Model comparison

Gemma 4 31B vs GPT-5.6 Sol

Updated August 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Gemma 4 31B

Google

60.1/100

Supported · Public rank #50

90% interval 44.6–75.6

GPT-5.6 Sol

OpenAI

81.5/100

Supported · Public rank #4

90% interval 77.7–85.3

GPT-5.6 Sol has the higher public score, 81.48 versus 60.08, and the 90% score intervals do not overlap.

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
GPT-5.6 Sol
GPT-5.6 Sol 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.

6 Gemma 4 31B only

2 Shared

22 GPT-5.6 Sol only

Shared results: 2
Gemma 4 31B only: 6
GPT-5.6 Sol only: 22
Like-for-like categories: 1 / 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.

Multimodal

Like-for-like

Gemma 4 31B: 76.9
GPT-5.6 Sol: 83.0
Weighted basis: 1 vs 1 rows
Reading: GPT-5.6 Sol leads

Knowledge

Directional only

Gemma 4 31B: 52.9
GPT-5.6 Sol: 94.6
Weighted basis: 3 vs 1 rows
Reading: Directional only

Agentic

Not comparable

Gemma 4 31B: Not measured
GPT-5.6 Sol: 92.0
Weighted basis: 0 vs 2 rows
Reading: Not comparable

Coding

Not comparable

Gemma 4 31B: 41.6
GPT-5.6 Sol: 64.6
Weighted basis: 1 vs 1 rows
Reading: Not comparable

Reasoning

Not comparable

Gemma 4 31B: Not measured
GPT-5.6 Sol: 92.5
Weighted basis: 0 vs 1 rows
Reading: Not comparable

Math

Not comparable

Gemma 4 31B: Not measured
GPT-5.6 Sol: 87.5
Weighted basis: 0 vs 2 rows
Reading: Not comparable

Multilingual

Not comparable

Gemma 4 31B: Not measured
GPT-5.6 Sol: Not measured
Weighted basis: 0 vs 0 rows
Reading: Not comparable

Instruction following

Not comparable

Gemma 4 31B: Not measured
GPT-5.6 Sol: Not measured
Weighted basis: 0 vs 0 rows
Reading: Not comparable

Category averages with the server-provided evidence basis for Gemma 4 31B and GPT-5.6 Sol
Category	Gemma 4 31B	GPT-5.6 Sol	Weighted basis	Reading
Multimodal	76.9	83.0	Like-for-like1 vs 1 rows	GPT-5.6 Sol leads
Knowledge	52.9	94.6	Directional only3 vs 1 rows	Directional only
Agentic	Not measured	92.0	Not comparable0 vs 2 rows	Not comparable
Coding	41.6	64.6	Not comparable1 vs 1 rows	Not comparable
Reasoning	Not measured	92.5	Not comparable0 vs 1 rows	Not comparable
Math	Not measured	87.5	Not comparable0 vs 2 rows	Not comparable
Multilingual	Not measured	Not measured	Not comparable0 vs 0 rows	Not comparable
Instruction following	Not measured	Not measured	Not comparable0 vs 0 rows	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.

GPQA
Knowledge
Gemma 4 31B: 84.3%GPT-5.6 Sol: 94.6%Normalized gap 10.3Gemma 4 31B source GPT-5.6 Sol source
MMMU-Pro
Multimodal
Gemma 4 31B: 76.9%GPT-5.6 Sol: 83%Normalized gap 6.1Gemma 4 31B source GPT-5.6 Sol source

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
GPT-5.6 Sol: $0.02; Fits in one request

Gemma 4 31B 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
GPT-5.6 Sol: $0.34; 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

Gemma 4 31B: Self-hosted; infrastructure cost varies; Fits in one request; Cached-input rate unavailable
GPT-5.6 Sol: $0.5; Fits in one request

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.

SpecificationGemma 4 31BGPT-5.6 Sol

Context window

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

Gemma 4 31B

256K

Google Gemma 4 model documentation

GPT-5.6 Sol

1.05M

OpenAI model catalog

API model ID

Gemma 4 31B

gemma-4-31b-it

Google Gemma Gemini API guide

GPT-5.6 Sol

gpt-5.6-sol

OpenAI model catalog

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

GPT-5.6 Sol

$0.5 per 1M cached input tokens

OpenAI pricing

Documented inputs

Gemma 4 31B

text, image

Google Gemma 4 model documentation

GPT-5.6 Sol

text, image

OpenAI model catalog

Documented outputs

Gemma 4 31B

text

Google Gemma 4 model documentation

GPT-5.6 Sol

text

OpenAI model catalog

Provider availability

Gemma 4 31B

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

Google Gemma Gemini API guide

GPT-5.6 Sol

Generally Available · OpenAI Responses API

OpenAI model catalog

Reasoning profile

Gemma 4 31B

Reasoning

GPT-5.6 Sol

Reasoning

Weight access

Gemma 4 31B

Open Weight

GPT-5.6 Sol

Proprietary

License

Gemma 4 31B

Open Weight

GPT-5.6 Sol

Proprietary

Release date

Gemma 4 31B

2026-04-02

GPT-5.6 Sol

2026-07-09

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: GPT-5.6 Sol has the higher public score, 81.48 versus 60.08, and the 90% score intervals do not overlap.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: GPT-5.6 Sol has the larger documented window (1.05M).

