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Xiaomi mit Released Sep 22, 2026 Updated

MiMo-V2.6-Flash

ToolsGuide Scores, rankings, test results and price.

Model summary

  • Text #76 / 416

    421 / 1,000

    ToolsGuide Score· votes only

  • Vision #62 / 167

    425 / 1,000

    ToolsGuide Score· votes only

  • Coding #23 / 147

    477 / 1,000

    ToolsGuide Score· votes 480, tests 474

  • Agents #20 / 53

    490 / 1,000

    ToolsGuide Score· votes 484, tests 492

  • Price per 1M tokens

    In $0.14· Out $0.28

    $0.14

    Input · median coding model $0.75

Summary

MiMo-V2.6-Flash is Xiaomi's model, released Sep 22, 2026. It's ranked in 4 categories and does best at coding: #23 of 147, with a ToolsGuide Score of 477 against a median of 316.

It also ranks #76 of 416 for text (421), #62 of 167 for vision (425), #20 of 53 for agents (490).

Its text and vision score uses votes only, because it has published fewer than two of the tests used for those categories.

It costs $0.14 per million input tokens and $0.28 per million output tokens, below the median coding model ($0.75).

It has published results on 16 tests and has the top score on 1 of them, including CyberGym.

Specifications

Creator
Xiaomi
Licence
mit
Released
Sep 22, 2026
Context window
1M tokens
Input price
$0.14 / 1M
Output price
$0.28 / 1M
Tests reported
16
Settings ranked
mimo-v2.6-flash, MiMo-V2.6-Flash

Model comparison

Against the ten best coding models.

Coding score

ToolsGuide Score out of 1,000 · Higher is better

Text score

ToolsGuide Score out of 1,000 · Higher is better

Input price

USD per 1M input tokens · Lower is better

Leaderboards

Where MiMo-V2.6-Flash sits among the top 20 in each category.

Text

#76 of 416 · ToolsGuide Score out of 1,000, where 1,000 is a flawless model

Full text leaderboard →

Vision

#62 of 167 · ToolsGuide Score out of 1,000, where 1,000 is a flawless model

Full vision leaderboard →

Coding

#23 of 147 · ToolsGuide Score out of 1,000, where 1,000 is a flawless model

Full coding leaderboard →

Agents

#20 of 53 · ToolsGuide Score out of 1,000, where 1,000 is a flawless model

Full agents leaderboard →

By task

Scored from votes on each kind of prompt alone, on the same 1,000-point scale.

Score against price

Coding · the 10 priced models ranked closest · up and to the left is better value

Against its nearest rivals

Text prompts by kind · ToolsGuide Score from votes · hover for values

Text

mimo-v2.6-flash · hover a bar for its rank

Vision

MiMo-V2.6-Flash · hover a bar for its rank

Coding

MiMo-V2.6-Flash · hover a bar for its rank

Test results

Every test MiMo-V2.6-Flash has a published result on, out of 100%, with its rank among the models that took it.

  • DeepSWE 1.1

    #24 / 50

    DeepSWE 1.1 evaluates software engineering agents using the mini-swe-agent harness.

    67.9%

    Self-reported · In the coding score

  • Terminal-Bench 2.1

    #9 / 46

    Terminal-Bench 2.1 is an updated release of the Terminal-Bench benchmark that tests AI agents' ability to operate a computer via the terminal. It evaluates how well models handle real-world, end-to-end tasks autonomously, including compiling code, training models, setting up servers, system administration, data science workflows, and security tasks.

    87.6%

    Self-reported

  • OSWorld-Verified

    #8 / 28

    OSWorld-Verified is a verified subset of OSWorld, a scalable real computer environment for multimodal agents supporting task setup, execution-based evaluation, and interactive learning across Ubuntu, Windows, and macOS.

    80.8%

    Self-reported

  • Terminal-Bench 4.0

    #19 / 28

    Terminal-Bench 4.0 evaluates AI agents on real-world work in terminal and command-line containerized environments across 66 tasks, with emphasis on science-adjacent and frontier engineering problems. Relative to earlier Terminal-Bench releases, 4.0 increases timeouts and adaptively raises RAM/CPU on selected tasks to reduce harness and resource confounds.

    28.8%

    Self-reported · In the coding score· In the agents score

  • Agents' Last Exam

    #18 / 24

    Agents' Last Exam is a challenging benchmark for AI agents on hard, long-horizon tasks that test sustained reasoning, planning, and tool use, reported with and without tool access.

    27.6%

    Self-reported · In the agents score

  • CyberGym

    #1 / 21

    CyberGym is a benchmark for evaluating AI agents on cybersecurity tasks, testing their ability to identify vulnerabilities, perform security analysis, and complete security-related challenges in a controlled environment.

    95.1%

    Self-reported

  • Program Bench

    #15 / 18

    Program Bench evaluates code-generation agents by asking them to recreate a program's behavior from only a compiled binary and documentation. It spans 200 tasks from small CLI tools to large systems such as FFmpeg and SQLite, with submissions judged against more than 248,000 fuzz-generated behavioral tests.

    26.0%

    Self-reported · In the coding score

  • ExploitBench

    #8 / 8

    ExploitBench is a cybersecurity benchmark that evaluates a model's ability to discover and exploit software vulnerabilities, reported as the fraction of challenges where the model captures the target (Cap%).

    25.3%

    Self-reported

  • ExploitGym

    #8 / 8

    ExploitGym is a large-scale, realistic benchmark built from real-world vulnerabilities across userspace programs, Google's V8 engine, and the Linux kernel. Given a vulnerability and a proof-of-vulnerability input, agents must craft a working end-to-end exploit that achieves unauthorized code execution.

    6.0%

    Self-reported

  • SEC-bench Pro

    #7 / 7

    SEC-bench Pro is a self-evolving software-security benchmark that measures agent bug hunting on critical, high-complexity systems. It instantiates validated vulnerabilities across the V8 and SpiderMonkey JavaScript engines as reproducible vulnerability-discovery and proof-of-concept-generation tasks with oracle-based validation.

    47.5%

    Self-reported

  • AutomationBench v1.0.6

    #3 / 6

    AutomationBench v1.0.6 evaluates agent performance on multi-step workflow automation tasks.

    52.3%

    Self-reported

  • JobBench

    #6 / 6

    JobBench evaluates agents on work-related tasks, as reported in the MiMo-V2.6 release.

    61.2%

    Self-reported

  • Toolathlon-Verified

    #4 / 5

    Verified Toolathlon evaluation of agent tool-use capability, as reported in the MiMo-V2.6 release.

    73.6%

    Self-reported

  • MiMo Coding Bench

    #4 / 4

    MiMo Coding Bench evaluates coding-agent capabilities on software engineering tasks reported with the MiMo model family.

    61.2%

    Self-reported

  • MiMo Cyber Bench

    #2 / 2

    Xiaomi's in-house cybersecurity evaluation, reported with MiMo-V2.6.

    77.2%

    Unverified

  • MiMo Visual Coding

    #2 / 2

    Xiaomi's in-house evaluation of visual coding capability, reported with MiMo-V2.6.

    71.5%

    Unverified