DeepSeek-V2.5 vs Qwen3.8 Max
Qwen3.8 Max leads the LLM Stats Score 51.9 to 8.1. DeepSeek-V2.5 is 14.1x cheaper per token.
DeepSeek · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
Qwen3.8 Max leads the overall LLM Stats Score 51.9 to 8.1, ranking #11 overall.
On price, DeepSeek-V2.5 is roughly 14.1x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Qwen3.8 Max also accepts a larger context window (256,000 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-V2.5
- cost matters — it's about 14.1x cheaper per token
Choose Qwen3.8 Max
- overall performance matters — it scores 51.9 and ranks #11 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- you process long inputs — it offers a 256,000 token context window
- you want the most recent training data — it shipped Aug 2026
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
15 reported for DeepSeek-V2.5 · 42 for Qwen3.8 Max
DeepSeek-V2.5 and Qwen3.8 Maxdon'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-V2.5 ($0.14/1M tokens) is 11.8x cheaper than Qwen3.8 Max ($1.65/1M tokens).
For output processing, DeepSeek-V2.5 ($0.28/1M tokens) is 17.7x cheaper than Qwen3.8 Max ($4.95/1M tokens).
In conclusion, Qwen3.8 Max is more expensive than DeepSeek-V2.5.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Qwen3.8 Max has 2164.0B more parameters than DeepSeek-V2.5, making it 916.9% larger.
Context Window
Maximum input and output token capacity
Qwen3.8 Max accepts 256,000 input tokens compared to DeepSeek-V2.5's 8,192 tokens. Qwen3.8 Max can generate longer responses up to 256,000 tokens, while DeepSeek-V2.5 is limited to 8,192 tokens.
Input capabilities
Documented input modalities across available providers
Qwen3.8 Max supports multimodal inputs, whereas DeepSeek-V2.5 does not.
Qwen3.8 Max can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V2.5
Qwen3.8 Max
License
Usage and distribution terms
DeepSeek-V2.5 is licensed under deepseek, while Qwen3.8 Max uses Qwen3.8-Max License.
License differences may affect how you can use these models in commercial or open-source projects.
deepseek
Open weights
Qwen3.8-Max License
Open weights
Release Timeline
When each model was launched
DeepSeek-V2.5 was released on 2024-05-08, while Qwen3.8 Max was released on 2026-08-02.
Qwen3.8 Max is 27 months newer than DeepSeek-V2.5.
May 8, 2024
2.3 years ago
Aug 2, 2026
1 months ago
2.2yr newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
DeepSeek-V2.5 is available from DeepSeek, DeepInfra, Hyperbolic. Qwen3.8 Max is available from DeepInfra, Fireworks, Novita, Together.
DeepSeek-V2.5
Qwen3.8 Max
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V2.5 and Qwen3.8 Max side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V2.5 vs Qwen3.8 Max.