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BenchLM
Model A
Claude Sonnet 4.5

Anthropic

54.2/100

Estimated · Public rank #103

90% interval 42.6–65.7

Claude Sonnet 4.5 vs Gemma 4 31B

Updated August 15, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B
Gemma 4 31B

Google

60.1/100

Supported · Public rank #60

90% interval 44.0–76.3

Decision reading

Gemma 4 31B has the higher public score estimate, 60.15 versus 54.16, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

2 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

    Gemma 4 31B

    Gemma 4 31B 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

    The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. Claude Sonnet 4.5 does not fit this workload in one request. Claude Sonnet 4.5 has no published cached-input rate, so cached tokens use its listed input rate. Gemma 4 31B has no comparable published API token rate.

    Confidence: rate-fallback

  • 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
2
Claude Sonnet 4.5 only
9
Gemma 4 31B only
6
Like-for-like categories
0 / 8

1 category uses different evidence sets. Those rows remain visible for coverage context but do not name a winner.

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.

Knowledge

Directional only
Claude Sonnet 4.5
83.4
Gemma 4 31B
52.9
Weighted basis
1 vs 3 rows
Reading
Directional only

Agentic

Not comparable
Claude Sonnet 4.5
55.4
Gemma 4 31B
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Coding

Not comparable
Claude Sonnet 4.5
77.2
Gemma 4 31B
41.6
Weighted basis
1 vs 1 rows
Reading
Not comparable

Reasoning

Not comparable
Claude Sonnet 4.5
13.6
Gemma 4 31B
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Math

Not comparable
Claude Sonnet 4.5
11.2
Gemma 4 31B
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Claude Sonnet 4.5
Not measured
Gemma 4 31B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Claude Sonnet 4.5
Not measured
Gemma 4 31B
76.9
Weighted basis
0 vs 1 rows
Reading
Not comparable

Instruction following

Not comparable
Claude Sonnet 4.5
Not measured
Gemma 4 31B
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.

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

Claude Sonnet 4.5
$0.0105
Fits in one request
Gemma 4 31B
Self-hosted; infrastructure cost varies
Fits in one request

Gemma 4 31B has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Claude Sonnet 4.5
$0.195
Fits in one request
Gemma 4 31B
Self-hosted; infrastructure cost varies
Fits in one request

Gemma 4 31B has no comparable published API token rate.

Cache-heavy agent loop

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

Claude Sonnet 4.5
$0.81
Does not fit in one request
Cached input priced at the published list-input rate
Gemma 4 31B
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable

Claude Sonnet 4.5 does not fit this workload in one request. Claude Sonnet 4.5 has no published cached-input rate, so cached tokens use its listed input rate. Gemma 4 31B 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.

Claude Sonnet 4.5

Not published

Gemma 4 31B

No comparable hosted API rate

Provider availability

Claude Sonnet 4.5

Not sourced

Gemma 4 31B

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

Google Gemma Gemini API guide

Reasoning profile

Claude Sonnet 4.5

Non-Reasoning

Gemma 4 31B

Reasoning

Weight access

Claude Sonnet 4.5

Proprietary

Gemma 4 31B

Open Weight

License

Claude Sonnet 4.5

Proprietary

Gemma 4 31B

Open Weight

Release date

Claude Sonnet 4.5

2025-09-01

Gemma 4 31B

2026-04-02

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
Gemma 4 31B has the higher public score estimate, 60.15 versus 54.16, but the 90% score intervals overlap.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Gemma 4 31B has the larger documented window (256K).

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.

Claude Sonnet 4.5
API / mo$13,500
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Gemma 4 31B
API / mo$0
Self-host / mo$429
Break-even—
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 evidence17 rows

Agentic

  • Terminal-Bench 2.0

    Claude Sonnet 4.550%
    Source
    Gemma 4 31B—

    Not directly comparable

  • OSWorld-Verified

    Claude Sonnet 4.561.4%
    Source
    Gemma 4 31B—

    Not directly comparable

  • VITA-Bench

    Claude Sonnet 4.517.0%
    Source
    Gemma 4 31B—

    Not directly comparable

  • Gert Labs

    Shared source
    Claude Sonnet 4.548.51%
    Gemma 4 31B35.26%

    Claude Sonnet 4.5 leads this result

  • JobBench

    Claude Sonnet 4.527.7%
    Source
    Gemma 4 31B—

    Not directly comparable

Coding

  • SWE-bench Verified

    Claude Sonnet 4.577.2%
    Source
    Gemma 4 31B—

    Not directly comparable

  • SWE-Rebench

    Claude Sonnet 4.5—
    Gemma 4 31B41.6%
    Source

    Not directly comparable

  • React Native Evals

    Claude Sonnet 4.5—
    Gemma 4 31B75.2%
    Source

    Not directly comparable

Reasoning

  • ARC-AGI-2

    Claude Sonnet 4.513.6%
    Source
    Gemma 4 31B—

    Not directly comparable

Knowledge

  • GPQA

    Claude Sonnet 4.583.4%
    Source
    Gemma 4 31B84.3%
    Source

    Gemma 4 31B leads this result

  • MMLU-Pro

    Claude Sonnet 4.5—
    Gemma 4 31B85.2%
    Source

    Not directly comparable

  • HLE

    Claude Sonnet 4.5—
    Gemma 4 31B26.5%
    Source

    Not directly comparable

  • HLE w/o tools

    Claude Sonnet 4.5—
    Gemma 4 31B19.5%
    Source

    Not directly comparable

Math

  • AIME 2025

    Claude Sonnet 4.587%
    Source
    Gemma 4 31B—

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    Claude Sonnet 4.513.495%
    Source
    Gemma 4 31B—

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    Claude Sonnet 4.54.167%
    Source
    Gemma 4 31B—

    Not directly comparable

Multimodal

  • MMMU-Pro

    Claude Sonnet 4.5—
    Gemma 4 31B76.9%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Claude Sonnet 4.5 or Gemma 4 31B?

Gemma 4 31B has the higher public score estimate, 60.15 versus 54.16, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, Claude Sonnet 4.5 or Gemma 4 31B?

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, Claude Sonnet 4.5 or Gemma 4 31B?

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, Claude Sonnet 4.5 or Gemma 4 31B?

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, Claude Sonnet 4.5 or Gemma 4 31B?

Gemma 4 31B has the larger documented context window: 256K, compared with 200K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 15, 2026

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