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

Comparing DeepSeek-V3.2 (Non-thinking) and Qwen3-235B-A22B-Instruct-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-Instruct-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

Qwen3-235B-A22B-Instruct-2507 costs less

For input processing, DeepSeek-V3.2 (Non-thinking) ($0.28/1M tokens) is 1.9x more expensive than Qwen3-235B-A22B-Instruct-2507 ($0.15/1M tokens).

For output processing, DeepSeek-V3.2 (Non-thinking) ($0.42/1M tokens) is 1.9x cheaper than Qwen3-235B-A22B-Instruct-2507 ($0.80/1M tokens).

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

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

Lowest available price from all providers

Sat Mar 14 2026 • llm-stats.com

DeepSeek-V3.2 (Non-thinking)

Input tokens$0.28

Output tokens$0.42

Best providerDeepSeek

Qwen3-235B-A22B-Instruct-2507

Input tokens$0.15

Output tokens$0.80

Best providerFireworks

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Model Size

Parameter count comparison

450.0B diff

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

DeepSeek-V3.2 (Non-thinking)

685.0Bparameters

Qwen3-235B-A22B-Instruct-2507

235.0Bparameters

685.0B

DeepSeek-V3.2 (Non-thinking)

235.0B

Qwen3-235B-A22B-Instruct-2507

Context Window

Maximum input and output token capacity

Qwen3-235B-A22B-Instruct-2507 accepts 262,144 input tokens compared to DeepSeek-V3.2 (Non-thinking)'s 131,072 tokens. Qwen3-235B-A22B-Instruct-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-Instruct-2507

Input262,144 tokens

Output131,072 tokens

Sat Mar 14 2026 • llm-stats.com

License

Usage and distribution terms

DeepSeek-V3.2 (Non-thinking) is licensed under MIT, while Qwen3-235B-A22B-Instruct-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-Instruct-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-Instruct-2507 was released on 2025-07-22.

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

DeepSeek-V3.2 (Non-thinking)

Dec 1, 2025

3 months ago

4mo newer

Qwen3-235B-A22B-Instruct-2507

Jul 22, 2025

7 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-Instruct-2507 is available from Fireworks, Novita. The availability of providers can affect quality of the model and reliability.