DeepSeek-V3.2 (Thinking) vs Qwen3 Max
DeepSeek-V3.2 (Thinking) significantly outperforms across most benchmarks. DeepSeek-V3.2 (Thinking) is 5.2x cheaper per token.
DeepSeek · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
DeepSeek-V3.2 (Thinking) outperforms in 4 benchmarks (AIME 2025, GPQA, SWE-Bench Verified, t2-bench), while Qwen3 Max is better at 0 benchmarks. DeepSeek-V3.2 (Thinking) significantly outperforms across most benchmarks.
On price, DeepSeek-V3.2 (Thinking) is roughly 5.2x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Qwen3 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 benchmark, pricing, and model metadata for 2026.
Choose DeepSeek-V3.2 (Thinking)
- you want the strongest raw capability — it leads on 4 of 4 shared benchmarks
- cost matters — it's about 5.2x cheaper per token
- you need open weights you can self-host or fine-tune
Choose Qwen3 Max
- you process long inputs — it offers a 256,000 token context window
- you want the most recent training data — it shipped Dec 2025
At a glance
The differences that matter most.
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V3.2 (Thinking) outperforms in 4 benchmarks (AIME 2025, GPQA, SWE-Bench Verified, t2-bench), while Qwen3 Max is better at 0 benchmarks.
DeepSeek-V3.2 (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-V3.2 (Thinking) ($0.28/1M tokens) is 1.8x cheaper than Qwen3 Max ($0.50/1M tokens).
For output processing, DeepSeek-V3.2 (Thinking) ($0.42/1M tokens) is 11.9x cheaper than Qwen3 Max ($5.00/1M tokens).
In conclusion, Qwen3 Max is more expensive than DeepSeek-V3.2 (Thinking).*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Qwen3 Max has 315.0B more parameters than DeepSeek-V3.2 (Thinking), making it 46.0% larger.
Context Window
Maximum input and output token capacity
Qwen3 Max accepts 256,000 input tokens compared to DeepSeek-V3.2 (Thinking)'s 131,072 tokens. Qwen3 Max can generate longer responses up to 131,072 tokens, while DeepSeek-V3.2 (Thinking) is limited to 65,536 tokens.
License
Usage and distribution terms
DeepSeek-V3.2 (Thinking) is licensed under MIT, while Qwen3 Max uses a proprietary license.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Proprietary
Closed source
Release Timeline
When each model was launched
DeepSeek-V3.2 (Thinking) was released on 2025-12-01, while Qwen3 Max was released on 2025-12-15.
Qwen3 Max is 0 month newer than DeepSeek-V3.2 (Thinking).
Dec 1, 2025
8 months ago
Dec 15, 2025
8 months ago
2w 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-V3.2 (Thinking) is available from DeepSeek. Qwen3 Max is available from Novita.
DeepSeek-V3.2 (Thinking)
Qwen3 Max
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V3.2 (Thinking) and Qwen3 Max side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V3.2 (Thinking) vs Qwen3 Max.