DeepSeek-V4-Flash-0731 vs Qwen3.5-397B-A17B
DeepSeek-V4-Flash-0731 significantly outperforms across most benchmarks. DeepSeek-V4-Flash-0731 is 12.0x cheaper per token.
DeepSeek · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
DeepSeek-V4-Flash-0731 outperforms in 1 benchmarks (Toolathlon), while Qwen3.5-397B-A17B is better at 0 benchmarks. DeepSeek-V4-Flash-0731 significantly outperforms across most benchmarks.
On price, DeepSeek-V4-Flash-0731 is roughly 12.0x 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 benchmark, pricing, and model metadata for 2026.
Choose DeepSeek-V4-Flash-0731
- you want the strongest raw capability — it leads on 1 of 1 shared benchmarks
- cost matters — it's about 12.0x 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 Qwen3.5-397B-A17B
- you want predictable pricing at $0.60/M input and $3.60/M output
At a glance
The differences that matter most.
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V4-Flash-0731 outperforms in 1 benchmarks (Toolathlon), while Qwen3.5-397B-A17B is better at 0 benchmarks.
DeepSeek-V4-Flash-0731 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-Flash-0731 ($0.09/1M tokens) is 6.7x cheaper than Qwen3.5-397B-A17B ($0.60/1M tokens).
For output processing, DeepSeek-V4-Flash-0731 ($0.18/1M tokens) is 20.0x cheaper than Qwen3.5-397B-A17B ($3.60/1M tokens).
In conclusion, Qwen3.5-397B-A17B is more expensive than DeepSeek-V4-Flash-0731.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Qwen3.5-397B-A17B has 93.0B more parameters than DeepSeek-V4-Flash-0731, making it 30.6% larger.
Context Window
Maximum input and output token capacity
DeepSeek-V4-Flash-0731 accepts 1,048,576 input tokens compared to Qwen3.5-397B-A17B's 262,144 tokens. DeepSeek-V4-Flash-0731 can generate longer responses up to 384,000 tokens, while Qwen3.5-397B-A17B is limited to 64,000 tokens.
Input Capabilities
Supported data types and modalities
Qwen3.5-397B-A17B supports multimodal inputs, whereas DeepSeek-V4-Flash-0731 does not.
Qwen3.5-397B-A17B can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V4-Flash-0731
Qwen3.5-397B-A17B
License
Usage and distribution terms
DeepSeek-V4-Flash-0731 is licensed under MIT, while Qwen3.5-397B-A17B 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 Qwen3.5-397B-A17B was released on 2026-02-16.
DeepSeek-V4-Flash-0731 is 6 months newer than Qwen3.5-397B-A17B.
Jul 31, 2026
3 weeks ago
5mo newerFeb 16, 2026
6 months 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. Qwen3.5-397B-A17B is available from Novita.
DeepSeek-V4-Flash-0731
Qwen3.5-397B-A17B
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4-Flash-0731 and Qwen3.5-397B-A17B side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4-Flash-0731 vs Qwen3.5-397B-A17B.