Model comparison

Gemma 4 E2B 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 E2B

Google

41.1/100

Estimated · Public rank #175

90% interval 29.6–52.7

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 41.15, and the 90% score intervals do not overlap.

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
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
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. Gemma 4 E2B does not fit this workload in one request. Gemma 4 E2B has no comparable published API token rate.
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.

1 Gemma 4 E2B only

1 Shared

23 GPT-5.6 Sol only

Shared results: 1
Gemma 4 E2B only: 1
GPT-5.6 Sol only: 23
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

Gemma 4 E2B: 56.9
GPT-5.6 Sol: 94.6
Weighted basis: 2 vs 1 rows
Reading: Directional only

Agentic

Not comparable

Gemma 4 E2B: Not measured
GPT-5.6 Sol: 92.0
Weighted basis: 0 vs 2 rows
Reading: Not comparable

Coding

Not comparable

Gemma 4 E2B: Not measured
GPT-5.6 Sol: 64.6
Weighted basis: 0 vs 1 rows
Reading: Not comparable

Reasoning

Not comparable

Gemma 4 E2B: Not measured
GPT-5.6 Sol: 92.5
Weighted basis: 0 vs 1 rows
Reading: Not comparable

Math

Not comparable

Gemma 4 E2B: Not measured
GPT-5.6 Sol: 87.5
Weighted basis: 0 vs 2 rows
Reading: Not comparable

Multilingual

Not comparable

Gemma 4 E2B: Not measured
GPT-5.6 Sol: Not measured
Weighted basis: 0 vs 0 rows
Reading: Not comparable

Multimodal

Not comparable

Gemma 4 E2B: Not measured
GPT-5.6 Sol: 83.0
Weighted basis: 0 vs 1 rows
Reading: Not comparable

Instruction following

Not comparable

Gemma 4 E2B: 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 E2B and GPT-5.6 Sol
Category	Gemma 4 E2B	GPT-5.6 Sol	Weighted basis	Reading
Knowledge	56.9	94.6	Directional only2 vs 1 rows	Directional only
Agentic	Not measured	92.0	Not comparable0 vs 2 rows	Not comparable
Coding	Not measured	64.6	Not comparable0 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
Multimodal	Not measured	83.0	Not comparable0 vs 1 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 E2B: 43.4%GPT-5.6 Sol: 94.6%Normalized gap 51.2Gemma 4 E2B 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 E2B: Self-hosted; infrastructure cost varies; Fits in one request
GPT-5.6 Sol: $0.02; Fits in one request

Gemma 4 E2B has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Gemma 4 E2B: Self-hosted; infrastructure cost varies; Fits in one request
GPT-5.6 Sol: $0.34; Fits in one request

Gemma 4 E2B has no comparable published API token rate.

Cache-heavy agent loop

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

Gemma 4 E2B: Self-hosted; infrastructure cost varies; Does not fit in one request; Cached-input rate unavailable
GPT-5.6 Sol: $0.5; Fits in one request

Gemma 4 E2B does not fit this workload in one request. Gemma 4 E2B 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 E2BGPT-5.6 Sol

Context window

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

Gemma 4 E2B

128K

Google Gemma 4 model documentation

GPT-5.6 Sol

1.05M

OpenAI model catalog

Gemma 4 E2B

Reasoning

GPT-5.6 Sol

Reasoning

Weight access

Gemma 4 E2B

Open Weight

GPT-5.6 Sol

Proprietary

License

Gemma 4 E2B

Open Weight

GPT-5.6 Sol

Proprietary

Release date

Gemma 4 E2B

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 41.15, 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.

