Model A

Google

58.69/100

Supported · Public rank #83

90% interval 42.8–74.6

Gemma 4 31B vs Laguna S 2.1

Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B

Laguna S 2.1

Poolside

—

Evidence status unavailable

90% interval unavailable

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.

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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
Laguna S 2.1
Laguna S 2.1 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
Laguna S 2.1 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
Laguna S 2.1 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

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

8 Gemma 4 31B only

6 Laguna S 2.1 only

Shared results: 0
Gemma 4 31B only: 8
Laguna S 2.1 only: 6
Like-for-like categories: 0 / 8

2 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign 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.

Agentic

Directional only

Gemma 4 31B: 27.6; Supported · #140/151
Laguna S 2.1: 48.2; Estimated · #77/151
Basis: BenchAlign lane · 1 vs 2 public rows
Reading: Directional only

Coding

Directional only

Gemma 4 31B: 43.5; Supported · #124/183
Laguna S 2.1: 47.8; Estimated · #87/183
Basis: BenchAlign lane · 2 vs 4 public rows
Reading: Directional only

Reasoning

Not comparable

Gemma 4 31B: 70.1; Unranked · 2 rankable rows
Laguna S 2.1: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Knowledge

Not comparable

Gemma 4 31B: 47.8; Supported · #104/181
Laguna S 2.1: Not ranked
Basis: BenchAlign lane · 4 vs 0 public rows
Reading: Not comparable

Math

Not comparable

Gemma 4 31B: Not ranked
Laguna S 2.1: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multilingual

Not comparable

Gemma 4 31B: Not ranked
Laguna S 2.1: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

Gemma 4 31B: 57.8; #30/48
Laguna S 2.1: Not ranked
Basis: Provisional lane · 1 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

Gemma 4 31B: 92.6; #12/120
Laguna S 2.1: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Gemma 4 31B and Laguna S 2.1
Category	Gemma 4 31B	Laguna S 2.1	Basis	Reading
Agentic	27.6Supported · #140/151	48.2Estimated · #77/151	Directional onlyBenchAlign lane · 1 vs 2 public rows	Directional only
Coding	43.5Supported · #124/183	47.8Estimated · #87/183	Directional onlyBenchAlign lane · 2 vs 4 public rows	Directional only
Reasoning	70.1Unranked · 2 rankable rows	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Knowledge	47.8Supported · #104/181	Not ranked	Not comparableBenchAlign lane · 4 vs 0 public rows	Not comparable
Math	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Multimodal	57.8#30/48	Not ranked	Not comparableProvisional lane · 1 vs 0 weighted rows	Not comparable
Instruction following	92.6#12/120	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) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

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.

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 31B: Self-hosted; infrastructure cost varies; Fits in one request
Laguna S 2.1: $0.0002; Fits in one request

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

Repository review

50K fresh input + 3K output tokens

Gemma 4 31B: Self-hosted; infrastructure cost varies; Fits in one request
Laguna S 2.1: $0.0056; 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

Gemma 4 31B: Self-hosted; infrastructure cost varies; Fits in one request; Cached-input rate unavailable
Laguna S 2.1: $0.006; Fits in one request

Reasoning

Laguna S 2.1

Reasoning

Weight access

Gemma 4 31B

Open Weight

Laguna S 2.1

Open Weight

License

Gemma 4 31B

Open Weight

Laguna S 2.1

Open Weight

Release date

Gemma 4 31B

2026-04-02

Laguna S 2.1

2026-07-21

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: 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: Laguna S 2.1 has the larger documented window (1M).

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

Self-host vs API cost

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

Gemma 4 31B

API / mo$0

Self-host / mo$429

Break-even—

Laguna S 2.1

API / mo$225

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 evidence14 rows

Agentic

Gert Labs
Gemma 4 31B35.26%
Source
Laguna S 2.1—
Not directly comparable
Terminal-Bench 2.0
Gemma 4 31B—
Laguna S 2.170.2%
Source
Not directly comparable
Toolathlon-Verified
Gemma 4 31B—
Laguna S 2.149.7%
Source
Not directly comparable

Coding

SWE-Rebench
Gemma 4 31B41.6%
Source
Laguna S 2.1—
Not directly comparable
React Native Evals
Gemma 4 31B75.2%
Source
Laguna S 2.1—
Not directly comparable
Terminal-Bench 2.0
Gemma 4 31B—
Laguna S 2.170.2%
Source
Not directly comparable
SWE Multilingual
Gemma 4 31B—
Laguna S 2.178.5%
Source
Not directly comparable
SWE-bench Pro
Gemma 4 31B—
Laguna S 2.159.4%
Source
Not directly comparable
deepSwe
Gemma 4 31B—
Laguna S 2.140.4%
Source
Not directly comparable

Knowledge

GPQA
Gemma 4 31B84.3%
Source
Laguna S 2.1—
Not directly comparable
MMLU-Pro
Gemma 4 31B85.2%
Source
Laguna S 2.1—
Not directly comparable
HLE
Gemma 4 31B26.5%
Source
Laguna S 2.1—
Not directly comparable
HLE w/o tools
Gemma 4 31B19.5%
Source
Laguna S 2.1—
Not directly comparable

Multimodal

MMMU-Pro
Gemma 4 31B76.9%
Source
Laguna S 2.1—
Not directly comparable

Frequently asked questions

Which is better, Gemma 4 31B or Laguna S 2.1?

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, Gemma 4 31B or Laguna S 2.1?

Laguna S 2.1 scores higher for coding on the public lane, 47.8 to 43.5. Laguna S 2.1 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, Gemma 4 31B or Laguna S 2.1?

Laguna S 2.1 scores higher for agentic tasks on the public lane, 48.2 to 27.6. Laguna S 2.1 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, Gemma 4 31B or Laguna S 2.1?

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 31B or Laguna S 2.1?

Laguna S 2.1 has the larger documented context window: 1M, compared with 256K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 4, 2026

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Gemma 4 31B vs Laguna S 2.1

Which one for your work

Long documents

Coding work

Agentic work

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

What is actually comparable

Category results, on a stated basis

Agentic

Coding

Reasoning

Knowledge

Math

Multilingual

Multimodal

Instruction following

Shape of the matched evidence

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

Self-host vs API cost

Benchmark evidence

Agentic

Coding

Knowledge

Multimodal

Frequently asked questions

Which is better, Gemma 4 31B or Laguna S 2.1?

Which is better for coding, Gemma 4 31B or Laguna S 2.1?

Which is better for agentic tasks, Gemma 4 31B or Laguna S 2.1?

Which costs less, Gemma 4 31B or Laguna S 2.1?

Which has the larger context window, Gemma 4 31B or Laguna S 2.1?

Related comparisons

Watch Gemma 4 31B vs Laguna S 2.1