Model Comparison

Qwen3.5-397B-A17B vs Gemma 4 E4B

Qwen3.5-397B-A17B significantly outperforms across most benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

6 benchmarks

Qwen3.5-397B-A17B outperforms in 6 benchmarks (AIME 2026, GPQA, LiveCodeBench v6, MMLU-Pro, MMMLU, t2-bench), while Gemma 4 E4B is better at 0 benchmarks.

Qwen3.5-397B-A17B significantly outperforms across most benchmarks.

Thu Apr 16 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Cost data unavailable.

Lowest available price from all providers
Thu Apr 16 2026 • llm-stats.com
Alibaba Cloud / Qwen Team
Qwen3.5-397B-A17B
Input tokens$0.60
Output tokens$3.60
Best providerNovita
Google
Gemma 4 E4B
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

389.0B diff

Qwen3.5-397B-A17B has 389.0B more parameters than Gemma 4 E4B, making it 4862.5% larger.

Alibaba Cloud / Qwen Team
Qwen3.5-397B-A17B
397.0Bparameters
Google
Gemma 4 E4B
8.0Bparameters
397.0B
Qwen3.5-397B-A17B
8.0B
Gemma 4 E4B

Context Window

Maximum input and output token capacity

Only Qwen3.5-397B-A17B specifies input context (262,144 tokens). Only Qwen3.5-397B-A17B specifies output context (64,000 tokens).

Alibaba Cloud / Qwen Team
Qwen3.5-397B-A17B
Input262,144 tokens
Output64,000 tokens
Google
Gemma 4 E4B
Input- tokens
Output- tokens
Thu Apr 16 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Both Qwen3.5-397B-A17B and Gemma 4 E4B support multimodal inputs.

They are both capable of processing various types of data, offering versatility in application.

Qwen3.5-397B-A17B

Text
Images
Audio
Video

Gemma 4 E4B

Text
Images
Audio
Video

License

Usage and distribution terms

Both models are licensed under Apache 2.0.

Both models share the same licensing terms, providing consistent usage rights.

Qwen3.5-397B-A17B

Apache 2.0

Open weights

Gemma 4 E4B

Apache 2.0

Open weights

Release Timeline

When each model was launched

Qwen3.5-397B-A17B was released on 2026-02-16, while Gemma 4 E4B was released on 2026-04-02.

Gemma 4 E4B is 2 months newer than Qwen3.5-397B-A17B.

Qwen3.5-397B-A17B

Feb 16, 2026

1 months ago

Gemma 4 E4B

Apr 2, 2026

2 weeks ago

1mo newer

Knowledge Cutoff

When training data ends

Gemma 4 E4B has a documented knowledge cutoff of 2025-01-01, while Qwen3.5-397B-A17B's cutoff date is not specified.

We can confirm Gemma 4 E4B's training data extends to 2025-01-01, but cannot make a direct comparison without Qwen3.5-397B-A17B's cutoff date.

Qwen3.5-397B-A17B

—

Gemma 4 E4B

Jan 2025

Outputs Comparison

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Key Takeaways

Alibaba Cloud / Qwen Team

Qwen3.5-397B-A17B

View details

Alibaba Cloud / Qwen Team

Larger context window (262,144 tokens)
Higher AIME 2026 score (91.3% vs 42.5%)
Higher GPQA score (88.4% vs 58.6%)
Higher LiveCodeBench v6 score (83.6% vs 52.0%)
Higher MMLU-Pro score (87.8% vs 69.4%)
Higher MMMLU score (88.5% vs 76.6%)
Higher t2-bench score (86.7% vs 57.5%)
Google

Detailed Comparison

AI Model Comparison Table
Feature
Alibaba Cloud / Qwen Team
Qwen3.5-397B-A17B
Google
Gemma 4 E4B

FAQ

Common questions about Qwen3.5-397B-A17B vs Gemma 4 E4B

Qwen3.5-397B-A17B significantly outperforms across most benchmarks. Qwen3.5-397B-A17B is made by Alibaba Cloud / Qwen Team and Gemma 4 E4B is made by Google. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.
Qwen3.5-397B-A17B scores MMLU-Redux: 94.9%, HMMT 2025: 94.8%, C-Eval: 93.0%, HMMT25: 92.7%, IFEval: 92.6%. Gemma 4 E4B scores MMMLU: 76.6%, MMLU-Pro: 69.4%, MathVision: 59.5%, GPQA: 58.6%, t2-bench: 57.5%.
Qwen3.5-397B-A17B supports 262K tokens and Gemma 4 E4B supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Qwen3.5-397B-A17B is developed by Alibaba Cloud / Qwen Team and Gemma 4 E4B is developed by Google.