Gemini 3.1 Pro vs Gemma 4 E4B

Updated November 13, 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, 63.11 versus 24.59. Their conditional score ranges do not 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

63.11/100

Estimated · Public rank #38

Conditional range 53.4–72.8

Model B

Gemma 4 E4B

Google

24.59/100

Estimated · Public rank #198

Conditional range 10.2–38.9

API price per 1M tokens

Gemini 3.1 Pro: $2 input / $12 output

Gemma 4 E4B: No comparable API price

Context window

Gemini 3.1 Pro: 1M

Gemma 4 E4B: 128K

Released

Gemini 3.1 Pro: February 19, 2026

Gemma 4 E4B: April 2, 2026

Shared results: 2
Gemini 3.1 Pro only: 32
Gemma 4 E4B only: 5
Like-for-like categories: 0 / 8

Estimated: Gemini 3.1 Pro and Gemma 4 E4B. 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
Gemini 3.1 Pro
Gemini 3.1 Pro 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 E4B 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 E4B 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
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. Gemma 4 E4B does not fit this workload in one request. Gemma 4 E4B 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

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.

37.9Gemini 3.1 Pro14.7Gemma 4 E4B

Directional only · 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

Or describe your own job

The LLM Selector reads what you need a model for and returns a shortlist, ranked in the same order the job pages publish.

What do you need a model for?

Describe the task and constraints. This selector recommends models; it cannot carry out the task or access your files and accounts.

We keep what you type here, drafts included, for 180 days, and after that only the job and requirements read from it. Nothing is tied to your IP address or account. Leave out names and other personal details. Privacy

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 31.3
Gemini 3.1 Pro:83.9%
Gemma 4 E4B:52.6%
Gemini 3.1 Pro source Gemma 4 E4B 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

Directional only

Gemini 3.1 Pro: 37.9; Supported · #76/162
Gemma 4 E4B: 14.7; Estimated · #151/162
Basis: BenchAlign v5.8 lane · 9 vs 1 public rows
Reading: Directional only

Reasoning

Directional only

Gemini 3.1 Pro: 56.3; Estimated · #44/160
Gemma 4 E4B: 21.8; Supported · #140/160
Basis: BenchAlign v5.8 lane · 2 vs 1 public rows
Reading: Directional only

Multimodal

Directional only

Gemini 3.1 Pro: 65.3; Estimated · #27/67
Gemma 4 E4B: 28.6; Supported · #67/67
Basis: BenchAlign v5.8 lane · 8 vs 3 public rows
Reading: Directional only

Knowledge

Directional only

Gemini 3.1 Pro: 66.0; Supported · #24/180
Gemma 4 E4B: 27.5; Estimated · #159/180
Basis: BenchAlign v5.8 lane · 6 vs 2 public rows
Reading: Directional only

Instruction following

Directional only

Gemini 3.1 Pro: 90.2; #27/157
Gemma 4 E4B: 51.9; #95/157
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Directional only

Agentic

Not comparable

Gemini 3.1 Pro: 39.6; Supported · #72/135
Gemma 4 E4B: Not ranked
Basis: BenchAlign v5.8 lane · 7 vs 0 public rows
Reading: Not comparable

Multilingual

Not comparable

Gemini 3.1 Pro: Not ranked
Gemma 4 E4B: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

Gemini 3.1 Pro: 60.6; Unranked · 2 rankable rows
Gemma 4 E4B: 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 E4B
Category	Gemini 3.1 Pro	Gemma 4 E4B	Basis	Reading
Coding	37.9Supported · #76/162	14.7Estimated · #151/162	Directional onlyBenchAlign v5.8 lane · 9 vs 1 public rows	Directional only
Reasoning	56.3Estimated · #44/160	21.8Supported · #140/160	Directional onlyBenchAlign v5.8 lane · 2 vs 1 public rows	Directional only
Multimodal	65.3Estimated · #27/67	28.6Supported · #67/67	Directional onlyBenchAlign v5.8 lane · 8 vs 3 public rows	Directional only
Knowledge	66.0Supported · #24/180	27.5Estimated · #159/180	Directional onlyBenchAlign v5.8 lane · 6 vs 2 public rows	Directional only
Instruction following	90.2#27/157	51.9#95/157	Directional onlyProvisional lane · 0 vs 0 weighted rows	Directional only
Agentic	39.6Supported · #72/135	Not ranked	Not comparableBenchAlign v5.8 lane · 7 vs 0 public rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	60.6Unranked · 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, 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

