DeepSeek-V2.5 vs Qwen3 VL 235B A22B Thinking
Qwen3 VL 235B A22B Thinking significantly outperforms across most benchmarks. DeepSeek-V2.5 is 6.9x cheaper per token.
DeepSeek · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
DeepSeek-V2.5 outperforms in 0 benchmarks, while Qwen3 VL 235B A22B Thinking is better at 1 benchmark (MMLU). Qwen3 VL 235B A22B Thinking significantly outperforms across most benchmarks.
On price, DeepSeek-V2.5 is roughly 6.9x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Qwen3 VL 235B A22B Thinking also accepts a larger context window (262,144 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-V2.5
- cost matters — it's about 6.9x cheaper per token
Choose Qwen3 VL 235B A22B Thinking
- you want the strongest raw capability — it leads on 1 of 1 shared benchmarks
- you process long inputs — it offers a 262,144 token context window
- you want the most recent training data — it shipped Sep 2025
At a glance
The differences that matter most.
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V2.5 outperforms in 0 benchmarks, while Qwen3 VL 235B A22B Thinking is better at 1 benchmark (MMLU).
Qwen3 VL 235B A22B Thinking significantly outperforms across most benchmarks.
Arena Performance
Playground indexes and blind preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V2.5 ($0.14/1M tokens) is 3.2x cheaper than Qwen3 VL 235B A22B Thinking ($0.45/1M tokens).
For output processing, DeepSeek-V2.5 ($0.28/1M tokens) is 12.5x cheaper than Qwen3 VL 235B A22B Thinking ($3.49/1M tokens).
In conclusion, Qwen3 VL 235B A22B Thinking is more expensive than DeepSeek-V2.5.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Qwen3 VL 235B A22B Thinking has 0.0B more parameters than DeepSeek-V2.5, making it 0.0% larger.
Context Window
Maximum input and output token capacity
Qwen3 VL 235B A22B Thinking accepts 262,144 input tokens compared to DeepSeek-V2.5's 8,192 tokens. Qwen3 VL 235B A22B Thinking can generate longer responses up to 262,144 tokens, while DeepSeek-V2.5 is limited to 8,192 tokens.
Input Capabilities
Supported data types and modalities
Qwen3 VL 235B A22B Thinking supports multimodal inputs, whereas DeepSeek-V2.5 does not.
Qwen3 VL 235B A22B Thinking can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V2.5
Qwen3 VL 235B A22B Thinking
License
Usage and distribution terms
DeepSeek-V2.5 is licensed under deepseek, while Qwen3 VL 235B A22B Thinking uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
deepseek
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
DeepSeek-V2.5 was released on 2024-05-08, while Qwen3 VL 235B A22B Thinking was released on 2025-09-22.
Qwen3 VL 235B A22B Thinking is 17 months newer than DeepSeek-V2.5.
May 8, 2024
2.3 years ago
Sep 22, 2025
11 months ago
1.4yr 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 VL 235B A22B Thinking is available from DeepInfra, Novita.
DeepSeek-V2.5
Qwen3 VL 235B A22B Thinking
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V2.5 and Qwen3 VL 235B A22B Thinking side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V2.5 vs Qwen3 VL 235B A22B Thinking.