DeepSeek-V4-Pro-0813 vs Qwen3-235B-A22B-Thinking-2507
DeepSeek-V4-Pro-0813 significantly outperforms across most benchmarks. DeepSeek-V4-Pro-0813 is 1.8x cheaper per token.
DeepSeek · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
DeepSeek-V4-Pro-0813 outperforms in 1 benchmarks (Humanity's Last Exam), while Qwen3-235B-A22B-Thinking-2507 is better at 0 benchmarks. DeepSeek-V4-Pro-0813 significantly outperforms across most benchmarks.
On price, DeepSeek-V4-Pro-0813 is roughly 1.8x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
DeepSeek-V4-Pro-0813 also accepts a larger context window (1,048,576 input tokens), making it the stronger choice for long documents and large codebases.
Based on current benchmark, pricing, and model metadata for 2026.
Choose DeepSeek-V4-Pro-0813
- you want the strongest raw capability — it leads on 1 of 1 shared benchmarks
- cost matters — it's about 1.8x cheaper per token
- you process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Aug 2026
Choose Qwen3-235B-A22B-Thinking-2507
- you want predictable pricing at $0.30/M input and $3.00/M output
At a glance
The differences that matter most.
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V4-Pro-0813 outperforms in 1 benchmarks (Humanity's Last Exam), while Qwen3-235B-A22B-Thinking-2507 is better at 0 benchmarks.
DeepSeek-V4-Pro-0813 significantly outperforms across most benchmarks.
Arena Performance
Playground indexes and blind preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V4-Pro-0813 ($0.43/1M tokens) is 1.4x more expensive than Qwen3-235B-A22B-Thinking-2507 ($0.30/1M tokens).
For output processing, DeepSeek-V4-Pro-0813 ($0.87/1M tokens) is 3.4x cheaper than Qwen3-235B-A22B-Thinking-2507 ($3.00/1M tokens).
In conclusion, Qwen3-235B-A22B-Thinking-2507 is more expensive than DeepSeek-V4-Pro-0813.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V4-Pro-0813 has 1365.0B more parameters than Qwen3-235B-A22B-Thinking-2507, making it 580.9% larger.
Context Window
Maximum input and output token capacity
DeepSeek-V4-Pro-0813 accepts 1,048,576 input tokens compared to Qwen3-235B-A22B-Thinking-2507's 262,144 tokens. DeepSeek-V4-Pro-0813 can generate longer responses up to 393,216 tokens, while Qwen3-235B-A22B-Thinking-2507 is limited to 131,072 tokens.
License
Usage and distribution terms
DeepSeek-V4-Pro-0813 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-V4-Pro-0813 was released on 2026-08-13, while Qwen3-235B-A22B-Thinking-2507 was released on 2025-07-25.
DeepSeek-V4-Pro-0813 is 13 months newer than Qwen3-235B-A22B-Thinking-2507.
Aug 13, 2026
1 weeks ago
1.1yr newerJul 25, 2025
1.1 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-Pro-0813 is available from DeepSeek, DeepInfra, Novita, Together. Qwen3-235B-A22B-Thinking-2507 is available from Fireworks, Novita.
DeepSeek-V4-Pro-0813
Qwen3-235B-A22B-Thinking-2507
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4-Pro-0813 and Qwen3-235B-A22B-Thinking-2507 side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4-Pro-0813 vs Qwen3-235B-A22B-Thinking-2507.