Model A

Google

39.27/100

Estimated · Public rank #191

90% interval 27.8–50.8

Gemma 4 E2B vs GPT-5.6 Terra

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

Model B

GPT-5.6 Terra

OpenAI

71.13/100

Estimated · Public rank #12

90% interval 65.4–76.9

Decision reading

GPT-5.6 Terra has the higher public score, 71.13 versus 39.27, and the 90% score intervals do not overlap.

1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

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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 Terra
GPT-5.6 Terra 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
Gemma 4 E2B is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Gemma 4 E2B is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
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

30 GPT-5.6 Terra only

Shared results: 1
Gemma 4 E2B only: 1
GPT-5.6 Terra only: 30
Like-for-like categories: 0 / 8

4 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.

Agentic

Directional only

Gemma 4 E2B: 42.9; Estimated · #103/152
GPT-5.6 Terra: 60.2; Supported · #20/152
Basis: BenchAlign lane · 0 vs 8 public rows
Reading: Directional only

Coding

Directional only

Gemma 4 E2B: 38.6; Estimated · #122/151
GPT-5.6 Terra: 67.0; Supported · #8/151
Basis: BenchAlign lane · 0 vs 8 public rows
Reading: Directional only

Knowledge

Directional only

Gemma 4 E2B: 36.4; Estimated · #151/183
GPT-5.6 Terra: 69.2; Supported · #16/183
Basis: BenchAlign lane · 2 vs 8 public rows
Reading: Directional only

Instruction following

Directional only

Gemma 4 E2B: 44.0; #97/123
GPT-5.6 Terra: 87.1; #34/123
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Directional only

Reasoning

Not comparable

Gemma 4 E2B: 32.2; Unranked · 2 rankable rows
GPT-5.6 Terra: 63.7; #14/20
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Not comparable

Math

Not comparable

Gemma 4 E2B: Not ranked
GPT-5.6 Terra: 97.0; Unranked · 3 rankable rows
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Not comparable

Multilingual

Not comparable

Gemma 4 E2B: Not ranked
GPT-5.6 Terra: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

Gemma 4 E2B: 25.8; Unranked · 1 rankable row
GPT-5.6 Terra: 77.1; #15/48
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Gemma 4 E2B and GPT-5.6 Terra
Category	Gemma 4 E2B	GPT-5.6 Terra	Basis	Reading
Agentic	42.9Estimated · #103/152	60.2Supported · #20/152	Directional onlyBenchAlign lane · 0 vs 8 public rows	Directional only
Coding	38.6Estimated · #122/151	67.0Supported · #8/151	Directional onlyBenchAlign lane · 0 vs 8 public rows	Directional only
Knowledge	36.4Estimated · #151/183	69.2Supported · #16/183	Directional onlyBenchAlign lane · 2 vs 8 public rows	Directional only
Instruction following	44.0#97/123	87.1#34/123	Directional onlyProvisional lane · 0 vs 0 weighted rows	Directional only
Reasoning	32.2Unranked · 2 rankable rows	63.7#14/20	Not comparableProvisional lane · 0 vs 2 weighted rows	Not comparable
Math	Not ranked	97.0Unranked · 3 rankable rows	Not comparableProvisional lane · 0 vs 2 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Multimodal	25.8Unranked · 1 rankable row	77.1#15/48	Not comparableProvisional lane · 0 vs 1 weighted rows	Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

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 Terra: 92.9%Normalized gap 49.5Gemma 4 E2B source GPT-5.6 Terra 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 Terra: $0.01; 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 Terra: $0.17; 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 Terra: $0.25; 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 Terra

Context window

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

Gemma 4 E2B

128K

Google Gemma 4 model documentation

GPT-5.6 Terra

1.05M

OpenAI model catalog

Gemma 4 E2B

Reasoning

GPT-5.6 Terra

Reasoning

Weight access

Gemma 4 E2B

Open Weight

GPT-5.6 Terra

Proprietary

License

Gemma 4 E2B

Open Weight

GPT-5.6 Terra

Proprietary

Release date

Gemma 4 E2B

2026-04-02

GPT-5.6 Terra

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 Terra has the higher public score, 71.13 versus 39.27, 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 Terra 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 evidence32 rows