Gemini 3.1 Pro: $0.008; Fits in one request
Gemma 4 E4B: Self-hosted; infrastructure cost varies; Fits in one request

Gemma 4 E4B 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 E4B: Self-hosted; infrastructure cost varies; Fits in one request

Gemma 4 E4B 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 E4B: Self-hosted; infrastructure cost varies; Does not fit in one request; Cached-input rate unavailable

Gemma 4 E4B does not fit this workload in one request. Gemma 4 E4B has no comparable published API token rate.

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

Price against score, among all priced models

Overall score against output price per 1M tokens, one model per family. Pricing as of November 12, 2026.

Gemini 3.1 Pro

10 cheaper models score at least as high as Gemini 3.1 Pro. The nearest is Gemini 4 Carbon (score 89.2, $10.00 per 1M output tokens).
Gemma 4 E4B has no public API price, so it is not plotted.

Gray dots are the other priced models. The dark step line is the efficiency frontier: for a model under it, another model costs no more and scores at least as high.

See Gemini 3.1 Pro on the full price vs performance chart

Specification differences

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

SpecificationGemini 3.1 ProGemma 4 E4B

Context window

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

Gemini 3.1 Pro

Google Gemini 3.1 Pro Preview model documentation

Gemma 4 E4B

128K

Google Gemma 4 model documentation

Gemini 3.1 Pro

Reasoning

Gemma 4 E4B

Reasoning

Weight access

Gemini 3.1 Pro

Proprietary

Gemma 4 E4B

Open Weight

License

Gemini 3.1 Pro

Proprietary

Gemma 4 E4B

Open Weight

Release date

Gemini 3.1 Pro

2026-02-19

Gemma 4 E4B

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, 63.11 versus 24.59. 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: 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 E4B?

Gemini 3.1 Pro has the higher public point estimate, 63.11 versus 24.59. 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, Gemini 3.1 Pro or Gemma 4 E4B?

Gemini 3.1 Pro scores higher for coding on the public lane, 37.9 to 14.7. Gemma 4 E4B 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, Gemini 3.1 Pro or Gemma 4 E4B?

Gemma 4 E4B 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 E4B?

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 E4B?

Gemini 3.1 Pro has the larger documented context window: 1M, compared with 128K.

Benchmark evidence

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

Browse raw public benchmark evidence39 rows

Agentic

Claw-Eval
Gemini 3.1 Pro57.8%
Source
Gemma 4 E4B—
Not directly comparable
DeepSearchQA
Gemini 3.1 Pro69.7%
Source
Gemma 4 E4B—
Not directly comparable
τ²-bench results
Gemini 3.1 Pro95.6%
Source
Gemma 4 E4B—
Not directly comparable
Gert Labs
Gemini 3.1 Pro56.87%
Source
Gemma 4 E4B—
Not directly comparable
ResearchClawBench
Gemini 3.1 Pro13.3%
Source
Gemma 4 E4B—
Not directly comparable
Terminal-Bench 2.1 (Vals)
Gemini 3.1 Pro70.8%
Source
Gemma 4 E4B—
Not directly comparable
MazeBench
Gemini 3.1 Pro1%
Source
Gemma 4 E4B—
Not directly comparable

Coding

ScarfBench
Gemini 3.1 Pro37.6%
Source
Gemma 4 E4B—
Not directly comparable
LiveCodeBench Pro
Gemini 3.1 Pro82.9%
Source
Gemma 4 E4B—
Not directly comparable
React Native Evals
Gemini 3.1 Pro78.9%
Source
Gemma 4 E4B—
Not directly comparable
Vibe Code Bench
Gemini 3.1 Pro32.03%
Source
Gemma 4 E4B—
Not directly comparable
LiveCodeBench (Vals)
Gemini 3.1 Pro88.5%
Source
Gemma 4 E4B—
Not directly comparable
SWE-bench (Vals)
Gemini 3.1 Pro78.8%
Source
Gemma 4 E4B—
Not directly comparable
PostTrainBench v1.1
Gemini 3.1 Pro22.0%
Source
Gemma 4 E4B—
Not directly comparable
PostTrainBench
Gemini 3.1 Pro18.1%
Source
Gemma 4 E4B—
Not directly comparable
PostTrainBench v1
Gemini 3.1 Pro21.6%
Source
Gemma 4 E4B—
Not directly comparable
LiveCodeBench v6
Gemini 3.1 Pro—
Gemma 4 E4B52.0%
Source
Not directly comparable

