DeepSeek-V3.2 (Non-thinking) vs Qwen3-Next-80B-A3B-Thinking
Comparing DeepSeek-V3.2 (Non-thinking) and Qwen3-Next-80B-A3B-Thinking across benchmarks, pricing, and capabilities.
DeepSeek · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
DeepSeek-V3.2 (Non-thinking) and Qwen3-Next-80B-A3B-Thinking trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.
On price, DeepSeek-V3.2 (Non-thinking) is roughly 1.5x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
DeepSeek-V3.2 (Non-thinking) also accepts a larger context window (131,072 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-V3.2 (Non-thinking)
- cost matters — it's about 1.5x cheaper per token
- you process long inputs — it offers a 131,072 token context window
- you want the most recent training data — it shipped Dec 2025
Choose Qwen3-Next-80B-A3B-Thinking
- you want predictable pricing at $0.15/M input and $1.50/M output
At a glance
The differences that matter most.
Individual benchmarks
0 reported for DeepSeek-V3.2 (Non-thinking) · 23 for Qwen3-Next-80B-A3B-Thinking
DeepSeek-V3.2 (Non-thinking) and Qwen3-Next-80B-A3B-Thinkingdon'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-V3.2 (Non-thinking) ($0.28/1M tokens) is 1.9x more expensive than Qwen3-Next-80B-A3B-Thinking ($0.15/1M tokens).
For output processing, DeepSeek-V3.2 (Non-thinking) ($0.42/1M tokens) is 3.6x cheaper than Qwen3-Next-80B-A3B-Thinking ($1.50/1M tokens).
In conclusion, Qwen3-Next-80B-A3B-Thinking is more expensive than DeepSeek-V3.2 (Non-thinking).*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V3.2 (Non-thinking) has 605.0B more parameters than Qwen3-Next-80B-A3B-Thinking, making it 756.3% larger.
Context Window
Maximum input and output token capacity
DeepSeek-V3.2 (Non-thinking) accepts 131,072 input tokens compared to Qwen3-Next-80B-A3B-Thinking's 65,536 tokens. Qwen3-Next-80B-A3B-Thinking can generate longer responses up to 65,536 tokens, while DeepSeek-V3.2 (Non-thinking) is limited to 8,192 tokens.
License
Usage and distribution terms
DeepSeek-V3.2 (Non-thinking) is licensed under MIT, while Qwen3-Next-80B-A3B-Thinking uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
DeepSeek-V3.2 (Non-thinking) was released on 2025-12-01, while Qwen3-Next-80B-A3B-Thinking was released on 2025-09-10.
DeepSeek-V3.2 (Non-thinking) is 3 months newer than Qwen3-Next-80B-A3B-Thinking.
Dec 1, 2025
9 months ago
2mo newerSep 10, 2025
1.0 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-V3.2 (Non-thinking) is available from DeepSeek. Qwen3-Next-80B-A3B-Thinking is available from Novita.
DeepSeek-V3.2 (Non-thinking)
Qwen3-Next-80B-A3B-Thinking
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V3.2 (Non-thinking) and Qwen3-Next-80B-A3B-Thinking side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V3.2 (Non-thinking) vs Qwen3-Next-80B-A3B-Thinking.