Gemini 3.7 Flash vs Gemma 4 31B

Updated September 27, 2026. Rank says Gemini 3.7 Flash is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead. 0 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 3.7 Flash

Google

67.66/100

Supported · Public rank #19

90% interval 63.0–72.3

Model B

Gemma 4 31B

Google

44.84/100

Estimated · Public rank #97

90% interval 24.3–65.3

Shared results: 0
Gemini 3.7 Flash only: 25
Gemma 4 31B only: 8
Like-for-like categories: 2 / 8

Supported: Gemini 3.7 Flash · Estimated: Gemma 4 31BHow 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 3.7 Flash
Gemini 3.7 Flash leads on the public coding lane, 60.3 to 35.7, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
Long documents
Prompts that approach the documented context limit
Gemini 3.7 Flash
Gemini 3.7 Flash 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 31B 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
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.

60.3Gemini 3.7 Flash35.7Gemma 4 31B

Like-for-like · BenchAlign v5.7

Gemini 3.7 Flash leads the like-for-like coding row.

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.

A shared-evidence shape is not available.

BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.7 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 3.7 Flash: 60.3; Supported · #17/135
Gemma 4 31B: 35.7; Supported · #72/135
Basis: BenchAlign v5.7 lane · 6 vs 2 public rows
Reading: Gemini 3.7 Flash leads

Knowledge

Like-for-like

Gemini 3.7 Flash: 71.1; Supported · #10/158
Gemma 4 31B: 40.0; Supported · #91/158
Basis: BenchAlign v5.7 lane · 6 vs 4 public rows
Reading: Gemini 3.7 Flash leads

Agentic

Directional only

Gemini 3.7 Flash: 58.6; Supported · #20/105
Gemma 4 31B: 23.9; Estimated · #83/105
Basis: BenchAlign v5.7 lane · 7 vs 1 public rows
Reading: Directional only

Multimodal

Directional only

Gemini 3.7 Flash: 82.6; #10/50
Gemma 4 31B: 58.5; #32/50
Basis: Provisional lane · 1 vs 1 weighted rows
Reading: Directional only

Reasoning

Not comparable

Gemini 3.7 Flash: 77.8; Unranked · 5 rankable rows
Gemma 4 31B: 70.0; Unranked · 2 rankable rows
Basis: Provisional lane · 1 vs 0 weighted rows
Reading: Not comparable

Multilingual

Not comparable

Gemini 3.7 Flash: Not ranked
Gemma 4 31B: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

Gemini 3.7 Flash: Not ranked
Gemma 4 31B: 91.5; #12/124
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

Gemini 3.7 Flash: Not ranked
Gemma 4 31B: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Gemini 3.7 Flash and Gemma 4 31B
Category	Gemini 3.7 Flash	Gemma 4 31B	Basis	Reading
Coding	60.3Supported · #17/135	35.7Supported · #72/135	Like-for-likeBenchAlign v5.7 lane · 6 vs 2 public rows	Gemini 3.7 Flash leads
Knowledge	71.1Supported · #10/158	40.0Supported · #91/158	Like-for-likeBenchAlign v5.7 lane · 6 vs 4 public rows	Gemini 3.7 Flash leads
Agentic	58.6Supported · #20/105	23.9Estimated · #83/105	Directional onlyBenchAlign v5.7 lane · 7 vs 1 public rows	Directional only
Multimodal	82.6#10/50	58.5#32/50	Directional onlyProvisional lane · 1 vs 1 weighted rows	Directional only
Reasoning	77.8Unranked · 5 rankable rows	70.0Unranked · 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	Not ranked	91.5#12/124	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.7) 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 3.7 Flash: $0.00262; Fits in one request
Gemma 4 31B: Self-hosted; infrastructure cost varies; Fits in one request

Gemma 4 31B has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Gemini 3.7 Flash: $0.04875; Fits in one request
Gemma 4 31B: Self-hosted; infrastructure cost varies; Fits in one request

Gemma 4 31B has no comparable published API token rate.

Cache-heavy agent loop

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

Gemini 3.7 Flash: $0.0675; Fits in one request
Gemma 4 31B: Self-hosted; infrastructure cost varies; Fits in one request; Cached-input rate unavailable

Reasoning

Gemma 4 31B

Reasoning

Weight access

Gemini 3.7 Flash

Proprietary

Gemma 4 31B

Open Weight

License

Gemini 3.7 Flash

Proprietary

Gemma 4 31B

Open Weight

Release date

Gemini 3.7 Flash

2026-08-13

Gemma 4 31B

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: The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: Gemini 3.7 Flash has the larger documented window (1M).

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

Questions

Which is better, Gemini 3.7 Flash or Gemma 4 31B?

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Gemini 3.7 Flash or Gemma 4 31B?

Gemini 3.7 Flash leads the public coding lane, 60.3 to 35.7, with Supported evidence for both models and non-overlapping 90% intervals.

Which is better for agentic tasks, Gemini 3.7 Flash or Gemma 4 31B?

Gemini 3.7 Flash scores higher for agentic tasks on the public lane, 58.6 to 23.9. Gemma 4 31B 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, Gemini 3.7 Flash or Gemma 4 31B?

