DeepSeek-V3.2 (Thinking) vs Qwen3-Next-80B-A3B-Instruct
DeepSeek-V3.2 (Thinking) significantly outperforms across most benchmarks. DeepSeek-V3.2 (Thinking) is 1.5x cheaper per token.
DeepSeek · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
DeepSeek-V3.2 (Thinking) outperforms in 3 benchmarks (AIME 2025, GPQA, MMLU-Pro), while Qwen3-Next-80B-A3B-Instruct is better at 0 benchmarks. DeepSeek-V3.2 (Thinking) significantly outperforms across most benchmarks.
On price, DeepSeek-V3.2 (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 (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 benchmark, pricing, and model metadata for 2026.
Choose DeepSeek-V3.2 (Thinking)
- you want the strongest raw capability — it leads on 3 of 3 shared benchmarks
- 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
- you want predictable pricing at $0.15/M input and $1.50/M output
At a glance
The differences that matter most.
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V3.2 (Thinking) outperforms in 3 benchmarks (AIME 2025, GPQA, MMLU-Pro), while Qwen3-Next-80B-A3B-Instruct 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 1.9x more expensive than Qwen3-Next-80B-A3B-Instruct ($0.15/1M tokens).
For output processing, DeepSeek-V3.2 (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 (Thinking).*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V3.2 (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 (Thinking) accepts 131,072 input tokens compared to Qwen3-Next-80B-A3B-Instruct's 65,536 tokens. Both models can generate responses up to 65,536 tokens.
License
Usage and distribution terms
DeepSeek-V3.2 (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 (Thinking) was released on 2025-12-01, while Qwen3-Next-80B-A3B-Instruct was released on 2025-09-10.
DeepSeek-V3.2 (Thinking) is 3 months newer than Qwen3-Next-80B-A3B-Instruct.
Dec 1, 2025
8 months ago
2mo newerSep 10, 2025
11 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 (Thinking) is available from DeepSeek. Qwen3-Next-80B-A3B-Instruct is available from Novita.
DeepSeek-V3.2 (Thinking)
Qwen3-Next-80B-A3B-Instruct
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V3.2 (Thinking) and Qwen3-Next-80B-A3B-Instruct side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V3.2 (Thinking) vs Qwen3-Next-80B-A3B-Instruct.