Model Comparison
Qwen2.5-Omni-7B vs Qwen3 VL 32B Thinking
Qwen3 VL 32B Thinking significantly outperforms across most benchmarks.
Performance Benchmarks
Comparative analysis across standard metrics
Qwen2.5-Omni-7B outperforms in 0 benchmarks, while Qwen3 VL 32B Thinking is better at 12 benchmarks (AI2D, GPQA, MathVision, MMBench-V1.1, MMLU-Pro, MMLU-Redux, MM-MT-Bench, MMMU-Pro, MMStar, MuirBench, MVBench, RealWorldQA).
Qwen3 VL 32B Thinking significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
Cost data unavailable.
Model Size
Parameter count comparison
Qwen3 VL 32B Thinking has 26.0B more parameters than Qwen2.5-Omni-7B, making it 371.4% larger.
Input Capabilities
Supported data types and modalities
Both Qwen2.5-Omni-7B and Qwen3 VL 32B Thinking support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
Qwen2.5-Omni-7B
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
Qwen2.5-Omni-7B was released on 2025-03-27, while Qwen3 VL 32B Thinking was released on 2025-09-22.
Qwen3 VL 32B Thinking is 6 months newer than Qwen2.5-Omni-7B.
Mar 27, 2025
1.0 years ago
Sep 22, 2025
6 months ago
5mo newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Outputs Comparison
Key Takeaways
Qwen2.5-Omni-7B
View detailsAlibaba Cloud / Qwen Team
Qwen3 VL 32B Thinking
View detailsAlibaba Cloud / Qwen Team
Detailed Comparison
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FAQ
Common questions about Qwen2.5-Omni-7B vs Qwen3 VL 32B Thinking