DeepSeek-V3.1 vs Qwen2.5 72B Instruct
DeepSeek-V3.1 leads the LLM Stats Score 22.1 to 12.1. Qwen2.5 72B Instruct is 1.2x cheaper per token.
DeepSeek · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
DeepSeek-V3.1 leads the overall LLM Stats Score 22.1 to 12.1, ranking #182 overall.
In the 4 individual benchmarks reported for both models, DeepSeek-V3.1 wins 4; this is a narrower head-to-head signal than the composite indexes.
On price, Qwen2.5 72B Instruct is roughly 1.2x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
DeepSeek-V3.1 also accepts a larger context window (163,840 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-V3.1
- overall performance matters — it scores 22.1 and ranks #182 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you value its reported benchmark strengths — it wins 4 of 4 exact shared results
- you process long inputs — it offers a 163,840 token context window
- you want the most recent training data — it shipped Jan 2025
Choose Qwen2.5 72B Instruct
- cost matters — it's about 1.2x cheaper per token
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
16 reported for DeepSeek-V3.1 · 14 for Qwen2.5 72B Instruct
DeepSeek-V3.1 outperforms in 4 benchmarks (GPQA, LiveCodeBench, MMLU-Pro, MMLU-Redux), while Qwen2.5 72B Instruct is better at 0 benchmarks.
DeepSeek-V3.1 significantly outperforms across most benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V3.1 ($0.25/1M tokens) is 1.4x cheaper than Qwen2.5 72B Instruct ($0.35/1M tokens).
For output processing, DeepSeek-V3.1 ($0.95/1M tokens) is 2.4x more expensive than Qwen2.5 72B Instruct ($0.40/1M tokens).
In conclusion, DeepSeek-V3.1 is more expensive than Qwen2.5 72B Instruct.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V3.1 has 598.3B more parameters than Qwen2.5 72B Instruct, making it 823.0% larger.
Context Window
Maximum input and output token capacity
DeepSeek-V3.1 accepts 163,840 input tokens compared to Qwen2.5 72B Instruct's 131,072 tokens. DeepSeek-V3.1 can generate longer responses up to 163,840 tokens, while Qwen2.5 72B Instruct is limited to 8,192 tokens.
License
Usage and distribution terms
DeepSeek-V3.1 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-V3.1 was released on 2025-01-10, while Qwen2.5 72B Instruct was released on 2024-09-19.
DeepSeek-V3.1 is 4 months newer than Qwen2.5 72B Instruct.
Jan 10, 2025
1.7 years ago
3mo 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-V3.1 is available from DeepInfra, Novita. Qwen2.5 72B Instruct is available from DeepInfra, Hyperbolic, Fireworks, Together.
DeepSeek-V3.1
Qwen2.5 72B Instruct
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V3.1 and Qwen2.5 72B Instruct side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V3.1 vs Qwen2.5 72B Instruct.