Gemma 4 26B A4B vs GPT-5.6 Terra

Updated November 9, 2026. Rank says GPT-5.6 Terra is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

GPT-5.6 Terra has the higher public point estimate, 69.42 versus 42.14. Their conditional score ranges do not overlap. These ranges do not establish rank confidence. 4 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

Gemma 4 26B A4B

Google

42.14/100

Estimated · Public rank #132

Conditional range 32.4–51.9

Model B

GPT-5.6 Terra

OpenAI

69.42/100

Supported · Public rank #16

90% interval 65.1–73.8

Shared results: 4
Gemma 4 26B A4B only: 6
GPT-5.6 Terra only: 34
Like-for-like categories: 3 / 8

Estimated: Gemma 4 26B A4B · Supported: GPT-5.6 Terra. Conditional ranges do not establish rank confidence.How the comparison works

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.

Coding work
Code generation, repair, and software-engineering tasks
GPT-5.6 Terra
GPT-5.6 Terra has the higher public coding point estimate, 61.8 to 32.1, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Confidence: limited
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

Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Gemma 4 26B A4B is not ranked on the public lane for agentic, so no winner is named for agentic.
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

Which one for a specific job

Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.

Pick the job you would use them for

The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.

32.1Gemma 4 26B A4B61.8GPT-5.6 Terra

Like-for-like · BenchAlign v5.8

GPT-5.6 Terra has the higher coding point estimate. Conditional score ranges do not establish rank confidence.

Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.

Open the coding leaderboard Shortlist models for this job

Same basis rules as the category table below

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.

GPQAKnowledge
Normalized gap 10.6
Gemma 4 26B A4B:82.3%
GPT-5.6 Terra:92.9%
Gemma 4 26B A4B source GPT-5.6 Terra source
MMMU-ProMultimodal
Normalized gap 6.9
Gemma 4 26B A4B:73.8%
GPT-5.6 Terra:80.7%
Gemma 4 26B A4B source GPT-5.6 Terra source

Bars run 0–100 on each benchmark’s normalized display scale

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.8 lane for agentic, coding, reasoning, multimodal, 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.

Coding

Like-for-like

Gemma 4 26B A4B: 32.1; Supported · #100/158
GPT-5.6 Terra: 61.8; Supported · #18/158
Basis: BenchAlign v5.8 lane · 2 vs 9 public rows
Reading: GPT-5.6 Terra leads

Reasoning

Like-for-like

Gemma 4 26B A4B: 35.3; Supported · #102/157
GPT-5.6 Terra: 69.1; Supported · #18/157
Basis: BenchAlign v5.8 lane · 1 vs 3 public rows
Reading: GPT-5.6 Terra leads

Multimodal

Like-for-like

Gemma 4 26B A4B: 48.7; Supported · #59/67
GPT-5.6 Terra: 68.9; Supported · #15/67
Basis: BenchAlign v5.8 lane · 3 vs 4 public rows
Reading: GPT-5.6 Terra leads

Knowledge

Directional only

Gemma 4 26B A4B: 41.4; Estimated · #110/179
GPT-5.6 Terra: 68.2; Supported · #20/179
Basis: BenchAlign v5.8 lane · 4 vs 8 public rows
Reading: Directional only

Instruction following

Directional only

Gemma 4 26B A4B: 88.7; #37/157
GPT-5.6 Terra: 87.1; #42/157
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Directional only

Agentic

Not comparable

Gemma 4 26B A4B: Not ranked
GPT-5.6 Terra: 60.4; Supported · #26/132
Basis: BenchAlign v5.8 lane · 0 vs 11 public rows
Reading: Not comparable

Multilingual

Not comparable

Gemma 4 26B A4B: Not ranked
GPT-5.6 Terra: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

Gemma 4 26B A4B: Not ranked
GPT-5.6 Terra: 86.6; Unranked · 3 rankable rows
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Gemma 4 26B A4B and GPT-5.6 Terra
Category	Gemma 4 26B A4B	GPT-5.6 Terra	Basis	Reading
Coding	32.1Supported · #100/158	61.8Supported · #18/158	Like-for-likeBenchAlign v5.8 lane · 2 vs 9 public rows	GPT-5.6 Terra leads
Reasoning	35.3Supported · #102/157	69.1Supported · #18/157	Like-for-likeBenchAlign v5.8 lane · 1 vs 3 public rows	GPT-5.6 Terra leads
Multimodal	48.7Supported · #59/67	68.9Supported · #15/67	Like-for-likeBenchAlign v5.8 lane · 3 vs 4 public rows	GPT-5.6 Terra leads
Knowledge	41.4Estimated · #110/179	68.2Supported · #20/179	Directional onlyBenchAlign v5.8 lane · 4 vs 8 public rows	Directional only
Instruction following	88.7#37/157	87.1#42/157	Directional onlyProvisional lane · 0 vs 0 weighted rows	Directional only
Agentic	Not ranked	60.4Supported · #26/132	Not comparableBenchAlign v5.8 lane · 0 vs 11 public rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	Not ranked	86.6Unranked · 3 rankable rows	Not comparableProvisional lane · 0 vs 2 weighted rows	Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, reasoning, multimodal, and knowledge denominators (BenchAlign v5.8) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

Supported evidence per lane · bars run 0–100Methodology

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 26B A4B: Self-hosted; infrastructure cost varies; Fits in one request
GPT-5.6 Terra: $0.008; Fits in one request

Gemma 4 26B A4B has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Gemma 4 26B A4B: Self-hosted; infrastructure cost varies; Fits in one request
GPT-5.6 Terra: $0.136; Fits in one request

Gemma 4 26B A4B has no comparable published API token rate.

