Model Comparison

Qwen3.5-397B-A17B vs DeepSeek VL2

Comparing Qwen3.5-397B-A17B and DeepSeek VL2 across benchmarks, pricing, and capabilities.

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Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

Qwen3.5-397B-A17B and DeepSeek VL2 don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

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
DeepSeek
DeepSeek VL2
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

370.0B diff

Qwen3.5-397B-A17B has 370.0B more parameters than DeepSeek VL2, making it 1370.4% larger.

Alibaba Cloud / Qwen Team
Qwen3.5-397B-A17B
397.0Bparameters
DeepSeek
DeepSeek VL2
27.0Bparameters
397.0B
Qwen3.5-397B-A17B
27.0B
DeepSeek VL2

Context Window

Maximum input and output token capacity

Qwen3.5-397B-A17B accepts 262,144 input tokens compared to DeepSeek VL2's 129,280 tokens. DeepSeek VL2 can generate longer responses up to 129,280 tokens, while Qwen3.5-397B-A17B is limited to 64,000 tokens.

Alibaba Cloud / Qwen Team
Qwen3.5-397B-A17B
Input262,144 tokens
Output64,000 tokens
DeepSeek
DeepSeek VL2
Input129,280 tokens
Output129,280 tokens
Thu Apr 16 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Both Qwen3.5-397B-A17B and DeepSeek VL2 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

DeepSeek VL2

Text
Images
Audio
Video

License

Usage and distribution terms

Qwen3.5-397B-A17B is licensed under Apache 2.0, while DeepSeek VL2 uses deepseek.

License differences may affect how you can use these models in commercial or open-source projects.

Qwen3.5-397B-A17B

Apache 2.0

Open weights

DeepSeek VL2

deepseek

Open weights

Release Timeline

When each model was launched

Qwen3.5-397B-A17B was released on 2026-02-16, while DeepSeek VL2 was released on 2024-12-13.

Qwen3.5-397B-A17B is 14 months newer than DeepSeek VL2.

Qwen3.5-397B-A17B

Feb 16, 2026

1 months ago

1.2yr newer
DeepSeek VL2

Dec 13, 2024

1.3 years ago

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

Provider Availability

Qwen3.5-397B-A17B is available from Novita. DeepSeek VL2 is available from Replicate.

Qwen3.5-397B-A17B

novita logo
Novita
Input Price:Input: $0.60/1MOutput Price:Output: $3.60/1M

DeepSeek VL2

replicate logo
Replicate
* Prices shown are per million tokens

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)
DeepSeek

Detailed Comparison

AI Model Comparison Table
Feature
Alibaba Cloud / Qwen Team
Qwen3.5-397B-A17B
DeepSeek
DeepSeek VL2

FAQ

Common questions about Qwen3.5-397B-A17B vs DeepSeek VL2

Qwen3.5-397B-A17B (Alibaba Cloud / Qwen Team) and DeepSeek VL2 (DeepSeek) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.
Qwen3.5-397B-A17B scores MMLU-Redux: 94.9%, HMMT 2025: 94.8%, C-Eval: 93.0%, HMMT25: 92.7%, IFEval: 92.6%. DeepSeek VL2 scores DocVQA: 93.3%, ChartQA: 86.0%, TextVQA: 84.2%, AI2D: 81.4%, OCRBench: 81.1%.
Qwen3.5-397B-A17B supports 262K tokens and DeepSeek VL2 supports 129K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include context window (262K vs 129K), licensing (Apache 2.0 vs deepseek). See the full comparison above for benchmark-by-benchmark results.
Qwen3.5-397B-A17B is developed by Alibaba Cloud / Qwen Team and DeepSeek VL2 is developed by DeepSeek.