Model Comparison
DeepSeek-V3.2 (Non-thinking) vs Qwen3-Next-80B-A3B-InstructWhich is better in 2026?
Comparing DeepSeek-V3.2 (Non-thinking) and Qwen3-Next-80B-A3B-Instruct across benchmarks, pricing, and capabilities.
Verdict: DeepSeek-V3.2 (Non-thinking) vs Qwen3-Next-80B-A3B-Instruct — which is better?
DeepSeek-V3.2 (Non-thinking) (by DeepSeek) and Qwen3-Next-80B-A3B-Instruct (by Alibaba Cloud / Qwen Team) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.
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.
Choose DeepSeek-V3.2 (Non-thinking) if…
- 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-Instruct if…
- you want predictable pricing at $0.15/M input and $1.50/M output
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V3.2 (Non-thinking) and Qwen3-Next-80B-A3B-Instructdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Arena Performance
Human preference votes
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-Instruct ($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-Instruct ($1.50/1M tokens).
In conclusion, Qwen3-Next-80B-A3B-Instruct 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-Instruct, 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-Instruct's 65,536 tokens. Qwen3-Next-80B-A3B-Instruct 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-Instruct 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-Instruct was released on 2025-09-10.
DeepSeek-V3.2 (Non-thinking) is 3 months newer than Qwen3-Next-80B-A3B-Instruct.
Dec 1, 2025
7 months ago
2mo newerSep 10, 2025
10 months 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-Instruct is available from Novita.
DeepSeek-V3.2 (Non-thinking)
Qwen3-Next-80B-A3B-Instruct
Outputs Comparison
Key Takeaways
Qwen3-Next-80B-A3B-Instruct
View detailsAlibaba Cloud / Qwen Team
Detailed Comparison
Interactive Arena
Judge for yourself.
Run your own prompts against DeepSeek-V3.2 (Non-thinking) and Qwen3-Next-80B-A3B-Instruct side-by-side, then vote on the output you prefer.
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FAQ
Common questions about DeepSeek-V3.2 (Non-thinking) vs Qwen3-Next-80B-A3B-Instruct.