Model A

Google

58.69/100

Supported · Public rank #83

90% interval 42.8–74.6

Gemma 4 31B vs Qwen3.6-27B

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

Model B

Qwen3.6-27B

Alibaba

52.73/100

Estimated · Public rank #120

90% interval 47.0–58.5

Decision reading

Gemma 4 31B has the higher public score estimate, 58.69 versus 52.73, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

5 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.

Coding work
Code generation, repair, and software-engineering tasks
Gemma 4 31B
Gemma 4 31B leads on the public coding lane, 43.5 to 42.6, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Qwen3.6-27B
Qwen3.6-27B leads on the public agentic lane, 33.9 to 27.6, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Long documents
Prompts that approach the documented context limit
Qwen3.6-27B
Qwen3.6-27B has the larger documented context window.
Confidence: documented

Show secondary and unsupported calls

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.

3 Gemma 4 31B only

5 Shared

33 Qwen3.6-27B only

Shared results: 5
Gemma 4 31B only: 3
Qwen3.6-27B only: 33
Like-for-like categories: 2 / 8

3 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

Like-for-like

Gemma 4 31B: 27.6; Supported · #140/151
Qwen3.6-27B: 33.9; Supported · #133/151
Basis: BenchAlign lane · 1 vs 6 public rows
Reading: Qwen3.6-27B leads · intervals overlap

Coding

Like-for-like

Gemma 4 31B: 43.5; Supported · #124/183
Qwen3.6-27B: 42.6; Supported · #128/183
Basis: BenchAlign lane · 2 vs 6 public rows
Reading: Gemma 4 31B leads · intervals overlap

Knowledge

Directional only

Gemma 4 31B: 47.8; Supported · #104/181
Qwen3.6-27B: 49.0; Estimated · #95/181
Basis: BenchAlign lane · 4 vs 6 public rows
Reading: Directional only

Multimodal

Directional only

Gemma 4 31B: 57.8; #30/48
Qwen3.6-27B: 51.5; #35/48
Basis: Provisional lane · 1 vs 2 weighted rows
Reading: Directional only

Instruction following

Directional only

Gemma 4 31B: 92.6; #12/120
Qwen3.6-27B: 82.2; #50/120
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Directional only

Reasoning

Not comparable

Gemma 4 31B: 70.1; Unranked · 2 rankable rows
Qwen3.6-27B: 73.7; Unranked · 2 rankable rows
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

Gemma 4 31B: Not ranked
Qwen3.6-27B: 72.8; Unranked · 5 rankable rows
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Not comparable

Multilingual

Not comparable

Gemma 4 31B: Not ranked
Qwen3.6-27B: 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 Qwen3.6-27B
Category	Gemma 4 31B	Qwen3.6-27B	Basis	Reading
Agentic	27.6Supported · #140/151	33.9Supported · #133/151	Like-for-likeBenchAlign lane · 1 vs 6 public rows	Qwen3.6-27B leads · intervals overlap
Coding	43.5Supported · #124/183	42.6Supported · #128/183	Like-for-likeBenchAlign lane · 2 vs 6 public rows	Gemma 4 31B leads · intervals overlap
Knowledge	47.8Supported · #104/181	49.0Estimated · #95/181	Directional onlyBenchAlign lane · 4 vs 6 public rows	Directional only
Multimodal	57.8#30/48	51.5#35/48	Directional onlyProvisional lane · 1 vs 2 weighted rows	Directional only
Instruction following	92.6#12/120	82.2#50/120	Directional onlyProvisional lane · 0 vs 0 weighted rows	Directional only
Reasoning	70.1Unranked · 2 rankable rows	73.7Unranked · 2 rankable rows	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	Not ranked	72.8Unranked · 5 rankable rows	Not comparableProvisional lane · 0 vs 2 weighted rows	Not comparable
Multilingual	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) 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.

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.

