Qwen3 VL 30B A3B Thinking vs Qwen3 VL 32B Thinking
Qwen3 VL 32B Thinking leads the LLM Stats Score 23.5 to 18.2.
Alibaba Cloud / Qwen Team · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
Qwen3 VL 32B Thinking leads the overall LLM Stats Score 23.5 to 18.2, ranking #180 overall.
In the 45 individual benchmarks reported for both models, Qwen3 VL 32B Thinking wins 43; this is a narrower head-to-head signal than the composite indexes.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose Qwen3 VL 30B A3B Thinking
- you want predictable pricing at $0.20/M input and $0.99/M output
Choose Qwen3 VL 32B Thinking
- overall performance matters — it scores 23.5 and ranks #180 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you value its reported benchmark strengths — it wins 43 of 45 exact shared results
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
1 more shared indexes
Individual benchmarks
50 reported for Qwen3 VL 30B A3B Thinking · 47 for Qwen3 VL 32B Thinking
Qwen3 VL 30B A3B Thinking outperforms in 2 benchmarks (GPQA, ScreenSpot Pro), while Qwen3 VL 32B Thinking is better at 43 benchmarks (AI2D, AIME 2025, Arena-Hard v2, BFCL-v3, BLINK, CharadesSTA, CharXiv-D, CharXiv-R, Creative Writing v3, DocVQAtest, ERQA, Hallusion Bench, IFEval, Include, InfoVQAtest, LiveBench 20241125, LiveCodeBench v6, LVBench, MathVision, MathVista-Mini, MMBench-V1.1, MMLU, MMLU-Pro, MMLU-ProX, MMLU-Redux, MM-MT-Bench, MMMU-Pro, MMMU (val), MMStar, MuirBench, Multi-IF, MVBench, OCRBench, OCRBench-V2 (en), OCRBench-V2 (zh), OSWorld, PolyMATH, RealWorldQA, ScreenSpot, SimpleQA, SuperGPQA, VideoMMMU, WritingBench).
Qwen3 VL 32B Thinking significantly outperforms across most benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Model Size
Parameter count comparison
Qwen3 VL 32B Thinking has 2.0B more parameters than Qwen3 VL 30B A3B Thinking, making it 6.5% larger.
Context Window
Maximum input and output token capacity
Only Qwen3 VL 30B A3B Thinking specifies input context (131,072 tokens). Only Qwen3 VL 30B A3B Thinking specifies output context (32,768 tokens).
Input capabilities
Documented input modalities across available providers
Both Qwen3 VL 30B A3B Thinking and Qwen3 VL 32B Thinking support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
Qwen3 VL 30B A3B Thinking
Qwen3 VL 32B Thinking
License
Usage and distribution terms
Both models are licensed under Apache 2.0.
Both models share the same licensing terms, providing consistent usage rights.
Apache 2.0
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
Both models were released on 2025-09-22.
They likely represent similar generations of model development.
Sep 22, 2025
1.0 years ago
Sep 22, 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.
Outputs Comparison
Judge for yourself.
Run your own prompts against Qwen3 VL 30B A3B Thinking and Qwen3 VL 32B Thinking side-by-side, then vote on the output you prefer.
FAQ
Common questions about Qwen3 VL 30B A3B Thinking vs Qwen3 VL 32B Thinking.