Gemma 4 12B vs GPT-6.1 Terra

Updated October 26, 2026. Rank says GPT-6.1 Terra is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

GPT-6.1 Terra has the higher public point estimate, 75.87 versus 35.99. Their conditional score ranges do not overlap. These ranges do not establish rank confidence. 1 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 12B

Google

35.99/100

Estimated · Public rank #158

Conditional range 21.6–50.3

Model B

GPT-6.1 Terra

OpenAI

75.87/100

Estimated · Public rank #10

Conditional range 61.5–90.2

Shared results: 1
Gemma 4 12B only: 11
GPT-6.1 Terra only: 9
Like-for-like categories: 2 / 8

Estimated: Gemma 4 12B and GPT-6.1 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.

Long documents
Prompts that approach the documented context limit
GPT-6.1 Terra
GPT-6.1 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
GPT-6.1 Terra is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Gemma 4 12B 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.

23.2Gemma 4 12B—GPT-6.1 Terra

Not comparable · BenchAlign v5.8

The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.

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.

MMMU-ProMultimodal
Normalized gap 16.4
Gemma 4 12B:69.1%
GPT-6.1 Terra:85.5%
Gemma 4 12B source GPT-6.1 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, 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.

Multimodal

Like-for-like

Gemma 4 12B: 60.0; #47/58
GPT-6.1 Terra: 91.6; #4/58
Basis: Provisional lane · 1 vs 1 weighted rows
Reading: GPT-6.1 Terra leads

Knowledge

Like-for-like

Gemma 4 12B: 34.9; Supported · #137/188
GPT-6.1 Terra: 74.9; Supported · #12/188
Basis: BenchAlign v5.8 lane · 5 vs 5 public rows
Reading: GPT-6.1 Terra leads

Agentic

Not comparable

Gemma 4 12B: Not ranked
GPT-6.1 Terra: 72.4; Estimated · #8/133
Basis: BenchAlign v5.8 lane · 0 vs 4 public rows
Reading: Not comparable

Coding

Not comparable

Gemma 4 12B: 23.2; Estimated · #117/154
GPT-6.1 Terra: Not ranked
Basis: BenchAlign v5.8 lane · 1 vs 0 public rows
Reading: Not comparable

Reasoning

Not comparable

Gemma 4 12B: 51.8; Unranked · 3 rankable rows
GPT-6.1 Terra: 81.8; Unranked · 2 rankable rows
Basis: Provisional lane · 1 vs 0 weighted rows
Reading: Not comparable

Multilingual

Not comparable

Gemma 4 12B: Not ranked
GPT-6.1 Terra: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

Gemma 4 12B: 90.1; #28/155
GPT-6.1 Terra: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

Gemma 4 12B: 61.3; Unranked · 1 rankable row
GPT-6.1 Terra: Not ranked
Basis: Provisional lane · 1 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Gemma 4 12B and GPT-6.1 Terra
Category	Gemma 4 12B	GPT-6.1 Terra	Basis	Reading
Multimodal	60.0#47/58	91.6#4/58	Like-for-likeProvisional lane · 1 vs 1 weighted rows	GPT-6.1 Terra leads
Knowledge	34.9Supported · #137/188	74.9Supported · #12/188	Like-for-likeBenchAlign v5.8 lane · 5 vs 5 public rows	GPT-6.1 Terra leads
Agentic	Not ranked	72.4Estimated · #8/133	Not comparableBenchAlign v5.8 lane · 0 vs 4 public rows	Not comparable
Coding	23.2Estimated · #117/154	Not ranked	Not comparableBenchAlign v5.8 lane · 1 vs 0 public rows	Not comparable
Reasoning	51.8Unranked · 3 rankable rows	81.8Unranked · 2 rankable rows	Not comparableProvisional lane · 1 vs 0 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Instruction following	90.1#28/155	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	61.3Unranked · 1 rankable row	Not ranked	Not comparableProvisional lane · 1 vs 0 weighted rows	Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, 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 12B: API rate not published; Fits in one request
GPT-6.1 Terra: $0.0035; Fits in one request

Gemma 4 12B has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Gemma 4 12B: API rate not published; Fits in one request
GPT-6.1 Terra: $0.065; Fits in one request

Gemma 4 12B has no comparable published API token rate.

