Model Comparison
DeepSeek-R1 vs Qwen3-235B-A22B-Thinking-2507Which is better in 2026?
Comparing DeepSeek-R1 and Qwen3-235B-A22B-Thinking-2507 across benchmarks, pricing, and capabilities.
Verdict: DeepSeek-R1 vs Qwen3-235B-A22B-Thinking-2507 — which is better?
DeepSeek-R1 (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.
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-R1 if…
- you want predictable pricing at $0.55/M input and $2.19/M output
Choose Qwen3-235B-A22B-Thinking-2507 if…
- you process long inputs — it offers a 262,144 token context window
- you want the most recent training data — it shipped Jul 2025
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-R1 and Qwen3-235B-A22B-Thinking-2507don'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
For input processing, DeepSeek-R1 ($0.55/1M tokens) is 1.8x more expensive than Qwen3-235B-A22B-Thinking-2507 ($0.30/1M tokens).
For output processing, DeepSeek-R1 ($2.19/1M tokens) is 1.4x cheaper than Qwen3-235B-A22B-Thinking-2507 ($3.00/1M tokens).
In conclusion, Qwen3-235B-A22B-Thinking-2507 is more expensive than DeepSeek-R1.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-R1 has 436.0B more parameters than Qwen3-235B-A22B-Thinking-2507, making it 185.5% larger.
Context Window
Maximum input and output token capacity
Qwen3-235B-A22B-Thinking-2507 accepts 262,144 input tokens compared to DeepSeek-R1's 131,072 tokens. Both models can generate responses up to 131,072 tokens.
License
Usage and distribution terms
DeepSeek-R1 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-R1 was released on 2025-01-20, while Qwen3-235B-A22B-Thinking-2507 was released on 2025-07-25.
Qwen3-235B-A22B-Thinking-2507 is 6 months newer than DeepSeek-R1.
Jan 20, 2025
1.5 years ago
Jul 25, 2025
1.0 years ago
6mo newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
DeepSeek-R1 is available from DeepSeek, DeepInfra, Together, Fireworks. Qwen3-235B-A22B-Thinking-2507 is available from Fireworks, Novita.
DeepSeek-R1
Qwen3-235B-A22B-Thinking-2507
Outputs Comparison
Key Takeaways
DeepSeek-R1
View detailsDeepSeek
Qwen3-235B-A22B-Thinking-2507
View detailsAlibaba Cloud / Qwen Team
Detailed Comparison
Interactive Arena
Judge for yourself.
Run your own prompts against DeepSeek-R1 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-R1 vs Qwen3-235B-A22B-Thinking-2507.