Cache-heavy agent loop

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

Gemma 4 26B A4B: Self-hosted; infrastructure cost varies; Fits in one request; Cached-input rate unavailable
GPT-5.6 Terra: $0.2; Fits in one request

Gemma 4 26B A4B has no comparable published API token rate.

Cached input falls back to the list input rate only where a cached rate is unpublished

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

SpecificationGemma 4 26B A4BGPT-5.6 Terra

Context window

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

Gemma 4 26B A4B

256K

Google Gemma 4 model documentation

GPT-5.6 Terra

1.05M

OpenAI model catalog

API model ID

Gemma 4 26B A4B

gemma-4-26b-a4b-it

Google Gemma Gemini API guide

GPT-5.6 Terra

gpt-5.6-terra

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 26B A4B

No comparable hosted API rate

GPT-5.6 Terra

$0.2 per 1M cached input tokens

OpenAI pricing

Documented inputs

Gemma 4 26B A4B

text, image

Google Gemma 4 model documentation

GPT-5.6 Terra

text, image

OpenAI model catalog

Documented outputs

Gemma 4 26B A4B

text

Google Gemma 4 model documentation

GPT-5.6 Terra

text

OpenAI model catalog

Provider availability

Gemma 4 26B A4B

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

Google Gemma Gemini API guide

GPT-5.6 Terra

Generally Available · OpenAI Responses API

OpenAI model catalog

Reasoning profile

Gemma 4 26B A4B

Reasoning

GPT-5.6 Terra

Reasoning

Weight access

Gemma 4 26B A4B

Open Weight

GPT-5.6 Terra

Proprietary

License

Gemma 4 26B A4B

Open Weight

GPT-5.6 Terra

Proprietary

Release date

Gemma 4 26B A4B

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 point estimate, 69.42 versus 42.14. Their conditional score ranges do not overlap. These ranges do not establish rank confidence.
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.

Questions

Which is better, Gemma 4 26B A4B or GPT-5.6 Terra?

GPT-5.6 Terra has the higher public point estimate, 69.42 versus 42.14. Their conditional score ranges do not overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Gemma 4 26B A4B or GPT-5.6 Terra?

GPT-5.6 Terra has the higher public coding point estimate, 61.8 to 32.1, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

Which is better for agentic tasks, Gemma 4 26B A4B or GPT-5.6 Terra?

Gemma 4 26B A4B is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Gemma 4 26B A4B 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 26B A4B or GPT-5.6 Terra?

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

Self-host vs API cost

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

Gemma 4 26B A4B

API / mo$0

Self-host / mo$298

Break-even—

GPT-5.6 Terra

API / mo$10,500

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 evidence44 rows

Agentic

Terminal-Bench 4.1
Gemma 4 26B A4B—
GPT-5.6 Terra21.50%
Source
Not directly comparable
Terminal-Bench 3.0
Gemma 4 26B A4B—
GPT-5.6 Terra20.8%
Source
Not directly comparable
Terminal-Bench 2.1
Gemma 4 26B A4B—
GPT-5.6 Terra87.4%
Source
Not directly comparable
BrowseComp
Gemma 4 26B A4B—
GPT-5.6 Terra87.5%
Source
Not directly comparable
OSWorld 2.0
Gemma 4 26B A4B—
GPT-5.6 Terra50.2%
Source
Not directly comparable
CyberGym
Gemma 4 26B A4B—
GPT-5.6 Terra81.8%
Source
Not directly comparable
ExploitGym
Gemma 4 26B A4B—
GPT-5.6 Terra23.2%
Source
Not directly comparable
Toolathlon
Gemma 4 26B A4B—
GPT-5.6 Terra53.1%
Source
Not directly comparable
Terminal-Bench 2.1 (Vals)
Gemma 4 26B A4B—
GPT-5.6 Terra77.5%
Source
Not directly comparable
ApprenticeBench
Gemma 4 26B A4B—
GPT-5.6 Terra16%
Source
Not directly comparable
MazeBench
Gemma 4 26B A4B—
GPT-5.6 Terra6%
Source
Not directly comparable

