Gemini 3.7 Flash vs Qwen3.8-27B

Updated September 28, 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

Gemini 3.7 Flash has the higher public score estimate, 67.53 versus 55.26, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 11 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

67.53/100

Supported · Public rank #22

90% interval 62.7–72.3

Model B

Qwen3.8-27B

Alibaba

55.26/100

Estimated · Public rank #58

90% interval 47.7–62.8

Shared results: 11
Gemini 3.7 Flash only: 14
Qwen3.8-27B only: 22
Like-for-like categories: 4 / 8

Supported: Gemini 3.7 Flash · Estimated: Qwen3.8-27BHow 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, 59.4 to 48.7, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Qwen3.8-27B
Qwen3.8-27B leads on the public agentic lane, 61.2 to 57.9, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Long documents
Prompts that approach the documented context limit
Gemini 3.7 Flash
Gemini 3.7 Flash 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

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.

59.4Gemini 3.7 Flash48.7Qwen3.8-27B

Like-for-like · BenchAlign v5.7

Gemini 3.7 Flash leads the like-for-like coding row, although the 90% intervals overlap.

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.

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.

MMLU-Pro (Vals)Knowledge
Normalized gap 5.8
Gemini 3.7 Flash:90.1%
Qwen3.8-27B:84.3%
Gemini 3.7 Flash source Qwen3.8-27B source
LiveCodeBench (Vals)Coding
Normalized gap 4.7
Gemini 3.7 Flash:88.7%
Qwen3.8-27B:84.0%
Gemini 3.7 Flash source Qwen3.8-27B source
CharXivMultimodal
Normalized gap 1.5
Gemini 3.7 Flash:88.7%
Qwen3.8-27B:90.2%
Gemini 3.7 Flash source Qwen3.8-27B 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 v5.7 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.

Agentic

Like-for-like

Gemini 3.7 Flash: 57.9; Supported · #23/117
Qwen3.8-27B: 61.2; Supported · #18/117
Basis: BenchAlign v5.7 lane · 7 vs 8 public rows
Reading: Qwen3.8-27B leads · intervals overlap

Coding

Like-for-like

Gemini 3.7 Flash: 59.4; Supported · #20/142
Qwen3.8-27B: 48.7; Supported · #45/142
Basis: BenchAlign v5.7 lane · 6 vs 8 public rows
Reading: Gemini 3.7 Flash leads · intervals overlap

Multimodal

Like-for-like

Gemini 3.7 Flash: 83.6; #10/50
Qwen3.8-27B: 80.9; #11/50
Basis: Provisional lane · 1 vs 1 weighted rows
Reading: Gemini 3.7 Flash leads

Knowledge

Like-for-like

Gemini 3.7 Flash: 71.0; Supported · #11/168
Qwen3.8-27B: 49.2; Supported · #63/168
Basis: BenchAlign v5.7 lane · 6 vs 6 public rows
Reading: Gemini 3.7 Flash leads

Reasoning

Not comparable

Gemini 3.7 Flash: 77.9; Unranked · 5 rankable rows
Qwen3.8-27B: 78.7; #8/27
Basis: Provisional lane · 1 vs 0 weighted rows
Reading: Not comparable

Multilingual

Not comparable

Gemini 3.7 Flash: Not ranked
Qwen3.8-27B: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

Gemini 3.7 Flash: Not ranked
Qwen3.8-27B: 83.2; #45/124
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Math

Not comparable

Gemini 3.7 Flash: Not ranked
Qwen3.8-27B: 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 Qwen3.8-27B
Category	Gemini 3.7 Flash	Qwen3.8-27B	Basis	Reading
Agentic	57.9Supported · #23/117	61.2Supported · #18/117	Like-for-likeBenchAlign v5.7 lane · 7 vs 8 public rows	Qwen3.8-27B leads · intervals overlap
Coding	59.4Supported · #20/142	48.7Supported · #45/142	Like-for-likeBenchAlign v5.7 lane · 6 vs 8 public rows	Gemini 3.7 Flash leads · intervals overlap
Multimodal	83.6#10/50	80.9#11/50	Like-for-likeProvisional lane · 1 vs 1 weighted rows	Gemini 3.7 Flash leads
Knowledge	71.0Supported · #11/168	49.2Supported · #63/168	Like-for-likeBenchAlign v5.7 lane · 6 vs 6 public rows	Gemini 3.7 Flash leads
Reasoning	77.9Unranked · 5 rankable rows	78.7#8/27	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	83.2#45/124	Not comparableProvisional lane · 0 vs 1 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
Qwen3.8-27B: Self-hosted; infrastructure cost varies; Fits in one request

Qwen3.8-27B 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
Qwen3.8-27B: Self-hosted; infrastructure cost varies; Fits in one request

Qwen3.8-27B 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
Qwen3.8-27B: Self-hosted; infrastructure cost varies; Fits in one request; Cached-input rate unavailable

Qwen3.8-27B has no comparable published API token rate.