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 3.7 Flash or Gemma 4 31B?

Gemini 3.7 Flash has the larger documented context window: 1M, compared with 256K.

Self-host vs API cost

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

Gemini 3.7 Flash

API / mo$3,375

Self-host / moNot listed

Break-even—

Proprietary model — self-hosting not applicable.

Gemma 4 31B

API / mo$0

Self-host / mo$429

Break-even—

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 evidence33 rows

Agentic

Terminal-Bench 2.1
Gemini 3.7 Flash85.8%
Source
Gemma 4 31B—
Not directly comparable
Terminal-Bench 3.0
Gemini 3.7 Flash14.9%
Source
Gemma 4 31B—
Not directly comparable
AutomationBench
Gemini 3.7 Flash30.4%
Source
Gemma 4 31B—
Not directly comparable
OSWorld 2.0
Gemini 3.7 Flash47.9%
Source
Gemma 4 31B—
Not directly comparable
Agents' Last Exam
Gemini 3.7 Flash26.3%
Source
Gemma 4 31B—
Not directly comparable
Terminal-Bench 2.1 (Vals)
Gemini 3.7 Flash77.5%
Source
Gemma 4 31B—
Not directly comparable
ApprenticeBench
Gemini 3.7 Flash16%
Source
Gemma 4 31B—
Not directly comparable
Gert Labs
Gemini 3.7 Flash—
Gemma 4 31B35.26%
Source
Not directly comparable

Coding

FrontierCode 1.1 Main
Gemini 3.7 Flash43.6%
Source
Gemma 4 31B—
Not directly comparable
DeepSWE
Gemini 3.7 Flash65.3%
Source
Gemma 4 31B—
Not directly comparable
Terminal-Bench 2.1
Gemini 3.7 Flash85.8%
Source
Gemma 4 31B—
Not directly comparable
FrontierSWE v2
Gemini 3.7 Flash20.3%
Source
Gemma 4 31B—
Not directly comparable
LiveCodeBench (Vals)
Gemini 3.7 Flash88.7%
Source
Gemma 4 31B—
Not directly comparable
SWE-bench (Vals)
Gemini 3.7 Flash80.8%
Source
Gemma 4 31B—
Not directly comparable
SWE-Rebench
Gemini 3.7 Flash—
Gemma 4 31B41.6%
Source
Not directly comparable
React Native Evals
Gemini 3.7 Flash—
Gemma 4 31B75.2%
Source
Not directly comparable

Reasoning

MRCR v2 64K-128K
Gemini 3.7 Flash97%
Source
Gemma 4 31B—
Not directly comparable
ARC-AGI-1
Gemini 3.7 Flash95.50%
Source
Gemma 4 31B—
Not directly comparable
ARC-AGI-2
Gemini 3.7 Flash84.6%
Source
Gemma 4 31B—
Not directly comparable

Multimodal

CharXiv w/o tools
Gemini 3.7 Flash84.5%
Source
Gemma 4 31B—
Not directly comparable
CharXiv
Gemini 3.7 Flash88.7%
Source
Gemma 4 31B—
Not directly comparable
LVBench
Gemini 3.7 Flash85.4%
Source
Gemma 4 31B—
Not directly comparable
MMMU-Pro
Gemini 3.7 Flash—
Gemma 4 31B76.9%
Source
Not directly comparable

Knowledge

HLE-Verified
Gemini 3.7 Flash53.6%
Source
Gemma 4 31B—
Not directly comparable
LABBench2
Gemini 3.7 Flash82.1%
Source
Gemma 4 31B—
Not directly comparable
BioMysteryBench (human-solvable)
Gemini 3.7 Flash87.1%
Source
Gemma 4 31B—
Not directly comparable
BioMysteryBench (human-difficult)
Gemini 3.7 Flash43.5%
Source
Gemma 4 31B—
Not directly comparable
GPQA Diamond (Vals)
Gemini 3.7 Flash93.9%
Source
Gemma 4 31B—
Not directly comparable
MMLU-Pro (Vals)
Gemini 3.7 Flash90.1%
Source
Gemma 4 31B—
Not directly comparable
GPQA
Gemini 3.7 Flash—
Gemma 4 31B84.3%
Source
Not directly comparable
MMLU-Pro
Gemini 3.7 Flash—
Gemma 4 31B85.2%
Source
Not directly comparable
HLE
Gemini 3.7 Flash—
Gemma 4 31B26.5%
Source
Not directly comparable
HLE w/o tools
Gemini 3.7 Flash—
Gemma 4 31B19.5%
Source
Not directly comparable

33 public results · 0 shared

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Last updated September 27, 2026

Gemini 3.7 Flash vs Gemma 4 31B

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

Agentic

Multimodal

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 3.7 Flash or Gemma 4 31B?

Which is better for coding, Gemini 3.7 Flash or Gemma 4 31B?

Which is better for agentic tasks, Gemini 3.7 Flash or Gemma 4 31B?

Which costs less, Gemini 3.7 Flash or Gemma 4 31B?

Which has the larger context window, Gemini 3.7 Flash or Gemma 4 31B?

Self-host vs API cost

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Related comparisons

Watch Gemini 3.7 Flash vs Gemma 4 31B