Coding

ScarfBench
Gemma 4 26B A4B24.4%
Source
GPT-5.6 Terra—
Not directly comparable
LiveCodeBench v6
Gemma 4 26B A4B77.1%
Source
GPT-5.6 Terra—
Not directly comparable
SWE-bench Pro
Gemma 4 26B A4B—
GPT-5.6 Terra63.4%
Source
Not directly comparable
Terminal-Bench 2.1
Gemma 4 26B A4B—
GPT-5.6 Terra87.4%
Source
Not directly comparable
DeepSWE
Gemma 4 26B A4B—
GPT-5.6 Terra69.6%
Source
Not directly comparable
FrontierCode 1.1 Extended
Gemma 4 26B A4B—
GPT-5.6 Terra55.8%
Source
Not directly comparable
CursorBench 3.2
Gemma 4 26B A4B—
GPT-5.6 Terra64.9%
Source
Not directly comparable
VulcanBench v3
Gemma 4 26B A4B—
GPT-5.6 Terra87.0%
Source
Not directly comparable
LiveCodeBench (Vals)
Gemma 4 26B A4B—
GPT-5.6 Terra85.9%
Source
Not directly comparable
SWE-bench (Vals)
Gemma 4 26B A4B—
GPT-5.6 Terra95.4%
Source
Not directly comparable
CursorBench 4.0
Gemma 4 26B A4B—
GPT-5.6 Terra41.3%
Source
Not directly comparable

Reasoning

MRCRv2
Gemma 4 26B A4B44.1%
Source
GPT-5.6 Terra—
Not directly comparable
ARC-AGI-2
Gemma 4 26B A4B—
GPT-5.6 Terra83.9%
Source
Not directly comparable
ARC-AGI-3
Gemma 4 26B A4B—
GPT-5.6 Terra0.8%
Source
Not directly comparable
ARC-AGI-1
Gemma 4 26B A4B—
GPT-5.6 Terra96.50%
Source
Not directly comparable

Multimodal

ExtractBench (LlamaIndex)
Shared source
Gemma 4 26B A4B70.27%
GPT-5.6 Terra92.26%
GPT-5.6 Terra leads this result
ParseBench
Shared source
Gemma 4 26B A4B58.50%
GPT-5.6 Terra67.06%
GPT-5.6 Terra leads this result
MMMU-Pro
Gemma 4 26B A4B73.8%
Source
GPT-5.6 Terra80.7%
Source
GPT-5.6 Terra leads this result
MMMU-Pro w/ Python
Gemma 4 26B A4B—
GPT-5.6 Terra82%
Source
Not directly comparable

Knowledge

GPQA
Gemma 4 26B A4B82.3%
Source
GPT-5.6 Terra92.9%
Source
GPT-5.6 Terra leads this result
MMLU-Pro
Gemma 4 26B A4B82.6%
Source
GPT-5.6 Terra—
Not directly comparable
HLE
Gemma 4 26B A4B17.2%
Source
GPT-5.6 Terra—
Not directly comparable
HLE w/o tools
Gemma 4 26B A4B8.7%
Source
GPT-5.6 Terra—
Not directly comparable
GPQA-D
Gemma 4 26B A4B—
GPT-5.6 Terra92.9%
Source
Not directly comparable
HLE-Verified
Gemma 4 26B A4B—
GPT-5.6 Terra51.1%
Source
Not directly comparable
LABBench2
Gemma 4 26B A4B—
GPT-5.6 Terra81.2%
Source
Not directly comparable
HealthBench Professional
Gemma 4 26B A4B—
GPT-5.6 Terra57.7%
Source
Not directly comparable
HealthBench Hard
Gemma 4 26B A4B—
GPT-5.6 Terra32.7%
Source
Not directly comparable
GPQA Diamond (Vals)
Gemma 4 26B A4B—
GPT-5.6 Terra90.9%
Source
Not directly comparable
MMLU-Pro (Vals)
Gemma 4 26B A4B—
GPT-5.6 Terra86.7%
Source
Not directly comparable

Math

FrontierMath (legacy)
Gemma 4 26B A4B—
GPT-5.6 Terra84.9%
Source
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Gemma 4 26B A4B—
GPT-5.6 Terra84.900%
Source
Not directly comparable
FrontierMath v2 (Tier 4)
Gemma 4 26B A4B—
GPT-5.6 Terra68.300%
Source
Not directly comparable

44 public results · 4 shared

Related comparisons

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Last updated November 9, 2026

Gemma 4 26B A4B vs GPT-5.6 Terra

Decision reading

Which one for your work

Coding work

Long documents

Agentic work

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Which one for a specific job

What is actually comparable

Shape of the matched evidence

Category results, on a stated basis

Coding

Reasoning

Multimodal

Knowledge

Instruction following

Agentic

Multilingual

Math

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

If you already use one of these models

Questions

Which is better, Gemma 4 26B A4B or GPT-5.6 Terra?

Which is better for coding, Gemma 4 26B A4B or GPT-5.6 Terra?

Which is better for agentic tasks, Gemma 4 26B A4B or GPT-5.6 Terra?

Which costs less, Gemma 4 26B A4B or GPT-5.6 Terra?

Which has the larger context window, Gemma 4 26B A4B or GPT-5.6 Terra?

Self-host vs API cost

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Math

Related comparisons

Watch Gemma 4 26B A4B vs GPT-5.6 Terra