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

Specification differences

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

SpecificationGemini 3.7 FlashQwen3.8-27B

Context window

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

Gemini 3.7 Flash

Google Gemini 3.7 Flash API documentation

Qwen3.8-27B

262K

Qwen3.8-27B model card

API model ID

Gemini 3.7 Flash

gemini-3.7-flash

Google Gemini 3.7 Flash API documentation

Qwen3.8-27B

Not sourced

Cached-input rate

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

Gemini 3.7 Flash

$0.075 per 1M cached input tokens

Google Gemini API pricing

Qwen3.8-27B

No comparable hosted API rate

Qwen3.8-27B model card

Documented inputs

Gemini 3.7 Flash

text, image, video, audio, pdf

Google Gemini 3.7 Flash API documentation

Qwen3.8-27B

Not sourced

Documented outputs

Gemini 3.7 Flash

text

Google Gemini 3.7 Flash API documentation

Qwen3.8-27B

Not sourced

Provider availability

Gemini 3.7 Flash

Generally Available · Gemini API, Google AI Studio, Gemini App - Spark, Gemini Enterprise App, Gemini Enterprise Agent Platform, Google Antigravity

Google DeepMind Gemini 3.7 Flash model card

Qwen3.8-27B

Not sourced

Reasoning profile

Gemini 3.7 Flash

Reasoning

Qwen3.8-27B

Reasoning

Weight access

Gemini 3.7 Flash

Proprietary

Qwen3.8-27B

Open Weight

License

Gemini 3.7 Flash

Proprietary

Qwen3.8-27B

Open Weight

Release date

Gemini 3.7 Flash

2026-08-13

Qwen3.8-27B

2026-08-05

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 3.7 Flash has the higher public score estimate, 67.53 versus 55.26, but the 90% score intervals overlap.
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 Qwen3.8-27B?

Gemini 3.7 Flash has the higher public score estimate, 67.53 versus 55.26, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, Gemini 3.7 Flash or Qwen3.8-27B?

Gemini 3.7 Flash leads the public coding lane, 59.4 to 48.7, with Supported evidence for both models, although the 90% intervals overlap.

Which is better for agentic tasks, Gemini 3.7 Flash or Qwen3.8-27B?

Qwen3.8-27B leads the public agentic tasks lane, 61.2 to 57.9, with Supported evidence for both models, although the 90% intervals overlap.

Which costs less, Gemini 3.7 Flash or Qwen3.8-27B?

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 3.7 Flash or Qwen3.8-27B?

Gemini 3.7 Flash has the larger documented context window: 1M, compared with 262K.

Benchmark evidence

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

Browse raw public benchmark evidence47 rows

Agentic

Terminal-Bench 2.1
Gemini 3.7 Flash85.8%
Source
Qwen3.8-27B73.0%
Source
Gemini 3.7 Flash leads this result
Terminal-Bench 3.0
Gemini 3.7 Flash14.9%
Source
Qwen3.8-27B—
Not directly comparable
AutomationBench
Gemini 3.7 Flash30.4%
Source
Qwen3.8-27B—
Not directly comparable
OSWorld 2.0
Gemini 3.7 Flash47.9%
Source
Qwen3.8-27B—
Not directly comparable
Agents' Last Exam
Gemini 3.7 Flash26.3%
Source
Qwen3.8-27B42.9%
Source
Qwen3.8-27B leads this result
Terminal-Bench 2.1 (Vals)
Gemini 3.7 Flash77.5%
Source
Qwen3.8-27B58.4%
Source
Gemini 3.7 Flash leads this result
ApprenticeBench
Gemini 3.7 Flash16%
Source
Qwen3.8-27B—
Not directly comparable
CoWorkBench
Gemini 3.7 Flash—
Qwen3.8-27B70.7%
Source
Not directly comparable
JobBench
Gemini 3.7 Flash—
Qwen3.8-27B33.4%
Source
Not directly comparable
OSWorld-Verified
Gemini 3.7 Flash—
Qwen3.8-27B84.3%
Source
Not directly comparable
WebArena-Verified
Gemini 3.7 Flash—
Qwen3.8-27B64.8%
Source
Not directly comparable
AndroidWorld
Gemini 3.7 Flash—
Qwen3.8-27B81.9%
Source
Not directly comparable

