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BenchLM
Model A
MiMo-V2-Omni

Xiaomi

62.2/100

Supported · Public rank #46

90% interval 52.0–72.4

MiMo-V2-Omni vs Qwen3.5-122B-A10B

Updated August 13, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B
Qwen3.5-122B-A10B

Alibaba

59.5/100

Supported · Public rank #61

90% interval 48.3–70.7

Decision reading

MiMo-V2-Omni has the higher public score estimate, 62.17 versus 59.47, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

1 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.

Which one for your work

Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.

  • Coding work

    Code generation, repair, and software-engineering tasks

    MiMo-V2-Omni

    MiMo-V2-Omni leads on the same 1 weighted benchmark row.

    Confidence: limited

Show secondary and unsupported calls

  • Agentic work

    Tool use, computer use, and multi-step task completion

    Not enough matched evidence

    No shared weighted benchmark basis supports a winner.

    Confidence: limited

  • Long documents

    Prompts that approach the documented context limit

    No clear pick

    The documented context windows are equal.

    Confidence: documented

  • Chat turn cost

    1K fresh input + 500 output tokens

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

  • Cache-heavy agent loop cost

    200K cached + 20K fresh input + 10K output tokens

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

  • Repository review cost

    50K fresh input + 3K output tokens

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

Shared results
1
MiMo-V2-Omni only
1
Qwen3.5-122B-A10B only
14
Like-for-like categories
1 / 8

Category results, on a stated basis

Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.

Coding

Like-for-like
MiMo-V2-Omni
74.8
Qwen3.5-122B-A10B
72.0
Weighted basis
1 vs 1 rows
Reading
MiMo-V2-Omni leads

Agentic

Not comparable
MiMo-V2-Omni
Not measured
Qwen3.5-122B-A10B
56.4
Weighted basis
0 vs 3 rows
Reading
Not comparable

Reasoning

Not comparable
MiMo-V2-Omni
Not measured
Qwen3.5-122B-A10B
60.2
Weighted basis
0 vs 1 rows
Reading
Not comparable

Knowledge

Not comparable
MiMo-V2-Omni
Not measured
Qwen3.5-122B-A10B
83.6
Weighted basis
0 vs 3 rows
Reading
Not comparable

Math

Not comparable
MiMo-V2-Omni
Not measured
Qwen3.5-122B-A10B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
MiMo-V2-Omni
Not measured
Qwen3.5-122B-A10B
82.2
Weighted basis
0 vs 1 rows
Reading
Not comparable

Multimodal

Not comparable
MiMo-V2-Omni
Not measured
Qwen3.5-122B-A10B
77.2
Weighted basis
0 vs 1 rows
Reading
Not comparable

Instruction following

Not comparable
MiMo-V2-Omni
Not measured
Qwen3.5-122B-A10B
93.4
Weighted basis
0 vs 1 rows
Reading
Not comparable

Shape of the matched evidence

Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.

Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.

What each workload costs

Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.

Chat turn

1K fresh input + 500 output tokens

MiMo-V2-Omni
API rate not published
Fits in one request
Qwen3.5-122B-A10B
Self-hosted; infrastructure cost varies
Fits in one request

MiMo-V2-Omni has no comparable published API token rate. Qwen3.5-122B-A10B has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

MiMo-V2-Omni
API rate not published
Fits in one request
Qwen3.5-122B-A10B
Self-hosted; infrastructure cost varies
Fits in one request

MiMo-V2-Omni has no comparable published API token rate. Qwen3.5-122B-A10B has no comparable published API token rate.

Cache-heavy agent loop

200K cached + 20K fresh input + 10K output tokens

MiMo-V2-Omni
API rate not published
Fits in one request
Cached-input rate unavailable
Qwen3.5-122B-A10B
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable

MiMo-V2-Omni has no comparable published API token rate. Qwen3.5-122B-A10B has no comparable published API token rate.

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

Context window

Maximum documented context; output-token limits may be lower.

