Model Comparison
DeepSeek-V3.1 vs Qwen3-235B-A22B-Instruct-2507Which is better in 2026?
Both models are evenly matched across the benchmarks. Qwen3-235B-A22B-Instruct-2507 is 1.4x cheaper per token.
Verdict: DeepSeek-V3.1 vs Qwen3-235B-A22B-Instruct-2507 — which is better?
DeepSeek-V3.1 (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-V3.1 outperforms in 3 benchmarks (Aider-Polyglot, MMLU-Pro, SimpleQA), while Qwen3-235B-A22B-Instruct-2507 is better at 3 benchmarks (AIME 2025, GPQA, MMLU-Redux). Both models are evenly matched across the benchmarks.
On price, Qwen3-235B-A22B-Instruct-2507 is roughly 1.4x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
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-V3.1 if…
- you want predictable pricing at $0.27/M input and $1.00/M output
Choose Qwen3-235B-A22B-Instruct-2507 if…
- cost matters — it's about 1.4x cheaper per token
- 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-V3.1 outperforms in 3 benchmarks (Aider-Polyglot, MMLU-Pro, SimpleQA), while Qwen3-235B-A22B-Instruct-2507 is better at 3 benchmarks (AIME 2025, GPQA, MMLU-Redux).
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 1.8x more expensive than Qwen3-235B-A22B-Instruct-2507 ($0.15/1M tokens).
For output processing, DeepSeek-V3.1 ($1.00/1M tokens) is 1.3x more expensive than Qwen3-235B-A22B-Instruct-2507 ($0.80/1M tokens).
In conclusion, DeepSeek-V3.1 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-V3.1 has 436.0B more parameters than Qwen3-235B-A22B-Instruct-2507, making it 185.5% larger.
Context Window
Maximum input and output token capacity
Qwen3-235B-A22B-Instruct-2507 accepts 262,144 input tokens compared to DeepSeek-V3.1's 163,840 tokens. DeepSeek-V3.1 can generate longer responses up to 163,840 tokens, while Qwen3-235B-A22B-Instruct-2507 is limited to 131,072 tokens.
License
Usage and distribution terms
DeepSeek-V3.1 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-V3.1 was released on 2025-01-10, while Qwen3-235B-A22B-Instruct-2507 was released on 2025-07-22.
Qwen3-235B-A22B-Instruct-2507 is 6 months newer than DeepSeek-V3.1.
Jan 10, 2025
1.5 years ago
Jul 22, 2025
11 months 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-V3.1 is available from DeepInfra, Novita. Qwen3-235B-A22B-Instruct-2507 is available from Fireworks, Novita.
DeepSeek-V3.1
Qwen3-235B-A22B-Instruct-2507
Outputs Comparison
Key Takeaways
DeepSeek-V3.1
View detailsDeepSeek
Qwen3-235B-A22B-Instruct-2507
View detailsAlibaba Cloud / Qwen Team
Detailed Comparison
Interactive Arena
Judge for yourself.
Run your own prompts against DeepSeek-V3.1 and Qwen3-235B-A22B-Instruct-2507 side-by-side, then vote on the output you prefer.
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FAQ
Common questions about DeepSeek-V3.1 vs Qwen3-235B-A22B-Instruct-2507.