Phi-4-multimodal-instruct vs Qwen2.5-Omni-7B
Phi-4-multimodal-instruct and Qwen2.5-Omni-7B are closely matched at 3.0 and 5.3 on the LLM Stats Score.
Microsoft · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
Phi-4-multimodal-instruct and Qwen2.5-Omni-7B are closely matched on the overall LLM Stats Score at 3.0 and 5.3.
In the 7 individual benchmarks reported for both models, Qwen2.5-Omni-7B wins 6; 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 Phi-4-multimodal-instruct
- you want predictable pricing at $0.05/M input and $0.10/M output
Choose Qwen2.5-Omni-7B
- you value its reported benchmark strengths — it wins 6 of 7 exact shared results
- you want the most recent training data — it shipped Mar 2025
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
15 reported for Phi-4-multimodal-instruct · 45 for Qwen2.5-Omni-7B
Phi-4-multimodal-instruct outperforms in 1 benchmarks (MMMU-Pro), while Qwen2.5-Omni-7B is better at 6 benchmarks (AI2D, ChartQA, DocVQA, MathVista, MMMU, TextVQA).
Qwen2.5-Omni-7B significantly outperforms across most benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Model Size
Parameter count comparison
Qwen2.5-Omni-7B has 1.4B more parameters than Phi-4-multimodal-instruct, making it 25.0% larger.
Context Window
Maximum input and output token capacity
Only Phi-4-multimodal-instruct specifies input context (128,000 tokens). Only Phi-4-multimodal-instruct specifies output context (128,000 tokens).
Input capabilities
Documented input modalities across available providers
Both Phi-4-multimodal-instruct and Qwen2.5-Omni-7B support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
Phi-4-multimodal-instruct
Qwen2.5-Omni-7B
License
Usage and distribution terms
Phi-4-multimodal-instruct is licensed under MIT, while Qwen2.5-Omni-7B 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
Phi-4-multimodal-instruct was released on 2025-02-01, while Qwen2.5-Omni-7B was released on 2025-03-27.
Qwen2.5-Omni-7B is 2 months newer than Phi-4-multimodal-instruct.
Feb 1, 2025
1.6 years ago
Mar 27, 2025
1.4 years ago
1mo newerKnowledge Cutoff
When training data ends
Phi-4-multimodal-instruct has a documented knowledge cutoff of 2024-06-01, while Qwen2.5-Omni-7B's cutoff date is not specified.
We can confirm Phi-4-multimodal-instruct's training data extends to 2024-06-01, but cannot make a direct comparison without Qwen2.5-Omni-7B's cutoff date.
Jun 2024
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Outputs Comparison
Judge for yourself.
Run your own prompts against Phi-4-multimodal-instruct and Qwen2.5-Omni-7B side-by-side, then vote on the output you prefer.
FAQ
Common questions about Phi-4-multimodal-instruct vs Qwen2.5-Omni-7B.