Benchmark evidence

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

Browse raw public benchmark evidence25 rows

Agentic

Terminal-Bench 2.0
Gemma 4 E2B—
GPT-5.6 Sol91.9%
Source
Not directly comparable
BrowseComp
Gemma 4 E2B—
GPT-5.6 Sol92.2%
Source
Not directly comparable
OSWorld 2.0
Gemma 4 E2B—
GPT-5.6 Sol62.6%
Source
Not directly comparable
CyberGym
Gemma 4 E2B—
GPT-5.6 Sol84.5%
Source
Not directly comparable
ExploitGym
Gemma 4 E2B—
GPT-5.6 Sol33.7%
Source
Not directly comparable
Toolathlon
Gemma 4 E2B—
GPT-5.6 Sol58%
Source
Not directly comparable

Coding

SWE-bench Pro
Gemma 4 E2B—
GPT-5.6 Sol64.6%
Source
Not directly comparable
Terminal-Bench 2.0
Gemma 4 E2B—
GPT-5.6 Sol91.9%
Source
Not directly comparable
deepSwe
Gemma 4 E2B—
GPT-5.6 Sol72.7%
Source
Not directly comparable
FrontierCode 1.1 Extended
Gemma 4 E2B—
GPT-5.6 Sol60.6%
Source
Not directly comparable
cursorBench32
Gemma 4 E2B—
GPT-5.6 Sol67.2%
Source
Not directly comparable
VulcanBench v3
Gemma 4 E2B—
GPT-5.6 Sol87.0%
Source
Not directly comparable

Reasoning

ARC-AGI-2
Gemma 4 E2B—
GPT-5.6 Sol92.5%
Source
Not directly comparable
ARC-AGI-3
Gemma 4 E2B—
GPT-5.6 Sol7.8%
Source
Not directly comparable
GeneBench-Pro
Gemma 4 E2B—
GPT-5.6 Sol28.7%
Source
Not directly comparable

Knowledge

GPQA
Gemma 4 E2B43.4%
Source
GPT-5.6 Sol94.6%
Source
GPT-5.6 Sol leads this result
MMLU-Pro
Gemma 4 E2B60%
Source
GPT-5.6 Sol—
Not directly comparable
GPQA-D
Gemma 4 E2B—
GPT-5.6 Sol94.6%
Source
Not directly comparable
HealthBench Professional
Gemma 4 E2B—
GPT-5.6 Sol60.5%
Source
Not directly comparable
HealthBench Hard
Gemma 4 E2B—
GPT-5.6 Sol33.1%
Source
Not directly comparable

Math

FrontierMath (legacy)
Gemma 4 E2B—
GPT-5.6 Sol89%
Source
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Gemma 4 E2B—
GPT-5.6 Sol89.000%
Source
Not directly comparable
FrontierMath v2 (Tier 4)
Gemma 4 E2B—
GPT-5.6 Sol83.000%
Source
Not directly comparable

Multimodal

MMMU-Pro
Gemma 4 E2B—
GPT-5.6 Sol83%
Source
Not directly comparable
MMMU-Pro w/ Python
Gemma 4 E2B—
GPT-5.6 Sol84.6%
Source
Not directly comparable

Frequently asked questions

Which is better, Gemma 4 E2B or GPT-5.6 Sol?

GPT-5.6 Sol has the higher public score, 81.48 versus 41.15, 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 E2B 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 E2B 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 E2B 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 E2B or GPT-5.6 Sol?

GPT-5.6 Sol has the larger documented context window: 1.05M, compared with 128K.

Related comparisons

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Last updated August 10, 2026

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Gemma 4 E2B 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

Knowledge

Agentic

Coding

Reasoning

Math

Multilingual

Multimodal

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

Benchmark evidence

Agentic

Coding

Reasoning

Knowledge

Math

Multimodal

Frequently asked questions

Which is better, Gemma 4 E2B or GPT-5.6 Sol?

Which is better for coding, Gemma 4 E2B or GPT-5.6 Sol?

Which is better for agentic tasks, Gemma 4 E2B or GPT-5.6 Sol?

Which costs less, Gemma 4 E2B or GPT-5.6 Sol?

Which has the larger context window, Gemma 4 E2B or GPT-5.6 Sol?

Related comparisons

Watch Gemma 4 E2B vs GPT-5.6 Sol