DeepSeek-V3 vs Qwen3 32B
DeepSeek-V3 and Qwen3 32B are closely matched at 15.6 and 18.4 on the LLM Stats Score. Qwen3 32B is 3.3x cheaper per token.
DeepSeek · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
DeepSeek-V3 and Qwen3 32B are closely matched on the overall LLM Stats Score at 15.6 and 18.4.
In the 2 individual benchmarks reported for both models, Qwen3 32B wins 2; this is a narrower head-to-head signal than the composite indexes.
On price, Qwen3 32B is roughly 3.3x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
DeepSeek-V3 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
- you process long inputs — it offers a 131,072 token context window
Choose Qwen3 32B
- you value its reported benchmark strengths — it wins 2 of 2 exact shared results
- cost matters — it's about 3.3x cheaper per token
- you want the most recent training data — it shipped Apr 2025
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
20 reported for DeepSeek-V3 · 9 for Qwen3 32B
DeepSeek-V3 outperforms in 0 benchmarks, while Qwen3 32B is better at 2 benchmarks (AIME 2024, LiveCodeBench).
Qwen3 32B significantly outperforms across most benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V3 ($0.27/1M tokens) is 3.4x more expensive than Qwen3 32B ($0.08/1M tokens).
For output processing, DeepSeek-V3 ($0.89/1M tokens) is 3.2x more expensive than Qwen3 32B ($0.28/1M tokens).
In conclusion, DeepSeek-V3 is more expensive than Qwen3 32B.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V3 has 638.2B more parameters than Qwen3 32B, making it 1945.7% larger.
Context Window
Maximum input and output token capacity
DeepSeek-V3 accepts 131,072 input tokens compared to Qwen3 32B's 40,960 tokens. DeepSeek-V3 can generate longer responses up to 131,072 tokens, while Qwen3 32B is limited to 40,960 tokens.
License
Usage and distribution terms
DeepSeek-V3 is licensed under MIT + Model License (Commercial use allowed), while Qwen3 32B uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
MIT + Model License (Commercial use allowed)
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
DeepSeek-V3 was released on 2024-12-25, while Qwen3 32B was released on 2025-04-29.
Qwen3 32B is 4 months newer than DeepSeek-V3.
Dec 25, 2024
1.8 years ago
Apr 29, 2025
1.5 years ago
4mo 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 is available from DeepSeek, DeepInfra. Qwen3 32B is available from DeepInfra, Novita, Sambanova.
DeepSeek-V3
Qwen3 32B
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V3 and Qwen3 32B side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V3 vs Qwen3 32B.