DeepSeek-V4-Flash-0731 vs Qwen2.5 72B Instruct
DeepSeek-V4-Flash-0731 leads the LLM Stats Score 44.7 to 12.1. DeepSeek-V4-Flash-0731 is 4.0x 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 12.1, ranking #36 overall.
On price, DeepSeek-V4-Flash-0731 is roughly 4.0x 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 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 #36 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- cost matters — it's about 4.0x 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 Qwen2.5 72B Instruct
- you want predictable pricing at $0.35/M input and $0.40/M output
At a glance
The differences that matter most.
Individual benchmarks
9 reported for DeepSeek-V4-Flash-0731 · 14 for Qwen2.5 72B Instruct
DeepSeek-V4-Flash-0731 and Qwen2.5 72B Instructdon'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 5.8x cheaper than Qwen2.5 72B Instruct ($0.35/1M tokens).
For output processing, DeepSeek-V4-Flash-0731 ($0.18/1M tokens) is 2.2x cheaper than Qwen2.5 72B Instruct ($0.40/1M tokens).
In conclusion, Qwen2.5 72B Instruct 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 231.3B more parameters than Qwen2.5 72B Instruct, making it 318.2% larger.
Context Window
Maximum input and output token capacity
DeepSeek-V4-Flash-0731 accepts 1,048,576 input tokens compared to Qwen2.5 72B Instruct's 131,072 tokens. DeepSeek-V4-Flash-0731 can generate longer responses up to 1,048,576 tokens, while Qwen2.5 72B Instruct is limited to 8,192 tokens.
License
Usage and distribution terms
DeepSeek-V4-Flash-0731 is licensed under MIT, while Qwen2.5 72B Instruct uses Qwen.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Qwen
Open weights
Release Timeline
When each model was launched
DeepSeek-V4-Flash-0731 was released on 2026-07-31, while Qwen2.5 72B Instruct was released on 2024-09-19.
DeepSeek-V4-Flash-0731 is 23 months newer than Qwen2.5 72B Instruct.
Jul 31, 2026
1 months ago
1.9yr newerSep 19, 2024
2.0 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. Qwen2.5 72B Instruct is available from DeepInfra, Hyperbolic, Fireworks, Together.
DeepSeek-V4-Flash-0731
Qwen2.5 72B Instruct
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4-Flash-0731 and Qwen2.5 72B Instruct side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4-Flash-0731 vs Qwen2.5 72B Instruct.