Model A

Google

69.82/100

Supported · Public rank #25

90% interval 62.9–76.8

Gemini 3.1 Pro 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

Gemini 3.1 Pro has the higher public score, 69.82 versus 52.73, and the 90% score intervals do not overlap.

6 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
Gemini 3.1 Pro
Gemini 3.1 Pro leads on the public coding lane, 46.2 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
Gemini 3.1 Pro
Gemini 3.1 Pro leads on the public agentic lane, 40.1 to 33.9, with Supported evidence for both models, although the 90% intervals overlap.
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

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.

22 Gemini 3.1 Pro only

6 Shared

32 Qwen3.6-27B only

Shared results: 6
Gemini 3.1 Pro only: 22
Qwen3.6-27B only: 32
Like-for-like categories: 3 / 8

1 category rests 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

Gemini 3.1 Pro: 40.1; Supported · #120/151
Qwen3.6-27B: 33.9; Supported · #133/151
Basis: BenchAlign lane · 6 vs 6 public rows
Reading: Gemini 3.1 Pro leads · intervals overlap

Coding

Like-for-like

Gemini 3.1 Pro: 46.2; Supported · #99/183
Qwen3.6-27B: 42.6; Supported · #128/183
Basis: BenchAlign lane · 5 vs 6 public rows
Reading: Gemini 3.1 Pro leads · intervals overlap

Multimodal

Like-for-like

Gemini 3.1 Pro: 79.2; #12/48
Qwen3.6-27B: 51.5; #35/48
Basis: Provisional lane · 2 vs 2 weighted rows
Reading: Gemini 3.1 Pro leads

Knowledge

Directional only

Gemini 3.1 Pro: 65.4; Supported · #24/181
Qwen3.6-27B: 49.0; Estimated · #95/181
Basis: BenchAlign lane · 6 vs 6 public rows
Reading: Directional only

Reasoning

Not comparable

Gemini 3.1 Pro: 50.7; Unranked · 2 rankable rows
Qwen3.6-27B: 73.7; Unranked · 2 rankable rows
Basis: Provisional lane · 2 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

Gemini 3.1 Pro: 54.3; Unranked · 2 rankable rows
Qwen3.6-27B: 72.8; Unranked · 5 rankable rows
Basis: Provisional lane · 2 vs 2 weighted rows
Reading: Not comparable

Multilingual

Not comparable

Gemini 3.1 Pro: Not ranked
Qwen3.6-27B: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

Gemini 3.1 Pro: Not ranked
Qwen3.6-27B: 82.2; #50/120
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Gemini 3.1 Pro and Qwen3.6-27B
Category	Gemini 3.1 Pro	Qwen3.6-27B	Basis	Reading
Agentic	40.1Supported · #120/151	33.9Supported · #133/151	Like-for-likeBenchAlign lane · 6 vs 6 public rows	Gemini 3.1 Pro leads · intervals overlap
Coding	46.2Supported · #99/183	42.6Supported · #128/183	Like-for-likeBenchAlign lane · 5 vs 6 public rows	Gemini 3.1 Pro leads · intervals overlap
Multimodal	79.2#12/48	51.5#35/48	Like-for-likeProvisional lane · 2 vs 2 weighted rows	Gemini 3.1 Pro leads
Knowledge	65.4Supported · #24/181	49.0Estimated · #95/181	Directional onlyBenchAlign lane · 6 vs 6 public rows	Directional only
Reasoning	50.7Unranked · 2 rankable rows	73.7Unranked · 2 rankable rows	Not comparableProvisional lane · 2 vs 0 weighted rows	Not comparable
Math	54.3Unranked · 2 rankable rows	72.8Unranked · 5 rankable rows	Not comparableProvisional lane · 2 vs 2 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Instruction following	Not ranked	82.2#50/120	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.

MMMU-Pro
Multimodal
Gemini 3.1 Pro: 83.9%Qwen3.6-27B: 75.8%Normalized gap 8.1Gemini 3.1 Pro source Qwen3.6-27B source
CharXiv
Multimodal
Gemini 3.1 Pro: 80.2%Qwen3.6-27B: 78.4%Normalized gap 1.8Gemini 3.1 Pro 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

Gemini 3.1 Pro: $0.008; Fits in one request
Qwen3.6-27B: Self-hosted; infrastructure cost varies; Fits in one request

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

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

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.

SpecificationGemini 3.1 ProQwen3.6-27B

Context window

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

Gemini 3.1 Pro

Google Gemini 3.1 Pro Preview model documentation

Qwen3.6-27B

262K

API model ID

Gemini 3.1 Pro

gemini-3.1-pro-preview

Google Gemini 3.1 Pro Preview model documentation

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.

