DeepSeek-V4-Flash-0731 vs Qwen3 30B A3B
DeepSeek-V4-Flash-0731 leads the LLM Stats Score 44.3 to 17.3. DeepSeek-V4-Flash-0731 is 1.7x cheaper per token.
DeepSeek · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
DeepSeek-V4-Flash-0731 leads the overall LLM Stats Score 44.3 to 17.3, ranking #39 overall.
On price, DeepSeek-V4-Flash-0731 is roughly 1.7x 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 44.3 and ranks #39 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- cost matters — it's about 1.7x 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 30B A3B
- you want predictable pricing at $0.10/M input and $0.30/M output
At a glance
The differences that matter most.
Individual benchmarks
9 reported for DeepSeek-V4-Flash-0731 · 8 for Qwen3 30B A3B
DeepSeek-V4-Flash-0731 and Qwen3 30B A3Bdon'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 1.7x cheaper than Qwen3 30B A3B ($0.10/1M tokens).
For output processing, DeepSeek-V4-Flash-0731 ($0.18/1M tokens) is 1.7x cheaper than Qwen3 30B A3B ($0.30/1M tokens).
In conclusion, Qwen3 30B A3B 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 273.5B more parameters than Qwen3 30B A3B, making it 896.7% larger.
Context Window
Maximum input and output token capacity
DeepSeek-V4-Flash-0731 accepts 1,048,576 input tokens compared to Qwen3 30B A3B's 128,000 tokens. DeepSeek-V4-Flash-0731 can generate longer responses up to 1,048,576 tokens, while Qwen3 30B A3B is limited to 128,000 tokens.
License
Usage and distribution terms
DeepSeek-V4-Flash-0731 is licensed under MIT, while Qwen3 30B A3B 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 30B A3B was released on 2025-04-29.
DeepSeek-V4-Flash-0731 is 15 months newer than Qwen3 30B A3B.
Jul 31, 2026
1 months ago
1.3yr newerApr 29, 2025
1.4 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 30B A3B is available from DeepInfra, Novita, Fireworks.
DeepSeek-V4-Flash-0731
Qwen3 30B A3B
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4-Flash-0731 and Qwen3 30B A3B side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4-Flash-0731 vs Qwen3 30B A3B.