Model Comparison
DeepSeek-V3.2 (Thinking) vs Qwen3-235B-A22B-Instruct-2507Which is better in 2026?
DeepSeek-V3.2 (Thinking) significantly outperforms across most benchmarks. Qwen3-235B-A22B-Instruct-2507 is 1.0x cheaper per token.
Verdict: DeepSeek-V3.2 (Thinking) vs Qwen3-235B-A22B-Instruct-2507 — which is better?
DeepSeek-V3.2 (Thinking) (by DeepSeek) and Qwen3-235B-A22B-Instruct-2507 (by Alibaba Cloud / Qwen Team) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.
DeepSeek-V3.2 (Thinking) outperforms in 3 benchmarks (AIME 2025, GPQA, MMLU-Pro), while Qwen3-235B-A22B-Instruct-2507 is better at 0 benchmarks. DeepSeek-V3.2 (Thinking) significantly outperforms across most benchmarks.
Qwen3-235B-A22B-Instruct-2507 also accepts a larger context window (262,144 input tokens), making it the stronger choice for long documents and large codebases.
Choose DeepSeek-V3.2 (Thinking) if…
- you want the strongest raw capability — it leads on 3 of 3 shared benchmarks
- you want the most recent training data — it shipped Dec 2025
Choose Qwen3-235B-A22B-Instruct-2507 if…
- you process long inputs — it offers a 262,144 token context window
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V3.2 (Thinking) outperforms in 3 benchmarks (AIME 2025, GPQA, MMLU-Pro), while Qwen3-235B-A22B-Instruct-2507 is better at 0 benchmarks.
DeepSeek-V3.2 (Thinking) significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V3.2 (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 (Thinking) ($0.42/1M tokens) is 1.9x cheaper than Qwen3-235B-A22B-Instruct-2507 ($0.80/1M tokens).
In conclusion, DeepSeek-V3.2 (Thinking) is more expensive than Qwen3-235B-A22B-Instruct-2507.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V3.2 (Thinking) has 450.0B more parameters than Qwen3-235B-A22B-Instruct-2507, making it 191.5% larger.
Context Window
Maximum input and output token capacity
Qwen3-235B-A22B-Instruct-2507 accepts 262,144 input tokens compared to DeepSeek-V3.2 (Thinking)'s 131,072 tokens. Qwen3-235B-A22B-Instruct-2507 can generate longer responses up to 131,072 tokens, while DeepSeek-V3.2 (Thinking) is limited to 65,536 tokens.
License
Usage and distribution terms
DeepSeek-V3.2 (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.
MIT
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
DeepSeek-V3.2 (Thinking) was released on 2025-12-01, while Qwen3-235B-A22B-Instruct-2507 was released on 2025-07-22.
DeepSeek-V3.2 (Thinking) is 4 months newer than Qwen3-235B-A22B-Instruct-2507.
Dec 1, 2025
6 months ago
4mo newerJul 22, 2025
10 months ago
Knowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
DeepSeek-V3.2 (Thinking) is available from DeepSeek. Qwen3-235B-A22B-Instruct-2507 is available from Fireworks, Novita.
DeepSeek-V3.2 (Thinking)
Qwen3-235B-A22B-Instruct-2507
Outputs Comparison
Key Takeaways
Qwen3-235B-A22B-Instruct-2507
View detailsAlibaba Cloud / Qwen Team
Detailed Comparison
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FAQ
Common questions about DeepSeek-V3.2 (Thinking) vs Qwen3-235B-A22B-Instruct-2507.