Gemini 3.1 Pro

$0.2 per 1M cached input tokens

Google Gemini API pricing

Qwen3.6-27B

No comparable hosted API rate

Documented inputs

Gemini 3.1 Pro

text, image, video, audio, pdf

Google Gemini 3.1 Pro Preview model documentation

Qwen3.6-27B

Not sourced

Documented outputs

Gemini 3.1 Pro

text

Google Gemini 3.1 Pro Preview model documentation

Qwen3.6-27B

Not sourced

Provider availability

Gemini 3.1 Pro

Preview · Gemini API, Google AI Studio

Google Gemini model catalog

Qwen3.6-27B

Not sourced

Reasoning profile

Gemini 3.1 Pro

Reasoning

Qwen3.6-27B

Reasoning

Weight access

Gemini 3.1 Pro

Proprietary

Qwen3.6-27B

Open Weight

License

Gemini 3.1 Pro

Proprietary

Qwen3.6-27B

Open Weight

Release date

Gemini 3.1 Pro

2026-02-19

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: Gemini 3.1 Pro has the higher public score, 69.82 versus 52.73, and the 90% score intervals do not overlap.
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.

Self-host vs API cost

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

Gemini 3.1 Pro

API / mo$10,500

Self-host / moNot listed

Break-even—

Proprietary model — self-hosting not applicable.

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 evidence60 rows

Agentic

Claw-Eval
Gemini 3.1 Pro57.8%
Source
Qwen3.6-27B72.4%
Source
Qwen3.6-27B leads this result
DeepSearchQA
Gemini 3.1 Pro69.7%
Source
Qwen3.6-27B—
Not directly comparable
τ²-bench results
Gemini 3.1 Pro95.6%
Source
Qwen3.6-27B—
Not directly comparable
Gert Labs
Shared source
Gemini 3.1 Pro56.87%
Qwen3.6-27B54.84%
Gemini 3.1 Pro leads this result
ResearchClawBench
Gemini 3.1 Pro13.3%
Source
Qwen3.6-27B—
Not directly comparable
Terminal-Bench 2.1 (Vals)
Gemini 3.1 Pro70.8%
Source
Qwen3.6-27B—
Not directly comparable
Terminal-Bench 2.0
Gemini 3.1 Pro—
Qwen3.6-27B59.3%
Source
Not directly comparable
QwenClawBench
Gemini 3.1 Pro—
Qwen3.6-27B53.4%
Source
Not directly comparable
QwenWebBench
Gemini 3.1 Pro—
Qwen3.6-27B1487
Source
Not directly comparable
AndroidWorld
Gemini 3.1 Pro—
Qwen3.6-27B70.3%
Source
Not directly comparable

Coding

LiveCodeBench Pro
Gemini 3.1 Pro82.9%
Source
Qwen3.6-27B—
Not directly comparable
React Native Evals
Gemini 3.1 Pro78.9%
Source
Qwen3.6-27B—
Not directly comparable
Vibe Code Bench
Gemini 3.1 Pro32.03%
Source
Qwen3.6-27B—
Not directly comparable
LiveCodeBench (Vals)
Gemini 3.1 Pro88.5%
Source
Qwen3.6-27B—
Not directly comparable
SWE-bench (Vals)
Gemini 3.1 Pro78.8%
Source
Qwen3.6-27B—
Not directly comparable
SWE-bench Verified
Gemini 3.1 Pro—
Qwen3.6-27B77.2%
Source
Not directly comparable
SWE Multilingual
Gemini 3.1 Pro—
Qwen3.6-27B71.3%
Source
Not directly comparable
SWE-bench Pro
Gemini 3.1 Pro—
Qwen3.6-27B53.5%
Source
Not directly comparable
Terminal-Bench 2.0
Gemini 3.1 Pro—
Qwen3.6-27B59.3%
Source
Not directly comparable
LiveCodeBench
Gemini 3.1 Pro—
Qwen3.6-27B83.9%
Source
Not directly comparable
NL2Repo
Gemini 3.1 Pro—
Qwen3.6-27B36.2%
Source
Not directly comparable

Reasoning

ARC-AGI-2
Gemini 3.1 Pro77.1%
Source
Qwen3.6-27B—
Not directly comparable
ARC-AGI-3
Gemini 3.1 Pro0.4%
Source
Qwen3.6-27B—
Not directly comparable

Knowledge

GPQA-D
Gemini 3.1 Pro94.3%
Source
Qwen3.6-27B—
Not directly comparable
HLE w/o tools
Gemini 3.1 Pro45.4%
Source
Qwen3.6-27B—
Not directly comparable
HealthBench Hard
Gemini 3.1 Pro20.6%
Source
Qwen3.6-27B—
Not directly comparable
MedXpertQA (Text)
Gemini 3.1 Pro71.5%
Source
Qwen3.6-27B—
Not directly comparable
GPQA Diamond (Vals)
Gemini 3.1 Pro95.5%
Source
Qwen3.6-27B—
Not directly comparable
MMLU-Pro (Vals)
Gemini 3.1 Pro91.0%
Source
Qwen3.6-27B—
Not directly comparable
MMLU-Pro
Gemini 3.1 Pro—
Qwen3.6-27B86.2%
Source
Not directly comparable
MMLU-Redux
Gemini 3.1 Pro—
Qwen3.6-27B93.5%
Source
Not directly comparable
SuperGPQA
Gemini 3.1 Pro—
Qwen3.6-27B66%
Source
Not directly comparable
C-Eval
Gemini 3.1 Pro—
Qwen3.6-27B91.4%
Source
Not directly comparable
GPQA
Gemini 3.1 Pro—
Qwen3.6-27B87.8%
Source
Not directly comparable
HLE
Gemini 3.1 Pro—
Qwen3.6-27B24%
Source
Not directly comparable

