Model A

Google

42.2/100

Estimated · Public rank #180

90% interval 30.7–53.7

Gemma 4 E2B vs Qwen3.5 397B

Updated August 18, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B

Qwen3.5 397B

Alibaba

57.5/100

Estimated · Public rank #82

90% interval 45.9–69.0

Decision reading

Qwen3.5 397B has the higher public score estimate, 57.46 versus 42.18, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

2 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.

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.

No workload recommendation clears the current evidence threshold.

Use the matched evidence, workload costs, and sourced specifications below instead of treating a point score as a universal answer.

Show secondary and unsupported calls

Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
Confidence: limited
Long documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
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
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. Gemma 4 E2B does not fit this workload in one request. Qwen3.5 397B does not fit this workload in one request. Qwen3.5 397B has no published cached-input rate, so cached tokens use its listed input rate. Gemma 4 E2B has no comparable published API token rate.
Confidence: rate-fallback
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.

2 Shared

36 Qwen3.5 397B only

Shared results: 2
Gemma 4 E2B only: 0
Qwen3.5 397B only: 36
Like-for-like categories: 0 / 8

1 category uses different evidence sets. Those rows remain visible for coverage context but do not name a winner.

Category results, on a stated basis

Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.

Knowledge

Directional only

Gemma 4 E2B: 56.9
Qwen3.5 397B: 56.6
Weighted basis: 2 vs 4 rows
Reading: Directional only

Agentic

Not comparable

Gemma 4 E2B: Not measured
Qwen3.5 397B: 56.5
Weighted basis: 0 vs 2 rows
Reading: Not comparable

Coding

Not comparable

Gemma 4 E2B: Not measured
Qwen3.5 397B: 66.5
Weighted basis: 0 vs 2 rows
Reading: Not comparable

Reasoning

Not comparable

Gemma 4 E2B: Not measured
Qwen3.5 397B: 63.2
Weighted basis: 0 vs 1 rows
Reading: Not comparable

Math

Not comparable

Gemma 4 E2B: Not measured
Qwen3.5 397B: 90.6
Weighted basis: 0 vs 2 rows
Reading: Not comparable

Multilingual

Not comparable

Gemma 4 E2B: Not measured
Qwen3.5 397B: 84.7
Weighted basis: 0 vs 1 rows
Reading: Not comparable

Multimodal

Not comparable

Gemma 4 E2B: Not measured
Qwen3.5 397B: 79.6
Weighted basis: 0 vs 2 rows
Reading: Not comparable

Instruction following

Not comparable

Gemma 4 E2B: Not measured
Qwen3.5 397B: 92.6
Weighted basis: 0 vs 1 rows
Reading: Not comparable

Category averages with the server-provided evidence basis for Gemma 4 E2B and Qwen3.5 397B
Category	Gemma 4 E2B	Qwen3.5 397B	Weighted basis	Reading
Knowledge	56.9	56.6	Directional only2 vs 4 rows	Directional only
Agentic	Not measured	56.5	Not comparable0 vs 2 rows	Not comparable
Coding	Not measured	66.5	Not comparable0 vs 2 rows	Not comparable
Reasoning	Not measured	63.2	Not comparable0 vs 1 rows	Not comparable
Math	Not measured	90.6	Not comparable0 vs 2 rows	Not comparable
Multilingual	Not measured	84.7	Not comparable0 vs 1 rows	Not comparable
Multimodal	Not measured	79.6	Not comparable0 vs 2 rows	Not comparable
Instruction following	Not measured	92.6	Not comparable0 vs 1 rows	Not comparable

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 E2B: 43.4%Qwen3.5 397B: 88.4%Normalized gap 45.0Gemma 4 E2B source Qwen3.5 397B source
MMLU-Pro
Knowledge
Gemma 4 E2B: 60%Qwen3.5 397B: 87.8%Normalized gap 27.8Gemma 4 E2B source Qwen3.5 397B 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 E2B: Self-hosted; infrastructure cost varies; Fits in one request
Qwen3.5 397B: $0.0024; Fits in one request

Gemma 4 E2B has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Gemma 4 E2B: Self-hosted; infrastructure cost varies; Fits in one request
Qwen3.5 397B: $0.0408; Fits in one request

Gemma 4 E2B has no comparable published API token rate.

Cache-heavy agent loop

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

Gemma 4 E2B: Self-hosted; infrastructure cost varies; Does not fit in one request; Cached-input rate unavailable
Qwen3.5 397B: $0.168; Does not fit in one request; Cached input priced at the published list-input rate

Gemma 4 E2B does not fit this workload in one request. Qwen3.5 397B does not fit this workload in one request. Qwen3.5 397B has no published cached-input rate, so cached tokens use its listed input rate. Gemma 4 E2B 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 E2BQwen3.5 397B

Context window

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

Gemma 4 E2B

128K

Google Gemma 4 model documentation

Qwen3.5 397B

128K

Gemma 4 E2B

Reasoning

Qwen3.5 397B

Non-Reasoning

Weight access

Gemma 4 E2B

Open Weight

Qwen3.5 397B

Open Weight

License

Gemma 4 E2B

Open Weight

Qwen3.5 397B

Open Weight

Release date

Gemma 4 E2B

2026-04-02

Qwen3.5 397B

2026-02-16

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: Qwen3.5 397B has the higher public score estimate, 57.46 versus 42.18, but the 90% score intervals overlap.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: Both models list 128K.

