Model Comparison
DeepSeek-V3.2-Speciale vs Qwen3-235B-A22B-Thinking-2507Which is better in 2026?
DeepSeek-V3.2-Speciale significantly outperforms across most benchmarks. DeepSeek-V3.2-Speciale is 3.1x cheaper per token.
Verdict: DeepSeek-V3.2-Speciale vs Qwen3-235B-A22B-Thinking-2507 — which is better?
DeepSeek-V3.2-Speciale (by DeepSeek) and Qwen3-235B-A22B-Thinking-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-Speciale outperforms in 2 benchmarks (AIME 2025, Humanity's Last Exam), while Qwen3-235B-A22B-Thinking-2507 is better at 0 benchmarks. DeepSeek-V3.2-Speciale significantly outperforms across most benchmarks.
On price, DeepSeek-V3.2-Speciale is roughly 3.1x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Qwen3-235B-A22B-Thinking-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-Speciale if…
- you want the strongest raw capability — it leads on 2 of 2 shared benchmarks
- cost matters — it's about 3.1x cheaper per token
- you want the most recent training data — it shipped Dec 2025
Choose Qwen3-235B-A22B-Thinking-2507 if…
- you process long inputs — it offers a 262,144 token context window
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V3.2-Speciale outperforms in 2 benchmarks (AIME 2025, Humanity's Last Exam), while Qwen3-235B-A22B-Thinking-2507 is better at 0 benchmarks.
DeepSeek-V3.2-Speciale significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V3.2-Speciale ($0.28/1M tokens) is 1.1x cheaper than Qwen3-235B-A22B-Thinking-2507 ($0.30/1M tokens).
For output processing, DeepSeek-V3.2-Speciale ($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-Speciale.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V3.2-Speciale has 450.0B more parameters than Qwen3-235B-A22B-Thinking-2507, making it 191.5% larger.
Context Window
Maximum input and output token capacity
Qwen3-235B-A22B-Thinking-2507 accepts 262,144 input tokens compared to DeepSeek-V3.2-Speciale's 131,072 tokens. Both models can generate responses up to 131,072 tokens.
License
Usage and distribution terms
DeepSeek-V3.2-Speciale 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.
MIT
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
DeepSeek-V3.2-Speciale was released on 2025-12-01, while Qwen3-235B-A22B-Thinking-2507 was released on 2025-07-25.
DeepSeek-V3.2-Speciale is 4 months newer than Qwen3-235B-A22B-Thinking-2507.
Dec 1, 2025
7 months ago
4mo newerJul 25, 2025
1.0 years 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-Speciale is available from DeepSeek. Qwen3-235B-A22B-Thinking-2507 is available from Fireworks, Novita.
DeepSeek-V3.2-Speciale
Qwen3-235B-A22B-Thinking-2507
Outputs Comparison
Key Takeaways
Qwen3-235B-A22B-Thinking-2507
View detailsAlibaba Cloud / Qwen Team
Detailed Comparison
Interactive Arena
Judge for yourself.
Run your own prompts against DeepSeek-V3.2-Speciale and Qwen3-235B-A22B-Thinking-2507 side-by-side, then vote on the output you prefer.
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FAQ
Common questions about DeepSeek-V3.2-Speciale vs Qwen3-235B-A22B-Thinking-2507.