Agentic

Terminal-Bench 3.0
Gemma 4 E2B—
GPT-5.6 Terra20.8%
Source
Not directly comparable
Terminal-Bench 2.0
Gemma 4 E2B—
GPT-5.6 Terra87.4%
Source
Not directly comparable
BrowseComp
Gemma 4 E2B—
GPT-5.6 Terra87.5%
Source
Not directly comparable
OSWorld 2.0
Gemma 4 E2B—
GPT-5.6 Terra50.2%
Source
Not directly comparable
CyberGym
Gemma 4 E2B—
GPT-5.6 Terra81.8%
Source
Not directly comparable
ExploitGym
Gemma 4 E2B—
GPT-5.6 Terra23.2%
Source
Not directly comparable
Toolathlon
Gemma 4 E2B—
GPT-5.6 Terra53.1%
Source
Not directly comparable
Terminal-Bench 2.1 (Vals)
Gemma 4 E2B—
GPT-5.6 Terra77.5%
Source
Not directly comparable

Coding

SWE-bench Pro
Gemma 4 E2B—
GPT-5.6 Terra63.4%
Source
Not directly comparable
Terminal-Bench 2.0
Gemma 4 E2B—
GPT-5.6 Terra87.4%
Source
Not directly comparable
DeepSWE
Gemma 4 E2B—
GPT-5.6 Terra69.6%
Source
Not directly comparable
FrontierCode 1.1 Extended
Gemma 4 E2B—
GPT-5.6 Terra55.8%
Source
Not directly comparable
cursorBench32
Gemma 4 E2B—
GPT-5.6 Terra64.9%
Source
Not directly comparable
VulcanBench v3
Gemma 4 E2B—
GPT-5.6 Terra87.0%
Source
Not directly comparable
LiveCodeBench (Vals)
Gemma 4 E2B—
GPT-5.6 Terra85.9%
Source
Not directly comparable
SWE-bench (Vals)
Gemma 4 E2B—
GPT-5.6 Terra95.4%
Source
Not directly comparable

Reasoning

ARC-AGI-2
Gemma 4 E2B—
GPT-5.6 Terra83.9%
Source
Not directly comparable
ARC-AGI-3
Gemma 4 E2B—
GPT-5.6 Terra0.8%
Source
Not directly comparable

Knowledge

GPQA
Gemma 4 E2B43.4%
Source
GPT-5.6 Terra92.9%
Source
GPT-5.6 Terra leads this result
MMLU-Pro
Gemma 4 E2B60%
Source
GPT-5.6 Terra—
Not directly comparable
GPQA-D
Gemma 4 E2B—
GPT-5.6 Terra92.9%
Source
Not directly comparable
HLE-Verified
Gemma 4 E2B—
GPT-5.6 Terra51.1%
Source
Not directly comparable
LABBench2
Gemma 4 E2B—
GPT-5.6 Terra81.2%
Source
Not directly comparable
HealthBench Professional
Gemma 4 E2B—
GPT-5.6 Terra57.7%
Source
Not directly comparable
HealthBench Hard
Gemma 4 E2B—
GPT-5.6 Terra32.7%
Source
Not directly comparable
GPQA Diamond (Vals)
Gemma 4 E2B—
GPT-5.6 Terra90.9%
Source
Not directly comparable
MMLU-Pro (Vals)
Gemma 4 E2B—
GPT-5.6 Terra86.7%
Source
Not directly comparable

Math

FrontierMath (legacy)
Gemma 4 E2B—
GPT-5.6 Terra84.9%
Source
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Gemma 4 E2B—
GPT-5.6 Terra84.900%
Source
Not directly comparable
FrontierMath v2 (Tier 4)
Gemma 4 E2B—
GPT-5.6 Terra68.300%
Source
Not directly comparable

Multimodal

MMMU-Pro
Gemma 4 E2B—
GPT-5.6 Terra80.7%
Source
Not directly comparable
MMMU-Pro w/ Python
Gemma 4 E2B—
GPT-5.6 Terra82%
Source
Not directly comparable

Frequently asked questions

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

GPT-5.6 Terra has the higher public score, 71.13 versus 39.27, 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 Terra?

GPT-5.6 Terra scores higher for coding on the public lane, 67 to 38.6. Gemma 4 E2B is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

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

GPT-5.6 Terra scores higher for agentic tasks on the public lane, 60.2 to 42.9. Gemma 4 E2B is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

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

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 Terra?

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

Related comparisons

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Last updated September 10, 2026

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Gemma 4 E2B vs GPT-5.6 Terra

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

Agentic

Coding

Knowledge

Instruction following

Reasoning

Math

Multilingual

Multimodal

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 Terra?

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

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

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

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

Related comparisons

Watch Gemma 4 E2B vs GPT-5.6 Terra