Reasoning

ARC-AGI-2
Gemini 3.1 Pro77.1%
Source
Gemma 4 E4B—
Not directly comparable
ARC-AGI-3
Gemini 3.1 Pro0.4%
Source
Gemma 4 E4B—
Not directly comparable
MRCRv2
Gemini 3.1 Pro—
Gemma 4 E4B25.4%
Source
Not directly comparable

Multimodal

ParseBench-1
Shared source
Gemini 3.1 Pro67.32%
Gemma 4 E4B41.13%
Gemini 3.1 Pro leads this result
MMMU-Pro
Gemini 3.1 Pro83.9%
Source
Gemma 4 E4B52.6%
Source
Gemini 3.1 Pro leads this result
CharXiv
Gemini 3.1 Pro80.2%
Source
Gemma 4 E4B—
Not directly comparable
ERQA
Gemini 3.1 Pro69.4%
Source
Gemma 4 E4B—
Not directly comparable
SimpleVQA
Gemini 3.1 Pro72.4%
Source
Gemma 4 E4B—
Not directly comparable
ScreenSpot Pro
Gemini 3.1 Pro84.4%
Source
Gemma 4 E4B—
Not directly comparable
ZeroBench
Gemini 3.1 Pro29.0%
Source
Gemma 4 E4B—
Not directly comparable
MedXpertQA (MM)
Gemini 3.1 Pro81.3%
Source
Gemma 4 E4B—
Not directly comparable
ExtractBench (LlamaIndex)
Gemini 3.1 Pro—
Gemma 4 E4B69.64%
Source
Not directly comparable

Knowledge

GPQA-D
Gemini 3.1 Pro94.3%
Source
Gemma 4 E4B—
Not directly comparable
HLE w/o tools
Gemini 3.1 Pro45.4%
Source
Gemma 4 E4B—
Not directly comparable
HealthBench Hard
Gemini 3.1 Pro20.6%
Source
Gemma 4 E4B—
Not directly comparable
MedXpertQA (Text)
Gemini 3.1 Pro71.5%
Source
Gemma 4 E4B—
Not directly comparable
GPQA Diamond (Vals)
Gemini 3.1 Pro95.5%
Source
Gemma 4 E4B—
Not directly comparable
MMLU-Pro (Vals)
Gemini 3.1 Pro91.0%
Source
Gemma 4 E4B—
Not directly comparable
GPQA
Gemini 3.1 Pro—
Gemma 4 E4B58.6%
Source
Not directly comparable
MMLU-Pro
Gemini 3.1 Pro—
Gemma 4 E4B69.4%
Source
Not directly comparable

Math

FrontierMath v2 (Tiers 1-3)
Gemini 3.1 Pro59.649%
Source
Gemma 4 E4B—
Not directly comparable
FrontierMath v2 (Tier 4)
Gemini 3.1 Pro26.829%
Source
Gemma 4 E4B—
Not directly comparable

39 public results · 2 shared

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Last updated November 13, 2026

Gemini 3.1 Pro vs Gemma 4 E4B

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

Or describe your own 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

Price against score, among all priced models

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 E4B?

Which is better for coding, Gemini 3.1 Pro or Gemma 4 E4B?

Which is better for agentic tasks, Gemini 3.1 Pro or Gemma 4 E4B?

Which costs less, Gemini 3.1 Pro or Gemma 4 E4B?

Which has the larger context window, Gemini 3.1 Pro or Gemma 4 E4B?

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Math

Related comparisons

Watch Gemini 3.1 Pro vs Gemma 4 E4B