2026 | HokAIhokai.io2026-09-222026-10-07exa
MiMo v2.6mimo.xiaomi.com official2026-09-212026-10-07hn
XiaomiMiMo/MiMo-V2.6-Pro-RL · Paper Feedpaperfeed.app2026-09-212026-10-07exa
Xiaomi MiMo-v2.6 Breaks Cover: A 1T-Class Chinese Lab Trains in Publicforkast.news2026-09-182026-10-07hn

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Sources

4 sources

Sources

Hub snapshot: 2026-10-25 · HF API: 2026-10-24 21:36 UTC · Providers: 2026-10-24 21:44 UTC · Articles: 2026-10-24 21:45 UTC · Summary: 2026-10-07 15:04 UTC · Last verified 2026-10-25

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