Model Comparison

DeepSeek-V3.2 (Non-thinking) vs Qwen3-235B-A22B-Thinking-2507

Comparing DeepSeek-V3.2 (Non-thinking) and Qwen3-235B-A22B-Thinking-2507 across benchmarks, pricing, and capabilities.

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Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

DeepSeek-V3.2 (Non-thinking) and Qwen3-235B-A22B-Thinking-2507 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

DeepSeek-V3.2 (Non-thinking) costs less

For input processing, DeepSeek-V3.2 (Non-thinking) ($0.28/1M tokens) is 1.1x cheaper than Qwen3-235B-A22B-Thinking-2507 ($0.30/1M tokens).

For output processing, DeepSeek-V3.2 (Non-thinking) ($0.42/1M tokens) is 7.1x cheaper than Qwen3-235B-A22B-Thinking-2507 ($3.00/1M tokens).

In conclusion, Qwen3-235B-A22B-Thinking-2507 is more expensive than DeepSeek-V3.2 (Non-thinking).*

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

Lowest available price from all providers

Wed May 13 2026 • llm-stats.com

DeepSeek-V3.2 (Non-thinking)

Input tokens$0.28

Output tokens$0.42

Best providerDeepSeek

Qwen3-235B-A22B-Thinking-2507

Input tokens$0.30

Output tokens$3.00

Best providerFireworks

Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

450.0B diff

DeepSeek-V3.2 (Non-thinking) has 450.0B more parameters than Qwen3-235B-A22B-Thinking-2507, making it 191.5% larger.

DeepSeek-V3.2 (Non-thinking)

685.0Bparameters

Qwen3-235B-A22B-Thinking-2507

235.0Bparameters

685.0B

DeepSeek-V3.2 (Non-thinking)

235.0B

Qwen3-235B-A22B-Thinking-2507

Context Window

Maximum input and output token capacity

Qwen3-235B-A22B-Thinking-2507 accepts 262,144 input tokens compared to DeepSeek-V3.2 (Non-thinking)'s 131,072 tokens. Qwen3-235B-A22B-Thinking-2507 can generate longer responses up to 131,072 tokens, while DeepSeek-V3.2 (Non-thinking) is limited to 8,192 tokens.

DeepSeek-V3.2 (Non-thinking)

Input131,072 tokens

Output8,192 tokens

Qwen3-235B-A22B-Thinking-2507

Input262,144 tokens

Output131,072 tokens

Wed May 13 2026 • llm-stats.com

License

Usage and distribution terms

DeepSeek-V3.2 (Non-thinking) is licensed under MIT, while Qwen3-235B-A22B-Thinking-2507 uses Apache 2.0.

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

DeepSeek-V3.2 (Non-thinking)

MIT

Open weights

Qwen3-235B-A22B-Thinking-2507

Apache 2.0

Open weights

Release Timeline

When each model was launched

DeepSeek-V3.2 (Non-thinking) was released on 2025-12-01, while Qwen3-235B-A22B-Thinking-2507 was released on 2025-07-25.

DeepSeek-V3.2 (Non-thinking) is 4 months newer than Qwen3-235B-A22B-Thinking-2507.

DeepSeek-V3.2 (Non-thinking)

Dec 1, 2025

5 months ago

4mo newer

Qwen3-235B-A22B-Thinking-2507

Jul 25, 2025

9 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

DeepSeek-V3.2 (Non-thinking) is available from DeepSeek. Qwen3-235B-A22B-Thinking-2507 is available from Fireworks, Novita.

DeepSeek-V3.2 (Non-thinking)

DeepSeek

Input Price:Input: $0.28/1MOutput Price:Output: $0.42/1M

Qwen3-235B-A22B-Thinking-2507

Fireworks

Input Price:Input: $0.30/1MOutput Price:Output: $3.00/1M

Novita

Input Price:Input: $0.30/1MOutput Price:Output: $3.00/1M

* Prices shown are per million tokens

Outputs Comparison

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Key Takeaways

DeepSeek-V3.2 (Non-thinking)

View details

DeepSeek

Less expensive input tokens

Less expensive output tokens

Qwen3-235B-A22B-Thinking-2507

View details

Alibaba Cloud / Qwen Team

Larger context window (262,144 tokens)

Detailed Comparison

AI Model Comparison Table
Feature	DeepSeek-V3.2 (Non-thinking)	Qwen3-235B-A22B-Thinking-2507

FAQ

Common questions about DeepSeek-V3.2 (Non-thinking) vs Qwen3-235B-A22B-Thinking-2507.

Which is better, DeepSeek-V3.2 (Non-thinking) or Qwen3-235B-A22B-Thinking-2507?

DeepSeek-V3.2 (Non-thinking) (DeepSeek) and Qwen3-235B-A22B-Thinking-2507 (Alibaba Cloud / Qwen Team) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.

How does DeepSeek-V3.2 (Non-thinking) compare to Qwen3-235B-A22B-Thinking-2507 in benchmarks?

Qwen3-235B-A22B-Thinking-2507 scores MMLU-Redux: 93.8%, AIME 2025: 92.3%, WritingBench: 88.3%, IFEval: 87.8%, Creative Writing v3: 86.1%.

Is DeepSeek-V3.2 (Non-thinking) cheaper than Qwen3-235B-A22B-Thinking-2507?

DeepSeek-V3.2 (Non-thinking) is 1.1x cheaper for input tokens. DeepSeek-V3.2 (Non-thinking) costs $0.28/M input and $0.42/M output via deepseek. Qwen3-235B-A22B-Thinking-2507 costs $0.30/M input and $3.00/M output via fireworks.

What are the context window sizes for DeepSeek-V3.2 (Non-thinking) and Qwen3-235B-A22B-Thinking-2507?

DeepSeek-V3.2 (Non-thinking) supports 131K tokens and Qwen3-235B-A22B-Thinking-2507 supports 262K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between DeepSeek-V3.2 (Non-thinking) and Qwen3-235B-A22B-Thinking-2507?

Key differences include context window (131K vs 262K), input pricing ($0.28 vs $0.30/M), licensing (MIT vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V3.2 (Non-thinking) and Qwen3-235B-A22B-Thinking-2507?

DeepSeek-V3.2 (Non-thinking) is developed by DeepSeek and Qwen3-235B-A22B-Thinking-2507 is developed by Alibaba Cloud / Qwen Team.