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

Benchmark evidence

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

Browse raw public benchmark evidence38 rows

Agentic

Terminal-Bench 2.0
Gemma 4 E2B—
Qwen3.5 397B52.5%
Source
Not directly comparable
BrowseComp
Gemma 4 E2B—
Qwen3.5 397B62%
Source
Not directly comparable
Claw-Eval
Gemma 4 E2B—
Qwen3.5 397B56.8%
Source
Not directly comparable
QwenClawBench
Gemma 4 E2B—
Qwen3.5 397B51.8%
Source
Not directly comparable
τ³-bench results
Gemma 4 E2B—
Qwen3.5 397B68.4%
Source
Not directly comparable
VITA-Bench
Gemma 4 E2B—
Qwen3.5 397B43.7%
Source
Not directly comparable
DeepPlanning
Gemma 4 E2B—
Qwen3.5 397B37.6%
Source
Not directly comparable
Toolathlon
Gemma 4 E2B—
Qwen3.5 397B36.3%
Source
Not directly comparable
MCP Atlas
Gemma 4 E2B—
Qwen3.5 397B46.1%
Source
Not directly comparable
MCP-Tasks
Gemma 4 E2B—
Qwen3.5 397B74.2%
Source
Not directly comparable
WideResearch
Gemma 4 E2B—
Qwen3.5 397B74.0%
Source
Not directly comparable
Gert Labs
Gemma 4 E2B—
Qwen3.5 397B46.76%
Source
Not directly comparable
ResearchClawBench
Gemma 4 E2B—
Qwen3.5 397B14.2%
Source
Not directly comparable

Coding

SWE-bench Verified
Gemma 4 E2B—
Qwen3.5 397B76.2%
Source
Not directly comparable
LiveCodeBench v6
Gemma 4 E2B—
Qwen3.5 397B83.6%
Source
Not directly comparable
SWE-bench Pro
Gemma 4 E2B—
Qwen3.5 397B50.9%
Source
Not directly comparable

Reasoning

LongBench v2
Gemma 4 E2B—
Qwen3.5 397B63.2%
Source
Not directly comparable
AI-Needle
Gemma 4 E2B—
Qwen3.5 397B68.7%
Source
Not directly comparable

Knowledge

GPQA
Gemma 4 E2B43.4%
Source
Qwen3.5 397B88.4%
Source
Qwen3.5 397B leads this result
MMLU-Pro
Gemma 4 E2B60%
Source
Qwen3.5 397B87.8%
Source
Qwen3.5 397B leads this result
SuperGPQA
Gemma 4 E2B—
Qwen3.5 397B70.4%
Source
Not directly comparable
MMLU-Redux
Gemma 4 E2B—
Qwen3.5 397B94.9%
Source
Not directly comparable
C-Eval
Gemma 4 E2B—
Qwen3.5 397B93%
Source
Not directly comparable
HLE
Gemma 4 E2B—
Qwen3.5 397B28.7%
Source
Not directly comparable

Math

AIME26
Gemma 4 E2B—
Qwen3.5 397B93.3%
Source
Not directly comparable
HMMT Feb 2025
Gemma 4 E2B—
Qwen3.5 397B94.8%
Source
Not directly comparable
HMMT Nov 2025
Gemma 4 E2B—
Qwen3.5 397B92.7%
Source
Not directly comparable
HMMT Feb 2026
Gemma 4 E2B—
Qwen3.5 397B87.9%
Source
Not directly comparable
MMAnswerBench
Gemma 4 E2B—
Qwen3.5 397B80.9%
Source
Not directly comparable

Multilingual

MMLU-ProX
Gemma 4 E2B—
Qwen3.5 397B84.7%
Source
Not directly comparable
NOVA-63
Gemma 4 E2B—
Qwen3.5 397B59.1%
Source
Not directly comparable

Multimodal

MMMU-Pro
Gemma 4 E2B—
Qwen3.5 397B79%
Source
Not directly comparable
MathVision
Gemma 4 E2B—
Qwen3.5 397B88.6%
Source
Not directly comparable
CharXiv
Gemma 4 E2B—
Qwen3.5 397B80.8%
Source
Not directly comparable
VideoMMMU
Gemma 4 E2B—
Qwen3.5 397B84.7%
Source
Not directly comparable
ScreenSpot Pro
Gemma 4 E2B—
Qwen3.5 397B65.6%
Source
Not directly comparable
V*
Gemma 4 E2B—
Qwen3.5 397B95.8%
Source
Not directly comparable

Instruction following

IFEval
Gemma 4 E2B—
Qwen3.5 397B92.6%
Source
Not directly comparable

Frequently asked questions

Which is better, Gemma 4 E2B or Qwen3.5 397B?

Qwen3.5 397B has the higher public score estimate, 57.46 versus 42.18, 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 E2B or Qwen3.5 397B?

The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.

Which is better for agentic tasks, Gemma 4 E2B or Qwen3.5 397B?

The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.

Which costs less, Gemma 4 E2B or Qwen3.5 397B?

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 E2B or Qwen3.5 397B?

Both models list the same context window, 128K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 18, 2026

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Gemma 4 E2B vs Qwen3.5 397B

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

Knowledge

Agentic

Coding

Reasoning

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

Benchmark evidence

Agentic

Coding

Reasoning

Knowledge

Math

Multilingual

Multimodal

Instruction following

Frequently asked questions

Which is better, Gemma 4 E2B or Qwen3.5 397B?

Which is better for coding, Gemma 4 E2B or Qwen3.5 397B?

Which is better for agentic tasks, Gemma 4 E2B or Qwen3.5 397B?

Which costs less, Gemma 4 E2B or Qwen3.5 397B?

Which has the larger context window, Gemma 4 E2B or Qwen3.5 397B?

Related comparisons

Watch Gemma 4 E2B vs Qwen3.5 397B