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—

GPT-5.6 Sol

API / mo$26,250

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 evidence30 rows

Agentic

Gert Labs
Gemma 4 31B35.26%
Source
GPT-5.6 Sol—
Not directly comparable
Terminal-Bench 2.0
Gemma 4 31B—
GPT-5.6 Sol91.9%
Source
Not directly comparable
BrowseComp
Gemma 4 31B—
GPT-5.6 Sol92.2%
Source
Not directly comparable
OSWorld 2.0
Gemma 4 31B—
GPT-5.6 Sol62.6%
Source
Not directly comparable
CyberGym
Gemma 4 31B—
GPT-5.6 Sol84.5%
Source
Not directly comparable
ExploitGym
Gemma 4 31B—
GPT-5.6 Sol33.7%
Source
Not directly comparable
Toolathlon
Gemma 4 31B—
GPT-5.6 Sol58%
Source
Not directly comparable

Coding

SWE-Rebench
Gemma 4 31B41.6%
Source
GPT-5.6 Sol—
Not directly comparable
React Native Evals
Gemma 4 31B75.2%
Source
GPT-5.6 Sol—
Not directly comparable
SWE-bench Pro
Gemma 4 31B—
GPT-5.6 Sol64.6%
Source
Not directly comparable
Terminal-Bench 2.0
Gemma 4 31B—
GPT-5.6 Sol91.9%
Source
Not directly comparable
deepSwe
Gemma 4 31B—
GPT-5.6 Sol72.7%
Source
Not directly comparable
FrontierCode 1.1 Extended
Gemma 4 31B—
GPT-5.6 Sol60.6%
Source
Not directly comparable
cursorBench32
Gemma 4 31B—
GPT-5.6 Sol67.2%
Source
Not directly comparable
VulcanBench v3
Gemma 4 31B—
GPT-5.6 Sol87.0%
Source
Not directly comparable

Reasoning

ARC-AGI-2
Gemma 4 31B—
GPT-5.6 Sol92.5%
Source
Not directly comparable
ARC-AGI-3
Gemma 4 31B—
GPT-5.6 Sol7.8%
Source
Not directly comparable
GeneBench-Pro
Gemma 4 31B—
GPT-5.6 Sol28.7%
Source
Not directly comparable

Knowledge

GPQA
Gemma 4 31B84.3%
Source
GPT-5.6 Sol94.6%
Source
GPT-5.6 Sol leads this result
MMLU-Pro
Gemma 4 31B85.2%
Source
GPT-5.6 Sol—
Not directly comparable
HLE
Gemma 4 31B26.5%
Source
GPT-5.6 Sol—
Not directly comparable
HLE w/o tools
Gemma 4 31B19.5%
Source
GPT-5.6 Sol—
Not directly comparable
GPQA-D
Gemma 4 31B—
GPT-5.6 Sol94.6%
Source
Not directly comparable
HealthBench Professional
Gemma 4 31B—
GPT-5.6 Sol60.5%
Source
Not directly comparable
HealthBench Hard
Gemma 4 31B—
GPT-5.6 Sol33.1%
Source
Not directly comparable

Math

FrontierMath (legacy)
Gemma 4 31B—
GPT-5.6 Sol89%
Source
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Gemma 4 31B—
GPT-5.6 Sol89.000%
Source
Not directly comparable
FrontierMath v2 (Tier 4)
Gemma 4 31B—
GPT-5.6 Sol83.000%
Source
Not directly comparable

Multimodal

MMMU-Pro
Gemma 4 31B76.9%
Source
GPT-5.6 Sol83%
Source
GPT-5.6 Sol leads this result
MMMU-Pro w/ Python
Gemma 4 31B—
GPT-5.6 Sol84.6%
Source
Not directly comparable

Frequently asked questions

Which is better, Gemma 4 31B or GPT-5.6 Sol?

GPT-5.6 Sol has the higher public score, 81.48 versus 60.08, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.

Which is better for coding, Gemma 4 31B or GPT-5.6 Sol?

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 GPT-5.6 Sol?

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 GPT-5.6 Sol?

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 GPT-5.6 Sol?

GPT-5.6 Sol has the larger documented context window: 1.05M, compared with 256K.

Related comparisons

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Last updated August 10, 2026

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Gemma 4 31B vs GPT-5.6 Sol

Which one for your work

Long documents

Coding work

Agentic work

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

What is actually comparable

Category results, on a stated basis

Multimodal

Knowledge

Agentic

Coding

Reasoning

Math

Multilingual

Instruction following

Shape of the matched evidence

What each workload costs

Chat turn

Repository review

Cache-heavy agent loop

Specification differences

Context window

API model ID

Cached-input rate

Documented inputs

Documented outputs

Provider availability

Reasoning profile

Weight access

License

Release date

Self-host vs API cost

Benchmark evidence

Agentic

Coding

Reasoning

Knowledge

Math

Multimodal

Frequently asked questions

Which is better, Gemma 4 31B or GPT-5.6 Sol?

Which is better for coding, Gemma 4 31B or GPT-5.6 Sol?

Which is better for agentic tasks, Gemma 4 31B or GPT-5.6 Sol?

Which costs less, Gemma 4 31B or GPT-5.6 Sol?

Which has the larger context window, Gemma 4 31B or GPT-5.6 Sol?

Related comparisons

Watch Gemma 4 31B vs GPT-5.6 Sol