Qwen3.5-397B-A17B vs DeepSeek-V3.2 (Thinking) Comparison

Comparing Qwen3.5-397B-A17B and DeepSeek-V3.2 (Thinking) across benchmarks, pricing, and capabilities.

Performance Benchmarks

Comparative analysis across standard metrics

11 benchmarks

Qwen3.5-397B-A17B outperforms in 10 benchmarks (BrowseComp, BrowseComp-zh, GPQA, HMMT 2025, Humanity's Last Exam, MMLU-Pro, SWE-Bench Verified, t2-bench, Terminal-Bench 2.0, Toolathlon), while DeepSeek-V3.2 (Thinking) is better at 1 benchmark (SWE-bench Multilingual).

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

Tue Mar 17 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

DeepSeek-V3.2 (Thinking) costs less

For input processing, Qwen3.5-397B-A17B ($0.60/1M tokens) is 2.1x more expensive than DeepSeek-V3.2 (Thinking) ($0.28/1M tokens).

For output processing, Qwen3.5-397B-A17B ($3.60/1M tokens) is 8.6x more expensive than DeepSeek-V3.2 (Thinking) ($0.42/1M tokens).

In conclusion, Qwen3.5-397B-A17B is more expensive than DeepSeek-V3.2 (Thinking).*

* Using a 3:1 ratio of input to output tokens

Lowest available price from all providers
Tue Mar 17 2026 • llm-stats.com
Alibaba Cloud / Qwen Team
Qwen3.5-397B-A17B
Input tokens$0.60
Output tokens$3.60
Best providerNovita
DeepSeek
DeepSeek-V3.2 (Thinking)
Input tokens$0.28
Output tokens$0.42
Best providerDeepSeek
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Model Size

Parameter count comparison

288.0B diff

DeepSeek-V3.2 (Thinking) has 288.0B more parameters than Qwen3.5-397B-A17B, making it 72.5% larger.

Alibaba Cloud / Qwen Team
Qwen3.5-397B-A17B
397.0Bparameters
DeepSeek
DeepSeek-V3.2 (Thinking)
685.0Bparameters
397.0B
Qwen3.5-397B-A17B
685.0B
DeepSeek-V3.2 (Thinking)

Context Window

Maximum input and output token capacity

Qwen3.5-397B-A17B accepts 262,144 input tokens compared to DeepSeek-V3.2 (Thinking)'s 131,072 tokens. DeepSeek-V3.2 (Thinking) can generate longer responses up to 65,536 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-V3.2 (Thinking)
Input131,072 tokens
Output65,536 tokens
Tue Mar 17 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Qwen3.5-397B-A17B supports multimodal inputs, whereas DeepSeek-V3.2 (Thinking) does not.

Qwen3.5-397B-A17B can handle both text and other forms of data like images, making it suitable for multimodal applications.

Qwen3.5-397B-A17B

Text
Images
Audio
Video

DeepSeek-V3.2 (Thinking)

Text
Images
Audio
Video

License

Usage and distribution terms

Qwen3.5-397B-A17B is licensed under Apache 2.0, while DeepSeek-V3.2 (Thinking) uses MIT.

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-V3.2 (Thinking)

MIT

Open weights

Release Timeline

When each model was launched

Qwen3.5-397B-A17B was released on 2026-02-16, while DeepSeek-V3.2 (Thinking) was released on 2025-12-01.

Qwen3.5-397B-A17B is 3 months newer than DeepSeek-V3.2 (Thinking).

Qwen3.5-397B-A17B

Feb 16, 2026

4 weeks ago

2mo newer
DeepSeek-V3.2 (Thinking)

Dec 1, 2025

3 months 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-V3.2 (Thinking) is available from DeepSeek. The availability of providers can affect quality of the model and reliability.

Qwen3.5-397B-A17B

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

DeepSeek-V3.2 (Thinking)

deepseek logo
DeepSeek
Input Price:Input: $0.28/1MOutput Price:Output: $0.42/1M
* 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)
Supports multimodal inputs
Higher BrowseComp score (69.0% vs 51.4%)
Higher BrowseComp-zh score (70.3% vs 65.0%)
Higher GPQA score (88.4% vs 82.4%)
Higher HMMT 2025 score (94.8% vs 90.2%)
Higher Humanity's Last Exam score (28.7% vs 25.1%)
Higher MMLU-Pro score (87.8% vs 85.0%)
Higher SWE-Bench Verified score (76.4% vs 73.1%)
Higher t2-bench score (86.7% vs 80.2%)
Higher Terminal-Bench 2.0 score (52.5% vs 46.4%)
Higher Toolathlon score (38.3% vs 35.2%)
Less expensive input tokens
Less expensive output tokens
Higher SWE-bench Multilingual score (70.2% vs 69.3%)

Detailed Comparison

AI Model Comparison Table
Feature
Alibaba Cloud / Qwen Team
Qwen3.5-397B-A17B
DeepSeek
DeepSeek-V3.2 (Thinking)