Model Comparison

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

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

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Performance Benchmarks

Comparative analysis across standard metrics

25 benchmarks

Qwen3.5-397B-A17B outperforms in 25 benchmarks (AA-LCR, BFCL-V4, C-Eval, DeepPlanning, Global PIQA, GPQA, HMMT 2025, HMMT25, IFBench, IFEval, Include, LiveCodeBench v6, LongBench v2, MAXIFE, MMLU-Pro, MMLU-ProX, MMLU-Redux, MMMLU, Multi-Challenge, NOVA-63, PolyMATH, SuperGPQA, t2-bench, VITA-Bench, WMT24++), while Qwen3.5-4B is better at 0 benchmarks.

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

Fri Apr 17 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
Fri Apr 17 2026 • llm-stats.com
Alibaba Cloud / Qwen Team
Qwen3.5-397B-A17B
Input tokens$0.60
Output tokens$3.60
Best providerNovita
Alibaba Cloud / Qwen Team
Qwen3.5-4B
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
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Model Size

Parameter count comparison

393.0B diff

Qwen3.5-397B-A17B has 393.0B more parameters than Qwen3.5-4B, making it 9825.0% larger.

Alibaba Cloud / Qwen Team
Qwen3.5-397B-A17B
397.0Bparameters
Alibaba Cloud / Qwen Team
Qwen3.5-4B
4.0Bparameters
397.0B
Qwen3.5-397B-A17B
4.0B
Qwen3.5-4B

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
Alibaba Cloud / Qwen Team
Qwen3.5-4B
Input- tokens
Output- tokens
Fri Apr 17 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Both Qwen3.5-397B-A17B and Qwen3.5-4B 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

Qwen3.5-4B

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

Qwen3.5-4B

Apache 2.0

Open weights

Release Timeline

When each model was launched

Qwen3.5-397B-A17B was released on 2026-02-16, while Qwen3.5-4B was released on 2026-03-02.

Qwen3.5-4B is 0 month newer than Qwen3.5-397B-A17B.

Qwen3.5-397B-A17B

Feb 16, 2026

2 months ago

Qwen3.5-4B

Mar 2, 2026

1 months ago

2w newer

Knowledge Cutoff

When training data ends

Neither model specifies a knowledge cutoff date.

Unable to compare the recency of their training data.

No cutoff dates available

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

Alibaba Cloud / Qwen Team

Qwen3.5-397B-A17B

View details

Alibaba Cloud / Qwen Team

Larger context window (262,144 tokens)
Higher AA-LCR score (68.7% vs 57.0%)
Higher BFCL-V4 score (72.9% vs 50.3%)
Higher C-Eval score (93.0% vs 85.1%)
Higher DeepPlanning score (34.3% vs 17.6%)
Higher Global PIQA score (89.8% vs 78.9%)
Higher GPQA score (88.4% vs 76.2%)
Higher HMMT 2025 score (94.8% vs 74.0%)
Higher HMMT25 score (92.7% vs 76.8%)
Higher IFBench score (76.5% vs 59.2%)
Higher IFEval score (92.6% vs 89.8%)
Higher Include score (85.6% vs 71.0%)
Higher LiveCodeBench v6 score (83.6% vs 55.8%)
Higher LongBench v2 score (63.2% vs 50.0%)
Higher MAXIFE score (88.2% vs 78.0%)
Higher MMLU-Pro score (87.8% vs 79.1%)
Higher MMLU-ProX score (84.7% vs 71.5%)
Higher MMLU-Redux score (94.9% vs 88.8%)
Higher MMMLU score (88.5% vs 76.1%)
Higher Multi-Challenge score (67.6% vs 49.0%)
Higher NOVA-63 score (59.1% vs 54.3%)
Higher PolyMATH score (73.3% vs 51.1%)
Higher SuperGPQA score (70.4% vs 52.9%)
Higher t2-bench score (86.7% vs 79.9%)
Higher VITA-Bench score (49.7% vs 22.0%)
Higher WMT24++ score (78.9% vs 66.6%)
Alibaba Cloud / Qwen Team

Qwen3.5-4B

View details

Alibaba Cloud / Qwen Team

Detailed Comparison

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

FAQ

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

Qwen3.5-397B-A17B significantly outperforms across most benchmarks. Qwen3.5-397B-A17B is made by Alibaba Cloud / Qwen Team and Qwen3.5-4B is made by Alibaba Cloud / Qwen Team. 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%. Qwen3.5-4B scores IFEval: 89.8%, MMLU-Redux: 88.8%, C-Eval: 85.1%, t2-bench: 79.9%, MMLU-Pro: 79.1%.
Qwen3.5-397B-A17B supports 262K tokens and Qwen3.5-4B supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.