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BenchLM
Model A
Gemma 4 31B

Google

60.1/100

Supported · Public rank #60

90% interval 44.0–76.3

Gemma 4 31B vs MiMo-V2-Pro

Updated August 18, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B
MiMo-V2-Pro

Xiaomi

66.9/100

Supported · Public rank #26

90% interval 58.9–74.9

Decision reading

MiMo-V2-Pro has the higher public score estimate, 66.94 versus 60.15, 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.

  • Long documents

    Prompts that approach the documented context limit

    MiMo-V2-Pro

    MiMo-V2-Pro has the larger documented context window.

    Confidence: documented

Show secondary and unsupported calls

  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    No shared weighted benchmark basis supports a winner.

    Confidence: limited

  • 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

  • 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
Gemma 4 31B only
7
MiMo-V2-Pro only
3
Like-for-like categories
0 / 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.

Agentic

Not comparable
Gemma 4 31B
Not measured
MiMo-V2-Pro
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Coding

Not comparable
Gemma 4 31B
41.6
MiMo-V2-Pro
78.0
Weighted basis
1 vs 1 rows
Reading
Not comparable

Reasoning

Not comparable
Gemma 4 31B
Not measured
MiMo-V2-Pro
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
Gemma 4 31B
52.9
MiMo-V2-Pro
Not measured
Weighted basis
3 vs 0 rows
Reading
Not comparable

Math

Not comparable
Gemma 4 31B
Not measured
MiMo-V2-Pro
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Gemma 4 31B
Not measured
MiMo-V2-Pro
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Gemma 4 31B
76.9
MiMo-V2-Pro
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Gemma 4 31B
Not measured
MiMo-V2-Pro
Not measured
Weighted basis
0 vs 0 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.

A shared-evidence shape is not available.

BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.

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

Gemma 4 31B
Self-hosted; infrastructure cost varies
Fits in one request
MiMo-V2-Pro
API rate not published
Fits in one request

Gemma 4 31B has no comparable published API token rate. MiMo-V2-Pro has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Gemma 4 31B
Self-hosted; infrastructure cost varies
Fits in one request
MiMo-V2-Pro
API rate not published
Fits in one request

Gemma 4 31B has no comparable published API token rate. MiMo-V2-Pro has no comparable published API token rate.

Cache-heavy agent loop

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

Gemma 4 31B
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable
MiMo-V2-Pro
API rate not published
Fits in one request
Cached-input rate unavailable

Gemma 4 31B has no comparable published API token rate. MiMo-V2-Pro 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.

Cached-input rate

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

Gemma 4 31B

No comparable hosted API rate

MiMo-V2-Pro

No comparable hosted API rate

Provider availability

Gemma 4 31B

Generally Available · Gemini API, Google AI Studio, open weights

Google Gemma Gemini API guide

MiMo-V2-Pro

Not sourced

Reasoning profile

Gemma 4 31B

Reasoning

MiMo-V2-Pro

Reasoning

Weight access

Gemma 4 31B

Open Weight

MiMo-V2-Pro

Proprietary

License

Gemma 4 31B

Open Weight

MiMo-V2-Pro

Proprietary

Release date

Gemma 4 31B

2026-04-02

MiMo-V2-Pro

2026-03-18

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-Pro has the higher public score estimate, 66.94 versus 60.15, but the 90% score intervals overlap.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
MiMo-V2-Pro has the larger documented window (1M).

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

Self-host vs API cost

Estimates at 50,000 req/day · 1000 tokens/req average.

Gemma 4 31B
API / mo$0
Self-host / mo$429
Break-even—
MiMo-V2-Pro
API / mo$0
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Model the full break-even

Benchmark evidence

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

Browse raw public benchmark evidence11 rows

Agentic

  • Gert Labs

    Shared source
    Gemma 4 31B35.26%
    MiMo-V2-Pro36.68%

    MiMo-V2-Pro leads this result

  • Claw-Eval

    Gemma 4 31B—
    MiMo-V2-Pro57.8%
    Source

    Not directly comparable

  • ResearchClawBench

    Gemma 4 31B—
    MiMo-V2-Pro15.3%
    Source

    Not directly comparable

Coding

  • SWE-Rebench

    Gemma 4 31B41.6%
    Source
    MiMo-V2-Pro—

    Not directly comparable

  • React Native Evals

    Gemma 4 31B75.2%
    Source
    MiMo-V2-Pro—

    Not directly comparable

  • SWE-bench Verified

    Gemma 4 31B—
    MiMo-V2-Pro78%
    Source

    Not directly comparable

Knowledge

  • GPQA

    Gemma 4 31B84.3%
    Source
    MiMo-V2-Pro—

    Not directly comparable

  • MMLU-Pro

    Gemma 4 31B85.2%
    Source
    MiMo-V2-Pro—

    Not directly comparable

  • HLE

    Gemma 4 31B26.5%
    Source
    MiMo-V2-Pro—

    Not directly comparable

  • HLE w/o tools

    Gemma 4 31B19.5%
    Source
    MiMo-V2-Pro—

    Not directly comparable

Multimodal

  • MMMU-Pro

    Gemma 4 31B76.9%
    Source
    MiMo-V2-Pro—

    Not directly comparable

Frequently asked questions

Which is better, Gemma 4 31B or MiMo-V2-Pro?

MiMo-V2-Pro has the higher public score estimate, 66.94 versus 60.15, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, Gemma 4 31B or MiMo-V2-Pro?

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

Which is better for agentic tasks, Gemma 4 31B or MiMo-V2-Pro?

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, Gemma 4 31B or MiMo-V2-Pro?

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, Gemma 4 31B or MiMo-V2-Pro?

MiMo-V2-Pro has the larger documented context window: 1M, compared with 256K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 18, 2026

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