DeepSeek VL2 vs Phi-4-multimodal-instruct
DeepSeek VL2 and Phi-4-multimodal-instruct are closely matched at 3.2 and 3.1 on the LLM Stats Score.
DeepSeek · Microsoft · Updated for 2026
Which is better?
DeepSeek VL2 and Phi-4-multimodal-instruct are closely matched on the overall LLM Stats Score at 3.2 and 3.1.
In the 9 individual benchmarks reported for both models, DeepSeek VL2 wins 5; this is a narrower head-to-head signal than the composite indexes.
DeepSeek VL2 also accepts a larger context window (129,280 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 VL2
- you value its reported benchmark strengths — it wins 5 of 9 exact shared results
- you process long inputs — it offers a 129,280 token context window
Choose Phi-4-multimodal-instruct
- you want the most recent training data — it shipped Feb 2025
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
14 reported for DeepSeek VL2 · 15 for Phi-4-multimodal-instruct
DeepSeek VL2 outperforms in 5 benchmarks (ChartQA, DocVQA, InfoVQA, MathVista, TextVQA), while Phi-4-multimodal-instruct is better at 4 benchmarks (AI2D, MMBench, MMMU, OCRBench).
DeepSeek VL2 has a slight edge in benchmark performance.
Human preference
Blind head-to-head votes and playground preference scores
Model Size
Parameter count comparison
DeepSeek VL2 has 21.4B more parameters than Phi-4-multimodal-instruct, making it 382.1% larger.
Context Window
Maximum input and output token capacity
DeepSeek VL2 accepts 129,280 input tokens compared to Phi-4-multimodal-instruct's 128,000 tokens. DeepSeek VL2 can generate longer responses up to 129,280 tokens, while Phi-4-multimodal-instruct is limited to 128,000 tokens.
Input capabilities
Documented input modalities across available providers
Both DeepSeek VL2 and Phi-4-multimodal-instruct support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
DeepSeek VL2
Phi-4-multimodal-instruct
License
Usage and distribution terms
DeepSeek VL2 is licensed under deepseek, while Phi-4-multimodal-instruct uses MIT.
License differences may affect how you can use these models in commercial or open-source projects.
deepseek
Open weights
MIT
Open weights
Release Timeline
When each model was launched
DeepSeek VL2 was released on 2024-12-13, while Phi-4-multimodal-instruct was released on 2025-02-01.
Phi-4-multimodal-instruct is 2 months newer than DeepSeek VL2.
Dec 13, 2024
1.7 years ago
Feb 1, 2025
1.6 years ago
1mo newerKnowledge Cutoff
When training data ends
Phi-4-multimodal-instruct has a documented knowledge cutoff of 2024-06-01, while DeepSeek VL2'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 DeepSeek VL2's cutoff date.
—
Jun 2024
Provider Availability
DeepSeek VL2 is available from Replicate. Phi-4-multimodal-instruct is available from DeepInfra.
DeepSeek VL2
Phi-4-multimodal-instruct
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek VL2 and Phi-4-multimodal-instruct side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek VL2 vs Phi-4-multimodal-instruct.