Model Comparison
DeepSeek-V4-Flash-0731 vs Qwen3 235B A22BWhich is better in 2026?
Comparing DeepSeek-V4-Flash-0731 and Qwen3 235B A22B across benchmarks, pricing, and capabilities.
Verdict: DeepSeek-V4-Flash-0731 vs Qwen3 235B A22B — which is better?
DeepSeek-V4-Flash-0731 (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.
On price, Qwen3 235B A22B is roughly 1.1x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
DeepSeek-V4-Flash-0731 also accepts a larger context window (1,048,576 input tokens), making it the stronger choice for long documents and large codebases.
Choose DeepSeek-V4-Flash-0731 if…
- you process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Jul 2026
Choose Qwen3 235B A22B if…
- cost matters — it's about 1.1x cheaper per token
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V4-Flash-0731 and Qwen3 235B A22Bdon'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-V4-Flash-0731 ($0.09/1M tokens) is 1.1x cheaper than Qwen3 235B A22B ($0.10/1M tokens).
For output processing, DeepSeek-V4-Flash-0731 ($0.18/1M tokens) is 1.8x more expensive than Qwen3 235B A22B ($0.10/1M tokens).
In conclusion, DeepSeek-V4-Flash-0731 is more expensive than Qwen3 235B A22B.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V4-Flash-0731 has 69.0B more parameters than Qwen3 235B A22B, making it 29.4% larger.
Context Window
Maximum input and output token capacity
DeepSeek-V4-Flash-0731 accepts 1,048,576 input tokens compared to Qwen3 235B A22B's 128,000 tokens. Qwen3 235B A22B can generate longer responses up to 128,000 tokens, while DeepSeek-V4-Flash-0731 is limited to 65,536 tokens.
License
Usage and distribution terms
DeepSeek-V4-Flash-0731 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-V4-Flash-0731 was released on 2026-07-31, while Qwen3 235B A22B was released on 2025-04-29.
DeepSeek-V4-Flash-0731 is 15 months newer than Qwen3 235B A22B.
Jul 31, 2026
3 days ago
1.3yr newerApr 29, 2025
1.3 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-V4-Flash-0731 is available from DeepInfra, Fireworks, Novita. Qwen3 235B A22B is available from Fireworks, DeepInfra, Novita, Together.
DeepSeek-V4-Flash-0731
Qwen3 235B A22B
Outputs Comparison
Key Takeaways
Qwen3 235B A22B
View detailsAlibaba Cloud / Qwen Team
Detailed Comparison
Interactive Arena
Judge for yourself.
Run your own prompts against DeepSeek-V4-Flash-0731 and Qwen3 235B A22B side-by-side, then vote on the output you prefer.
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FAQ
Common questions about DeepSeek-V4-Flash-0731 vs Qwen3 235B A22B.