DeepSeek-V4-Flash-0731 vs Qwen3-235B-A22B-Instruct-2507
DeepSeek-V4-Flash-0731 leads the LLM Stats Score 44.7 to 24.2. DeepSeek-V4-Flash-0731 is 2.3x cheaper per token.
DeepSeek · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
DeepSeek-V4-Flash-0731 leads the overall LLM Stats Score 44.7 to 24.2, ranking #35 overall.
On price, DeepSeek-V4-Flash-0731 is roughly 2.3x 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.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose DeepSeek-V4-Flash-0731
- overall performance matters — it scores 44.7 and ranks #35 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- cost matters — it's about 2.3x 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 Jul 2026
Choose Qwen3-235B-A22B-Instruct-2507
- you want predictable pricing at $0.09/M input and $0.55/M output
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
9 reported for DeepSeek-V4-Flash-0731 · 25 for Qwen3-235B-A22B-Instruct-2507
DeepSeek-V4-Flash-0731 and Qwen3-235B-A22B-Instruct-2507don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V4-Flash-0731 ($0.06/1M tokens) is 1.5x cheaper than Qwen3-235B-A22B-Instruct-2507 ($0.09/1M tokens).
For output processing, DeepSeek-V4-Flash-0731 ($0.18/1M tokens) is 3.1x cheaper than Qwen3-235B-A22B-Instruct-2507 ($0.55/1M tokens).
In conclusion, Qwen3-235B-A22B-Instruct-2507 is more expensive than DeepSeek-V4-Flash-0731.*
* 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-Instruct-2507, 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-Instruct-2507's 262,144 tokens. DeepSeek-V4-Flash-0731 can generate longer responses up to 1,048,576 tokens, while Qwen3-235B-A22B-Instruct-2507 is limited to 262,144 tokens.
License
Usage and distribution terms
DeepSeek-V4-Flash-0731 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-V4-Flash-0731 was released on 2026-07-31, while Qwen3-235B-A22B-Instruct-2507 was released on 2025-07-22.
DeepSeek-V4-Flash-0731 is 12 months newer than Qwen3-235B-A22B-Instruct-2507.
Jul 31, 2026
1 months ago
1.0yr newerJul 22, 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-Flash-0731 is available from DeepInfra, Novita, Fireworks. Qwen3-235B-A22B-Instruct-2507 is available from DeepInfra, Fireworks, Novita.
DeepSeek-V4-Flash-0731
Qwen3-235B-A22B-Instruct-2507
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4-Flash-0731 and Qwen3-235B-A22B-Instruct-2507 side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4-Flash-0731 vs Qwen3-235B-A22B-Instruct-2507.