DeepSeek-V3.2-Speciale vs Qwen3.5-397B-A17B
DeepSeek-V3.2-Speciale and Qwen3.5-397B-A17B are closely matched at 34.3 and 38.9 on the LLM Stats Score. DeepSeek-V3.2-Speciale is 4.3x cheaper per token.
DeepSeek · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
DeepSeek-V3.2-Speciale and Qwen3.5-397B-A17B are closely matched on the overall LLM Stats Score at 34.3 and 38.9.
In the 6 individual benchmarks reported for both models, Qwen3.5-397B-A17B wins 4; this is a narrower head-to-head signal than the composite indexes.
On price, DeepSeek-V3.2-Speciale is roughly 4.3x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Qwen3.5-397B-A17B also accepts a larger context window (262,144 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-Speciale
- cost matters — it's about 4.3x cheaper per token
Choose Qwen3.5-397B-A17B
- you value its reported benchmark strengths — it wins 4 of 6 exact shared results
- you process long inputs — it offers a 262,144 token context window
- you want the most recent training data — it shipped Feb 2026
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
8 reported for DeepSeek-V3.2-Speciale · 38 for Qwen3.5-397B-A17B
DeepSeek-V3.2-Speciale outperforms in 2 benchmarks (HMMT 2025, Humanity's Last Exam), while Qwen3.5-397B-A17B is better at 4 benchmarks (SWE-Bench Verified, t2-bench, Terminal-Bench 2.0, Toolathlon).
Qwen3.5-397B-A17B shows notably better performance in the majority of benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V3.2-Speciale ($0.28/1M tokens) is 2.1x cheaper than Qwen3.5-397B-A17B ($0.60/1M tokens).
For output processing, DeepSeek-V3.2-Speciale ($0.42/1M tokens) is 8.6x cheaper than Qwen3.5-397B-A17B ($3.60/1M tokens).
In conclusion, Qwen3.5-397B-A17B is more expensive than DeepSeek-V3.2-Speciale.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V3.2-Speciale has 288.0B more parameters than Qwen3.5-397B-A17B, making it 72.5% larger.
Context Window
Maximum input and output token capacity
Qwen3.5-397B-A17B accepts 262,144 input tokens compared to DeepSeek-V3.2-Speciale's 131,072 tokens. DeepSeek-V3.2-Speciale can generate longer responses up to 131,072 tokens, while Qwen3.5-397B-A17B is limited to 64,000 tokens.
Input capabilities
Documented input modalities across available providers
Qwen3.5-397B-A17B supports multimodal inputs, whereas DeepSeek-V3.2-Speciale does not.
Qwen3.5-397B-A17B can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V3.2-Speciale
Qwen3.5-397B-A17B
License
Usage and distribution terms
DeepSeek-V3.2-Speciale is licensed under MIT, while Qwen3.5-397B-A17B 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-Speciale was released on 2025-12-01, while Qwen3.5-397B-A17B was released on 2026-02-16.
Qwen3.5-397B-A17B is 3 months newer than DeepSeek-V3.2-Speciale.
Dec 1, 2025
9 months ago
Feb 16, 2026
6 months ago
2mo 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-Speciale is available from DeepSeek. Qwen3.5-397B-A17B is available from Novita.
DeepSeek-V3.2-Speciale
Qwen3.5-397B-A17B
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V3.2-Speciale and Qwen3.5-397B-A17B side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V3.2-Speciale vs Qwen3.5-397B-A17B.