Math

FrontierMath v2 (Tiers 1-3)
Gemini 3.1 Pro36.900%
Source
Qwen3.6-27B—
Not directly comparable
FrontierMath v2 (Tier 4)
Gemini 3.1 Pro16.700%
Source
Qwen3.6-27B—
Not directly comparable
HMMT Feb 2025
Gemini 3.1 Pro—
Qwen3.6-27B93.8%
Source
Not directly comparable
HMMT Nov 2025
Gemini 3.1 Pro—
Qwen3.6-27B90.7%
Source
Not directly comparable
HMMT Feb 2026
Gemini 3.1 Pro—
Qwen3.6-27B84.3%
Source
Not directly comparable
MMAnswerBench
Gemini 3.1 Pro—
Qwen3.6-27B80.8%
Source
Not directly comparable
AIME26
Gemini 3.1 Pro—
Qwen3.6-27B94.1%
Source
Not directly comparable

Multimodal

MMMU-Pro
Gemini 3.1 Pro83.9%
Source
Qwen3.6-27B75.8%
Source
Gemini 3.1 Pro leads this result
CharXiv
Gemini 3.1 Pro80.2%
Source
Qwen3.6-27B78.4%
Source
Gemini 3.1 Pro leads this result
ERQA
Gemini 3.1 Pro69.4%
Source
Qwen3.6-27B62.5%
Source
Gemini 3.1 Pro leads this result
SimpleVQA
Gemini 3.1 Pro72.4%
Source
Qwen3.6-27B56.1%
Source
Gemini 3.1 Pro leads this result
ScreenSpot Pro
Gemini 3.1 Pro84.4%
Source
Qwen3.6-27B—
Not directly comparable
ZeroBench
Gemini 3.1 Pro29.0%
Source
Qwen3.6-27B—
Not directly comparable
MedXpertQA (MM)
Gemini 3.1 Pro81.3%
Source
Qwen3.6-27B—
Not directly comparable
MMMU
Gemini 3.1 Pro—
Qwen3.6-27B82.9%
Source
Not directly comparable
RealWorldQA
Gemini 3.1 Pro—
Qwen3.6-27B84.1%
Source
Not directly comparable
DynaMath
Gemini 3.1 Pro—
Qwen3.6-27B85.6%
Source
Not directly comparable
MStar
Gemini 3.1 Pro—
Qwen3.6-27B81.4%
Source
Not directly comparable
CC-OCR
Gemini 3.1 Pro—
Qwen3.6-27B81.2%
Source
Not directly comparable
CountBench
Gemini 3.1 Pro—
Qwen3.6-27B97.8%
Source
Not directly comparable
RefCOCO (avg)
Gemini 3.1 Pro—
Qwen3.6-27B92.5%
Source
Not directly comparable
Video-MME (with subtitle)
Gemini 3.1 Pro—
Qwen3.6-27B87.7%
Source
Not directly comparable
VideoMMMU
Gemini 3.1 Pro—
Qwen3.6-27B84.4%
Source
Not directly comparable
MLVU (M-Avg)
Gemini 3.1 Pro—
Qwen3.6-27B86.6%
Source
Not directly comparable
V*
Gemini 3.1 Pro—
Qwen3.6-27B94.7%
Source
Not directly comparable

Frequently asked questions

Which is better, Gemini 3.1 Pro or Qwen3.6-27B?

Gemini 3.1 Pro has the higher public score, 69.82 versus 52.73, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.

Which is better for coding, Gemini 3.1 Pro or Qwen3.6-27B?

Gemini 3.1 Pro leads the public coding lane, 46.2 to 42.6, with Supported evidence for both models, although the 90% intervals overlap.

Which is better for agentic tasks, Gemini 3.1 Pro or Qwen3.6-27B?

Gemini 3.1 Pro leads the public agentic tasks lane, 40.1 to 33.9, with Supported evidence for both models, although the 90% intervals overlap.

Which costs less, Gemini 3.1 Pro 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, Gemini 3.1 Pro or Qwen3.6-27B?

Gemini 3.1 Pro has the larger documented context window: 1M, compared with 262K.

Related comparisons

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Last updated September 4, 2026

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Gemini 3.1 Pro 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

Multimodal

Knowledge

Reasoning

Math

Multilingual

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

Reasoning

Knowledge

Math

Multimodal

Frequently asked questions

Which is better, Gemini 3.1 Pro or Qwen3.6-27B?

Which is better for coding, Gemini 3.1 Pro or Qwen3.6-27B?

Which is better for agentic tasks, Gemini 3.1 Pro or Qwen3.6-27B?

Which costs less, Gemini 3.1 Pro or Qwen3.6-27B?

Which has the larger context window, Gemini 3.1 Pro or Qwen3.6-27B?

Related comparisons

Watch Gemini 3.1 Pro vs Qwen3.6-27B