GPQA
Knowledge
Gemma 4 31B: 84.3%Qwen3.6-27B: 87.8%Normalized gap 3.5Gemma 4 31B source Qwen3.6-27B source
HLE
Knowledge
Gemma 4 31B: 26.5%Qwen3.6-27B: 24%Normalized gap 2.5Gemma 4 31B source Qwen3.6-27B source
MMMU-Pro
Multimodal
Gemma 4 31B: 76.9%Qwen3.6-27B: 75.8%Normalized gap 1.1Gemma 4 31B source Qwen3.6-27B source
MMLU-Pro
Knowledge
Gemma 4 31B: 85.2%Qwen3.6-27B: 86.2%Normalized gap 1.0Gemma 4 31B source Qwen3.6-27B source

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
Qwen3.6-27B: Self-hosted; infrastructure cost varies; Fits in one request

Gemma 4 31B has no comparable published API token rate. Qwen3.6-27B 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
Qwen3.6-27B: Self-hosted; infrastructure cost varies; Fits in one request

Gemma 4 31B has no comparable published API token rate. Qwen3.6-27B 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
Qwen3.6-27B: Self-hosted; infrastructure cost varies; Fits in one request; Cached-input rate unavailable

Gemma 4 31B has no comparable published API token rate. Qwen3.6-27B has no comparable published API token rate.

Specification differences

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

SpecificationGemma 4 31BQwen3.6-27B

Context window

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

Gemma 4 31B

256K

Google Gemma 4 model documentation

Qwen3.6-27B

262K

API model ID

Gemma 4 31B

gemma-4-31b-it

Google Gemma Gemini API guide

Qwen3.6-27B

Not sourced

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

Gemma 4 31B

No comparable hosted API rate

Qwen3.6-27B

No comparable hosted API rate

Documented inputs

Gemma 4 31B

text, image

Google Gemma 4 model documentation

Qwen3.6-27B

Not sourced

Documented outputs

Gemma 4 31B

text

Google Gemma 4 model documentation

Qwen3.6-27B

Not sourced

Provider availability

Gemma 4 31B

Generally Available · Gemini API, Google AI Studio, open weights

Google Gemma Gemini API guide

Qwen3.6-27B

Not sourced

Reasoning profile

Gemma 4 31B

Reasoning

Qwen3.6-27B

Reasoning

Weight access

Gemma 4 31B

Open Weight

Qwen3.6-27B

Open Weight

License

Gemma 4 31B

Open Weight

Qwen3.6-27B

Open Weight

Release date

Gemma 4 31B

2026-04-02

Qwen3.6-27B

2026-04-21

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: Gemma 4 31B has the higher public score estimate, 58.69 versus 52.73, but the 90% score intervals overlap.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: Qwen3.6-27B has the larger documented window (262K).

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—

Qwen3.6-27B

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 evidence41 rows

Agentic

Gert Labs
Shared source
Gemma 4 31B35.26%
Qwen3.6-27B54.84%
Qwen3.6-27B leads this result
Terminal-Bench 2.0
Gemma 4 31B—
Qwen3.6-27B59.3%
Source
Not directly comparable
Claw-Eval
Gemma 4 31B—
Qwen3.6-27B72.4%
Source
Not directly comparable
QwenClawBench
Gemma 4 31B—
Qwen3.6-27B53.4%
Source
Not directly comparable
QwenWebBench
Gemma 4 31B—
Qwen3.6-27B1487
Source
Not directly comparable
AndroidWorld
Gemma 4 31B—
Qwen3.6-27B70.3%
Source
Not directly comparable

Coding

SWE-Rebench
Gemma 4 31B41.6%
Source
Qwen3.6-27B—
Not directly comparable
React Native Evals
Gemma 4 31B75.2%
Source
Qwen3.6-27B—
Not directly comparable
SWE-bench Verified
Gemma 4 31B—
Qwen3.6-27B77.2%
Source
Not directly comparable
SWE Multilingual
Gemma 4 31B—
Qwen3.6-27B71.3%
Source
Not directly comparable
SWE-bench Pro
Gemma 4 31B—
Qwen3.6-27B53.5%
Source
Not directly comparable
Terminal-Bench 2.0
Gemma 4 31B—
Qwen3.6-27B59.3%
Source
Not directly comparable
LiveCodeBench
Gemma 4 31B—
Qwen3.6-27B83.9%
Source
Not directly comparable
NL2Repo
Gemma 4 31B—
Qwen3.6-27B36.2%
Source
Not directly comparable

