Model Comparison
DeepSeek-V3.1 vs Qwen3 235B A22BWhich is better in 2026?
Both models are evenly matched across the benchmarks. Qwen3 235B A22B is 4.5x cheaper per token.
Verdict: DeepSeek-V3.1 vs Qwen3 235B A22B — which is better?
DeepSeek-V3.1 (by DeepSeek) and Qwen3 235B A22B (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-V3.1 outperforms in 3 benchmarks (GPQA, MMLU-Pro, MMLU-Redux), while Qwen3 235B A22B is better at 3 benchmarks (AIME 2024, AIME 2025, LiveCodeBench). Both models are evenly matched across the benchmarks.
On price, Qwen3 235B A22B is roughly 4.5x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
DeepSeek-V3.1 also accepts a larger context window (163,840 input tokens), making it the stronger choice for long documents and large codebases.
Choose DeepSeek-V3.1 if…
- you process long inputs — it offers a 163,840 token context window
Choose Qwen3 235B A22B if…
- cost matters — it's about 4.5x cheaper per token
- you want the most recent training data — it shipped Apr 2025
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V3.1 outperforms in 3 benchmarks (GPQA, MMLU-Pro, MMLU-Redux), while Qwen3 235B A22B is better at 3 benchmarks (AIME 2024, AIME 2025, LiveCodeBench).
Both models are evenly matched across the benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V3.1 ($0.27/1M tokens) is 2.7x more expensive than Qwen3 235B A22B ($0.10/1M tokens).
For output processing, DeepSeek-V3.1 ($1.00/1M tokens) is 10.0x more expensive than Qwen3 235B A22B ($0.10/1M tokens).
In conclusion, DeepSeek-V3.1 is more expensive than Qwen3 235B A22B.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V3.1 has 436.0B more parameters than Qwen3 235B A22B, making it 185.5% larger.
Context Window
Maximum input and output token capacity
DeepSeek-V3.1 accepts 163,840 input tokens compared to Qwen3 235B A22B's 128,000 tokens. DeepSeek-V3.1 can generate longer responses up to 163,840 tokens, while Qwen3 235B A22B is limited to 128,000 tokens.
License
Usage and distribution terms
DeepSeek-V3.1 is licensed under MIT, while Qwen3 235B A22B 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-V3.1 was released on 2025-01-10, while Qwen3 235B A22B was released on 2025-04-29.
Qwen3 235B A22B is 4 months newer than DeepSeek-V3.1.
Jan 10, 2025
1.4 years ago
Apr 29, 2025
1.1 years ago
3mo 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-V3.1 is available from DeepInfra, Novita. Qwen3 235B A22B is available from Fireworks, DeepInfra, Novita, Together.
DeepSeek-V3.1
Qwen3 235B A22B
Outputs Comparison
Key Takeaways
DeepSeek-V3.1
View detailsDeepSeek
Qwen3 235B A22B
View detailsAlibaba Cloud / Qwen Team
Detailed Comparison
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FAQ
Common questions about DeepSeek-V3.1 vs Qwen3 235B A22B.