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MiMo-V2.6-Flash

Xiaomi · released Sep 21, 2026

Open weightsReasoningTool useStructured outputVisionAudio inVideo in

MiMo-V2.6-Flash is an open-source foundation model developed by Xiaomi. Built on a Mixture-of-Experts architecture with 309B total parameters and 15B activated per token, it employs a hybrid attention mechanism for...

Specification

Context window
1.05M
Max output
131K
Knowledge cutoff
Not stated
Parameters
310.8B
Licence
mit
Serving providers
8
Moderated
No
Uptime
100.0%

Intelligence

37.9

89th percentile

Coding

—

Coding Index

Agentic

—

Agentic Index

Output speed

82 t/s

Median across providers

Latency

1.25s

Time to first token

Cost per task

$0.03

Estimated

Benchmarks

Where the score comes from

The Intelligence Index is a composite. These are the underlying evaluations this model was actually measured on.

Evaluation scores

Percentage correct · higher is better

  • AA-LCR (long context)
    74.3%
  • SciCode
    51.3%
  • Humanity's Last Exam
    35.1%

An evaluation missing from this list was not run for this model — it is not a zero.

View as table
EvaluationScore
AA-LCR (long context)74.3%
SciCode51.3%
Humanity's Last Exam35.1%

Against its peers

Intelligence Index · this model highlighted, nearest peers in grey

Peers are the models sitting closest on the Intelligence Index — the set you would realistically choose between.

View as table
ModelIntelligence
Gemini 3.7 Flash39.1
GPT-5.439.0
Grok 4.538.8
GPT-5.538.4
Claude Sonnet 538.2
GPT-6 Luna38.1
MiMo-V2.6-Flash37.9
GPT-5.6 Luna37.3

Percentile among all indexed models

Intelligence89th

Pricing

What it costs to run

List prices per million tokens, plus what one representative task works out to.

List price

Input / 1M tokens
$0.14
Output / 1M tokens
$0.28
Cached input / 1M
$0.003
Blended 3:1
$0.175

One task, estimated

$0.03

Input tokens
50,000
Output tokens
80,000
Profile
Reasoning

Estimated from list pricing: 50K input tokens plus 80K output tokens for reasoning models (25K for non-reasoning).

Serving providers

Speed and latency figures are medians across these providers, so a widely-served model reports a blend rather than any single endpoint.