Knowledge

GPQA
Gemma 4 31B84.3%
Source
Qwen3.6-27B87.8%
Source
Qwen3.6-27B leads this result
MMLU-Pro
Gemma 4 31B85.2%
Source
Qwen3.6-27B86.2%
Source
Qwen3.6-27B leads this result
HLE
Gemma 4 31B26.5%
Source
Qwen3.6-27B24%
Source
Gemma 4 31B leads this result
HLE w/o tools
Gemma 4 31B19.5%
Source
Qwen3.6-27B—
Not directly comparable
MMLU-Redux
Gemma 4 31B—
Qwen3.6-27B93.5%
Source
Not directly comparable
SuperGPQA
Gemma 4 31B—
Qwen3.6-27B66%
Source
Not directly comparable
C-Eval
Gemma 4 31B—
Qwen3.6-27B91.4%
Source
Not directly comparable

Math

HMMT Feb 2025
Gemma 4 31B—
Qwen3.6-27B93.8%
Source
Not directly comparable
HMMT Nov 2025
Gemma 4 31B—
Qwen3.6-27B90.7%
Source
Not directly comparable
HMMT Feb 2026
Gemma 4 31B—
Qwen3.6-27B84.3%
Source
Not directly comparable
MMAnswerBench
Gemma 4 31B—
Qwen3.6-27B80.8%
Source
Not directly comparable
AIME26
Gemma 4 31B—
Qwen3.6-27B94.1%
Source
Not directly comparable

Multimodal

MMMU-Pro
Gemma 4 31B76.9%
Source
Qwen3.6-27B75.8%
Source
Gemma 4 31B leads this result
MMMU
Gemma 4 31B—
Qwen3.6-27B82.9%
Source
Not directly comparable
RealWorldQA
Gemma 4 31B—
Qwen3.6-27B84.1%
Source
Not directly comparable
DynaMath
Gemma 4 31B—
Qwen3.6-27B85.6%
Source
Not directly comparable
MStar
Gemma 4 31B—
Qwen3.6-27B81.4%
Source
Not directly comparable
SimpleVQA
Gemma 4 31B—
Qwen3.6-27B56.1%
Source
Not directly comparable
CharXiv
Gemma 4 31B—
Qwen3.6-27B78.4%
Source
Not directly comparable
CC-OCR
Gemma 4 31B—
Qwen3.6-27B81.2%
Source
Not directly comparable
CountBench
Gemma 4 31B—
Qwen3.6-27B97.8%
Source
Not directly comparable
RefCOCO (avg)
Gemma 4 31B—
Qwen3.6-27B92.5%
Source
Not directly comparable
ERQA
Gemma 4 31B—
Qwen3.6-27B62.5%
Source
Not directly comparable
Video-MME (with subtitle)
Gemma 4 31B—
Qwen3.6-27B87.7%
Source
Not directly comparable
VideoMMMU
Gemma 4 31B—
Qwen3.6-27B84.4%
Source
Not directly comparable
MLVU (M-Avg)
Gemma 4 31B—
Qwen3.6-27B86.6%
Source
Not directly comparable
V*
Gemma 4 31B—
Qwen3.6-27B94.7%
Source
Not directly comparable

Frequently asked questions

Which is better, Gemma 4 31B or Qwen3.6-27B?

Gemma 4 31B has the higher public score estimate, 58.69 versus 52.73, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, Gemma 4 31B or Qwen3.6-27B?

Gemma 4 31B leads the public coding lane, 43.5 to 42.6, with Supported evidence for both models, although the 90% intervals overlap.

Which is better for agentic tasks, Gemma 4 31B or Qwen3.6-27B?

Qwen3.6-27B leads the public agentic tasks lane, 33.9 to 27.6, with Supported evidence for both models, although the 90% intervals overlap.

Which costs less, Gemma 4 31B or Qwen3.6-27B?

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 Qwen3.6-27B?

Qwen3.6-27B has the larger documented context window: 262K, 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 Qwen3.6-27B

Which one for your work

Coding work

Agentic work

Long documents

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

What is actually comparable

Category results, on a stated basis

Agentic

Coding

Knowledge

Multimodal

Instruction following

Reasoning

Math

Multilingual

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

Math

Multimodal

Frequently asked questions

Which is better, Gemma 4 31B or Qwen3.6-27B?

Which is better for coding, Gemma 4 31B or Qwen3.6-27B?

Which is better for agentic tasks, Gemma 4 31B or Qwen3.6-27B?

Which costs less, Gemma 4 31B or Qwen3.6-27B?

Which has the larger context window, Gemma 4 31B or Qwen3.6-27B?

Related comparisons

Watch Gemma 4 31B vs Qwen3.6-27B