MiMo-V2-Omni

262K

Qwen3.5-122B-A10B

262K

API model ID

MiMo-V2-Omni

Not sourced

Qwen3.5-122B-A10B

Not sourced

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

MiMo-V2-Omni

No comparable hosted API rate

Qwen3.5-122B-A10B

No comparable hosted API rate

Documented inputs

MiMo-V2-Omni

Not sourced

Qwen3.5-122B-A10B

Not sourced

Documented outputs

MiMo-V2-Omni

Not sourced

Qwen3.5-122B-A10B

Not sourced

Provider availability

MiMo-V2-Omni

Not sourced

Qwen3.5-122B-A10B

Not sourced

Reasoning profile

MiMo-V2-Omni

Reasoning

Qwen3.5-122B-A10B

Reasoning

Weight access

MiMo-V2-Omni

Proprietary

Qwen3.5-122B-A10B

Open Weight

License

MiMo-V2-Omni

Proprietary

Qwen3.5-122B-A10B

Open Weight

Release date

MiMo-V2-Omni

2026-03-18

Qwen3.5-122B-A10B

2026-03-04

If you already use one of these models
Deployment change
The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
Quality signal
MiMo-V2-Omni has the higher public score estimate, 62.17 versus 59.47, but the 90% score intervals overlap.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Both models list 262K.

Run the same representative tasks against both endpoints before changing production traffic.

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence16 rows

Agentic

  • Claw-Eval

    MiMo-V2-Omni45.2%
    Source
    Qwen3.5-122B-A10B—

    Not directly comparable

  • Terminal-Bench 2.0

    MiMo-V2-Omni—
    Qwen3.5-122B-A10B49.4%
    Source

    Not directly comparable

  • BrowseComp

    MiMo-V2-Omni—
    Qwen3.5-122B-A10B63.8%
    Source

    Not directly comparable

  • OSWorld-Verified

    MiMo-V2-Omni—
    Qwen3.5-122B-A10B58%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    MiMo-V2-Omni74.8%
    Source
    Qwen3.5-122B-A10B72%
    Source

    MiMo-V2-Omni leads this result

Reasoning

  • LongBench v2

    MiMo-V2-Omni—
    Qwen3.5-122B-A10B60.2%
    Source

    Not directly comparable

Knowledge

  • MMLU-Pro

    MiMo-V2-Omni—
    Qwen3.5-122B-A10B86.7%
    Source

    Not directly comparable

  • SuperGPQA

    MiMo-V2-Omni—
    Qwen3.5-122B-A10B67.1%
    Source

    Not directly comparable

  • GPQA

    MiMo-V2-Omni—
    Qwen3.5-122B-A10B86.6%
    Source

    Not directly comparable

Multilingual

  • MMLU-ProX

    MiMo-V2-Omni—
    Qwen3.5-122B-A10B82.2%
    Source

    Not directly comparable

Multimodal

  • MMMU

    MiMo-V2-Omni—
    Qwen3.5-122B-A10B83.9%
    Source

    Not directly comparable

  • MMVU

    MiMo-V2-Omni—
    Qwen3.5-122B-A10B74.7%
    Source

    Not directly comparable

  • MathVision

    MiMo-V2-Omni—
    Qwen3.5-122B-A10B86.2%
    Source

    Not directly comparable

  • CharXiv

    MiMo-V2-Omni—
    Qwen3.5-122B-A10B77.2%
    Source

    Not directly comparable

  • V*

    MiMo-V2-Omni—
    Qwen3.5-122B-A10B93.2%
    Source

    Not directly comparable

Instruction following

  • IFEval

    MiMo-V2-Omni—
    Qwen3.5-122B-A10B93.4%
    Source

    Not directly comparable

Frequently asked questions

Which is better, MiMo-V2-Omni or Qwen3.5-122B-A10B?

MiMo-V2-Omni has the higher public score estimate, 62.17 versus 59.47, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, MiMo-V2-Omni or Qwen3.5-122B-A10B?

MiMo-V2-Omni leads the like-for-like coding comparison across 1 shared weighted benchmark row.

Which is better for agentic tasks, MiMo-V2-Omni or Qwen3.5-122B-A10B?

The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.

Which costs less, MiMo-V2-Omni or Qwen3.5-122B-A10B?

Both models do not have comparable published API token rates, so this page does not name a universal price winner.

Which has the larger context window, MiMo-V2-Omni or Qwen3.5-122B-A10B?

Both models list the same context window, 262K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 13, 2026

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