Coding

FrontierCode 1.1 Main
Gemini 3.7 Flash43.6%
Source
Qwen3.8-27B—
Not directly comparable
DeepSWE
Gemini 3.7 Flash65.3%
Source
Qwen3.8-27B42.2%
Source
Gemini 3.7 Flash leads this result
Terminal-Bench 2.1
Gemini 3.7 Flash85.8%
Source
Qwen3.8-27B73.0%
Source
Gemini 3.7 Flash leads this result
FrontierSWE v2
Gemini 3.7 Flash20.3%
Source
Qwen3.8-27B—
Not directly comparable
LiveCodeBench (Vals)
Gemini 3.7 Flash88.7%
Source
Qwen3.8-27B84.0%
Source
Gemini 3.7 Flash leads this result
SWE-bench (Vals)
Gemini 3.7 Flash80.8%
Source
Qwen3.8-27B86.0%
Source
Qwen3.8-27B leads this result
SWE-bench Pro
Gemini 3.7 Flash—
Qwen3.8-27B61.7%
Source
Not directly comparable
NL2Repo
Gemini 3.7 Flash—
Qwen3.8-27B42.3%
Source
Not directly comparable
LiveCodeBench v6
Gemini 3.7 Flash—
Qwen3.8-27B90.3%
Source
Not directly comparable
VulcanBench v3
Gemini 3.7 Flash—
Qwen3.8-27B82.6%
Source
Not directly comparable

Reasoning

MRCR v2 64K-128K
Gemini 3.7 Flash97%
Source
Qwen3.8-27B—
Not directly comparable
ARC-AGI-1
Gemini 3.7 Flash95.50%
Source
Qwen3.8-27B—
Not directly comparable
ARC-AGI-2
Gemini 3.7 Flash84.6%
Source
Qwen3.8-27B—
Not directly comparable

Multimodal

CharXiv w/o tools
Gemini 3.7 Flash84.5%
Source
Qwen3.8-27B83.7%
Source
Gemini 3.7 Flash leads this result
CharXiv
Gemini 3.7 Flash88.7%
Source
Qwen3.8-27B90.2%
Source
Qwen3.8-27B leads this result
LVBench
Gemini 3.7 Flash85.4%
Source
Qwen3.8-27B—
Not directly comparable
MathVision
Gemini 3.7 Flash—
Qwen3.8-27B90.0%
Source
Not directly comparable
MathVision w/ Python
Gemini 3.7 Flash—
Qwen3.8-27B94.6%
Source
Not directly comparable
BabyVision
Gemini 3.7 Flash—
Qwen3.8-27B65.7%
Source
Not directly comparable
BabyVision w/ Python
Gemini 3.7 Flash—
Qwen3.8-27B85.6%
Source
Not directly comparable
Vision2Web
Gemini 3.7 Flash—
Qwen3.8-27B62.9%
Source
Not directly comparable
OmniDocBench 1.5
Gemini 3.7 Flash—
Qwen3.8-27B91.1%
Source
Not directly comparable
RealWorldQA
Gemini 3.7 Flash—
Qwen3.8-27B85.9%
Source
Not directly comparable
ERQA
Gemini 3.7 Flash—
Qwen3.8-27B65.5%
Source
Not directly comparable

Knowledge

HLE-Verified
Gemini 3.7 Flash53.6%
Source
Qwen3.8-27B—
Not directly comparable
LABBench2
Gemini 3.7 Flash82.1%
Source
Qwen3.8-27B—
Not directly comparable
BioMysteryBench (human-solvable)
Gemini 3.7 Flash87.1%
Source
Qwen3.8-27B—
Not directly comparable
BioMysteryBench (human-difficult)
Gemini 3.7 Flash43.5%
Source
Qwen3.8-27B—
Not directly comparable
GPQA Diamond (Vals)
Gemini 3.7 Flash93.9%
Source
Qwen3.8-27B88.9%
Source
Gemini 3.7 Flash leads this result
MMLU-Pro (Vals)
Gemini 3.7 Flash90.1%
Source
Qwen3.8-27B84.3%
Source
Gemini 3.7 Flash leads this result
GPQA
Gemini 3.7 Flash—
Qwen3.8-27B89.2%
Source
Not directly comparable
GPQA-D
Gemini 3.7 Flash—
Qwen3.8-27B89.2%
Source
Not directly comparable
HLE
Gemini 3.7 Flash—
Qwen3.8-27B30.8%
Source
Not directly comparable
HLE w/o tools
Gemini 3.7 Flash—
Qwen3.8-27B30.8%
Source
Not directly comparable

Instruction following

IFBench
Gemini 3.7 Flash—
Qwen3.8-27B79.5%
Source
Not directly comparable

47 public results · 11 shared

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Last updated September 28, 2026

Gemini 3.7 Flash vs Qwen3.8-27B

Decision reading

Which one for your work

Coding work

Agentic work

Long documents

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

Agentic

Coding

Multimodal

Knowledge

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 Qwen3.8-27B?

Which is better for coding, Gemini 3.7 Flash or Qwen3.8-27B?

Which is better for agentic tasks, Gemini 3.7 Flash or Qwen3.8-27B?

Which costs less, Gemini 3.7 Flash or Qwen3.8-27B?

Which has the larger context window, Gemini 3.7 Flash or Qwen3.8-27B?

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Instruction following

Related comparisons

Watch Gemini 3.7 Flash vs Qwen3.8-27B