Gemini 3.1 Pro vs Gemma 4 26B A4B

Updated October 1, 2026. Rank says Gemini 3.1 Pro is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

Gemini 3.1 Pro has the higher public point estimate, 65.05 versus 46.88. Their conditional score ranges overlap. These ranges do not establish rank confidence. 2 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

Gemini 3.1 Pro

Google

65.05/100

Estimated · Public rank #35

Conditional range 55.3–74.8

Model B

Gemma 4 26B A4B

Google

46.88/100

Supported · Public rank #104

90% interval 32.3–61.5

Shared results: 2
Gemini 3.1 Pro only: 27
Gemma 4 26B A4B only: 2
Like-for-like categories: 2 / 8

Estimated: Gemini 3.1 Pro · Supported: Gemma 4 26B A4B. 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
Gemini 3.1 Pro
Gemini 3.1 Pro has the higher public coding point estimate, 42.3 to 33.8, 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
Gemini 3.1 Pro
Gemini 3.1 Pro 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.

42.3Gemini 3.1 Pro33.8Gemma 4 26B A4B

Like-for-like · BenchAlign v5.8

Gemini 3.1 Pro 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.

HLE w/o toolsKnowledge
Normalized gap 36.7
Gemini 3.1 Pro:45.4%
Gemma 4 26B A4B:8.7%
Gemini 3.1 Pro source Gemma 4 26B A4B source
MMMU-ProMultimodal
Normalized gap 10.1
Gemini 3.1 Pro:83.9%
Gemma 4 26B A4B:73.8%
Gemini 3.1 Pro source Gemma 4 26B A4B 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.

Coding

Like-for-like

Gemini 3.1 Pro: 42.3; Supported · #61/144
Gemma 4 26B A4B: 33.8; Supported · #86/144
Basis: BenchAlign v5.8 lane · 6 vs 0 public rows
Reading: Gemini 3.1 Pro leads · intervals overlap

Knowledge

Like-for-like

Gemini 3.1 Pro: 66.2; Supported · #22/170
Gemma 4 26B A4B: 36.5; Supported · #108/170
Basis: BenchAlign v5.8 lane · 6 vs 3 public rows
Reading: Gemini 3.1 Pro leads

Multimodal

Directional only

Gemini 3.1 Pro: 80.1; #13/49
Gemma 4 26B A4B: 45.2; #42/49
Basis: Provisional lane · 2 vs 1 weighted rows
Reading: Directional only

Agentic

Not comparable

Gemini 3.1 Pro: 38.4; Supported · #62/119
Gemma 4 26B A4B: Not ranked
Basis: BenchAlign v5.8 lane · 6 vs 0 public rows
Reading: Not comparable

Reasoning

Not comparable

Gemini 3.1 Pro: 54.5; Unranked · 2 rankable rows
Gemma 4 26B A4B: 67.4; Unranked · 2 rankable rows
Basis: Provisional lane · 2 vs 0 weighted rows
Reading: Not comparable

Multilingual

Not comparable

Gemini 3.1 Pro: Not ranked
Gemma 4 26B A4B: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

Gemini 3.1 Pro: Not ranked
Gemma 4 26B A4B: 87.3; #31/124
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

Gemini 3.1 Pro: 54.2; Unranked · 2 rankable rows
Gemma 4 26B A4B: Not ranked
Basis: Provisional lane · 2 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Gemini 3.1 Pro and Gemma 4 26B A4B
Category	Gemini 3.1 Pro	Gemma 4 26B A4B	Basis	Reading
Coding	42.3Supported · #61/144	33.8Supported · #86/144	Like-for-likeBenchAlign v5.8 lane · 6 vs 0 public rows	Gemini 3.1 Pro leads · intervals overlap
Knowledge	66.2Supported · #22/170	36.5Supported · #108/170	Like-for-likeBenchAlign v5.8 lane · 6 vs 3 public rows	Gemini 3.1 Pro leads
Multimodal	80.1#13/49	45.2#42/49	Directional onlyProvisional lane · 2 vs 1 weighted rows	Directional only
Agentic	38.4Supported · #62/119	Not ranked	Not comparableBenchAlign v5.8 lane · 6 vs 0 public rows	Not comparable
Reasoning	54.5Unranked · 2 rankable rows	67.4Unranked · 2 rankable rows	Not comparableProvisional lane · 2 vs 0 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Instruction following	Not ranked	87.3#31/124	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	54.2Unranked · 2 rankable rows	Not ranked	Not comparableProvisional lane · 2 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

Gemini 3.1 Pro: $0.008; Fits in one request
Gemma 4 26B A4B: Self-hosted; infrastructure cost varies; Fits in one request

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

Repository review

50K fresh input + 3K output tokens

Gemini 3.1 Pro: $0.136; Fits in one request
Gemma 4 26B A4B: Self-hosted; infrastructure cost varies; 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

Gemini 3.1 Pro: $0.2; Fits in one request
Gemma 4 26B A4B: Self-hosted; infrastructure cost varies; Fits in one request; Cached-input rate unavailable

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

Reasoning

Weight access

Gemini 3.1 Pro

Proprietary

Gemma 4 26B A4B

Open Weight

License

Gemini 3.1 Pro

Proprietary

Gemma 4 26B A4B

Open Weight

Release date

Gemini 3.1 Pro

2026-02-19

Gemma 4 26B A4B

2026-04-02

If you already use one of these models

Deployment change: Both entries list Google as the provider. Confirm endpoint, model ID, limits, and feature support before switching.
Quality signal: Gemini 3.1 Pro has the higher public point estimate, 65.05 versus 46.88. Their conditional score ranges overlap. These ranges do not establish rank confidence.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: Gemini 3.1 Pro has the larger documented window (1M).

Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Gemini 3.1 Pro or Gemma 4 26B A4B?

Gemini 3.1 Pro has the higher public point estimate, 65.05 versus 46.88. Their conditional score ranges 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, Gemini 3.1 Pro or Gemma 4 26B A4B?

Gemini 3.1 Pro has the higher public coding point estimate, 42.3 to 33.8, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

Which is better for agentic tasks, Gemini 3.1 Pro or Gemma 4 26B A4B?

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, Gemini 3.1 Pro or Gemma 4 26B A4B?

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, Gemini 3.1 Pro or Gemma 4 26B A4B?

Gemini 3.1 Pro has the larger documented context window: 1M, 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 evidence31 rows

Agentic

Claw-Eval
Gemini 3.1 Pro57.8%
Source
Gemma 4 26B A4B—
Not directly comparable
DeepSearchQA
Gemini 3.1 Pro69.7%
Source
Gemma 4 26B A4B—
Not directly comparable
τ²-bench results
Gemini 3.1 Pro95.6%
Source
Gemma 4 26B A4B—
Not directly comparable
Gert Labs
Gemini 3.1 Pro56.87%
Source
Gemma 4 26B A4B—
Not directly comparable
ResearchClawBench
Gemini 3.1 Pro13.3%
Source
Gemma 4 26B A4B—
Not directly comparable
Terminal-Bench 2.1 (Vals)
Gemini 3.1 Pro70.8%
Source
Gemma 4 26B A4B—
Not directly comparable

Coding

LiveCodeBench Pro
Gemini 3.1 Pro82.9%
Source
Gemma 4 26B A4B—
Not directly comparable
React Native Evals
Gemini 3.1 Pro78.9%
Source
Gemma 4 26B A4B—
Not directly comparable
Vibe Code Bench
Gemini 3.1 Pro32.03%
Source
Gemma 4 26B A4B—
Not directly comparable
LiveCodeBench (Vals)
Gemini 3.1 Pro88.5%
Source
Gemma 4 26B A4B—
Not directly comparable
SWE-bench (Vals)
Gemini 3.1 Pro78.8%
Source
Gemma 4 26B A4B—
Not directly comparable
PostTrainBench v1.1
Gemini 3.1 Pro22.0%
Source
Gemma 4 26B A4B—
Not directly comparable

Reasoning

ARC-AGI-2
Gemini 3.1 Pro77.1%
Source
Gemma 4 26B A4B—
Not directly comparable
ARC-AGI-3
Gemini 3.1 Pro0.4%
Source
Gemma 4 26B A4B—
Not directly comparable

Multimodal

MMMU-Pro
Gemini 3.1 Pro83.9%
Source
Gemma 4 26B A4B73.8%
Source
Gemini 3.1 Pro leads this result
CharXiv
Gemini 3.1 Pro80.2%
Source
Gemma 4 26B A4B—
Not directly comparable
ERQA
Gemini 3.1 Pro69.4%
Source
Gemma 4 26B A4B—
Not directly comparable
SimpleVQA
Gemini 3.1 Pro72.4%
Source
Gemma 4 26B A4B—
Not directly comparable
ScreenSpot Pro
Gemini 3.1 Pro84.4%
Source
Gemma 4 26B A4B—
Not directly comparable
ZeroBench
Gemini 3.1 Pro29.0%
Source
Gemma 4 26B A4B—
Not directly comparable
MedXpertQA (MM)
Gemini 3.1 Pro81.3%
Source
Gemma 4 26B A4B—
Not directly comparable

Knowledge

GPQA-D
Gemini 3.1 Pro94.3%
Source
Gemma 4 26B A4B—
Not directly comparable
HLE w/o tools
Gemini 3.1 Pro45.4%
Source
Gemma 4 26B A4B8.7%
Source
Gemini 3.1 Pro leads this result
HealthBench Hard
Gemini 3.1 Pro20.6%
Source
Gemma 4 26B A4B—
Not directly comparable
MedXpertQA (Text)
Gemini 3.1 Pro71.5%
Source
Gemma 4 26B A4B—
Not directly comparable
GPQA Diamond (Vals)
Gemini 3.1 Pro95.5%
Source
Gemma 4 26B A4B—
Not directly comparable
MMLU-Pro (Vals)
Gemini 3.1 Pro91.0%
Source
Gemma 4 26B A4B—
Not directly comparable
MMLU-Pro
Gemini 3.1 Pro—
Gemma 4 26B A4B82.6%
Source
Not directly comparable
HLE
Gemini 3.1 Pro—
Gemma 4 26B A4B17.2%
Source
Not directly comparable

Math

FrontierMath v2 (Tiers 1-3)
Gemini 3.1 Pro36.900%
Source
Gemma 4 26B A4B—
Not directly comparable
FrontierMath v2 (Tier 4)
Gemini 3.1 Pro16.700%
Source
Gemma 4 26B A4B—
Not directly comparable

31 public results · 2 shared

Related comparisons

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Last updated October 1, 2026

Gemini 3.1 Pro vs Gemma 4 26B A4B

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

Knowledge

Multimodal

Agentic

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, Gemini 3.1 Pro or Gemma 4 26B A4B?

Which is better for coding, Gemini 3.1 Pro or Gemma 4 26B A4B?

Which is better for agentic tasks, Gemini 3.1 Pro or Gemma 4 26B A4B?

Which costs less, Gemini 3.1 Pro or Gemma 4 26B A4B?

Which has the larger context window, Gemini 3.1 Pro or Gemma 4 26B A4B?

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Math

Related comparisons

Watch Gemini 3.1 Pro vs Gemma 4 26B A4B