Cache-heavy agent loop

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

Gemma 4 12B: API rate not published; Fits in one request; Cached-input rate unavailable
GPT-6.1 Terra: $0.08; Fits in one request

Gemma 4 12B 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 12BGPT-6.1 Terra

Context window

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

Gemma 4 12B

256K

Google Gemma 4 model documentation

GPT-6.1 Terra

1.05M

OpenAI GPT-6.1 Terra model documentation

Gemma 4 12B

Reasoning

GPT-6.1 Terra

Reasoning

Weight access

Gemma 4 12B

Open Weight

GPT-6.1 Terra

Proprietary

License

Gemma 4 12B

Open Weight

GPT-6.1 Terra

Proprietary

Release date

Gemma 4 12B

2026-06-03

GPT-6.1 Terra

2026-10-26

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-6.1 Terra has the higher public point estimate, 75.87 versus 35.99. 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-6.1 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 12B or GPT-6.1 Terra?

GPT-6.1 Terra has the higher public point estimate, 75.87 versus 35.99. 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 12B or GPT-6.1 Terra?

GPT-6.1 Terra is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, Gemma 4 12B or GPT-6.1 Terra?

Gemma 4 12B is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Gemma 4 12B or GPT-6.1 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 12B or GPT-6.1 Terra?

GPT-6.1 Terra has the larger documented context window: 1.05M, compared with 256K.

Benchmark evidence

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

Browse raw public benchmark evidence21 rows

Agentic

Terminal-Bench 4.0
Gemma 4 12B—
GPT-6.1 Terra50.30%
Source
Not directly comparable
Terminal-Bench-Science 0.1
Gemma 4 12B—
GPT-6.1 Terra58.3%
Source
Not directly comparable
Agents' Last Exam
Gemma 4 12B—
GPT-6.1 Terra60.0%
Source
Not directly comparable
OSWorld 2.1 Offline
Gemma 4 12B—
GPT-6.1 Terra74.2%
Source
Not directly comparable

Coding

LiveCodeBench v6
Gemma 4 12B72.0%
Source
GPT-6.1 Terra—
Not directly comparable

Reasoning

MRCRv2
Gemma 4 12B43.4%
Source
GPT-6.1 Terra—
Not directly comparable

Multimodal

ExtractBench (LlamaIndex)
Gemma 4 12B45.39%
Source
GPT-6.1 Terra—
Not directly comparable
MMMU-Pro
Gemma 4 12B69.1%
Source
GPT-6.1 Terra85.5%
Source
GPT-6.1 Terra leads this result
MathVision
Gemma 4 12B79.7%
Source
GPT-6.1 Terra—
Not directly comparable
MedXpertQA (MM)
Gemma 4 12B48.7%
Source
GPT-6.1 Terra—
Not directly comparable

Knowledge

GPQA
Gemma 4 12B78.8%
Source
GPT-6.1 Terra—
Not directly comparable
GPQA-D
Gemma 4 12B78.8%
Source
GPT-6.1 Terra—
Not directly comparable
MMLU-Pro
Gemma 4 12B77.2%
Source
GPT-6.1 Terra—
Not directly comparable
HLE w/o tools
Gemma 4 12B5.2%
Source
GPT-6.1 Terra—
Not directly comparable
MMMLU
Gemma 4 12B83.4%
Source
GPT-6.1 Terra—
Not directly comparable
HealthBench (raw)
Gemma 4 12B—
GPT-6.1 Terra51.1%
Source
Not directly comparable
HealthBench (length-adjusted)
Gemma 4 12B—
GPT-6.1 Terra55.1%
Source
Not directly comparable
HealthBench Professional
Gemma 4 12B—
GPT-6.1 Terra62.7%
Source
Not directly comparable
HealthBench Professional (raw)
Gemma 4 12B—
GPT-6.1 Terra64.2%
Source
Not directly comparable
HealthBench Hard
Gemma 4 12B—
GPT-6.1 Terra31.4%
Source
Not directly comparable

Math

AIME26
Gemma 4 12B77.5%
Source
GPT-6.1 Terra—
Not directly comparable

21 public results · 1 shared

Related comparisons

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Last updated October 26, 2026

Gemma 4 12B vs GPT-6.1 Terra

Decision reading

Which one for your work

Long documents

Coding work

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

Multimodal

Knowledge

Agentic

Coding

Reasoning

Multilingual

Instruction following

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 12B or GPT-6.1 Terra?

Which is better for coding, Gemma 4 12B or GPT-6.1 Terra?

Which is better for agentic tasks, Gemma 4 12B or GPT-6.1 Terra?

Which costs less, Gemma 4 12B or GPT-6.1 Terra?

Which has the larger context window, Gemma 4 12B or GPT-6.1 Terra?

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Math

Related comparisons

Watch Gemma 4 12B vs GPT-6.1 Terra