DeepSeek VL2 vs Phi-3.5-vision-instruct
DeepSeek VL2 and Phi-3.5-vision-instruct are closely matched at 2.9 and -3.4 on the LLM Stats Score.
DeepSeek · Microsoft · Updated for 2026
Which is better?
DeepSeek VL2 and Phi-3.5-vision-instruct are closely matched on the overall LLM Stats Score at 2.9 and -3.4.
In the 6 individual benchmarks reported for both models, DeepSeek VL2 wins 5; 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 DeepSeek VL2
- you value its reported benchmark strengths — it wins 5 of 6 exact shared results
- you want the most recent training data — it shipped Dec 2024
Choose Phi-3.5-vision-instruct
- you are already invested in the Microsoft ecosystem
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 · 9 for Phi-3.5-vision-instruct
DeepSeek VL2 outperforms in 5 benchmarks (AI2D, ChartQA, MathVista, MMMU, TextVQA), while Phi-3.5-vision-instruct is better at 1 benchmark (MMBench).
DeepSeek VL2 significantly outperforms across most benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Model Size
Parameter count comparison
DeepSeek VL2 has 22.8B more parameters than Phi-3.5-vision-instruct, making it 542.9% larger.
Context Window
Maximum input and output token capacity
Only DeepSeek VL2 specifies input context (129,280 tokens). Only DeepSeek VL2 specifies output context (129,280 tokens).
Input capabilities
Documented input modalities across available providers
Both DeepSeek VL2 and Phi-3.5-vision-instruct support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
DeepSeek VL2
Phi-3.5-vision-instruct
License
Usage and distribution terms
DeepSeek VL2 is licensed under deepseek, while Phi-3.5-vision-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-3.5-vision-instruct was released on 2024-08-23.
DeepSeek VL2 is 4 months newer than Phi-3.5-vision-instruct.
Dec 13, 2024
1.7 years ago
3mo newerAug 23, 2024
2.0 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 DeepSeek VL2 and Phi-3.5-vision-instruct side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek VL2 vs Phi-3.5-vision-instruct.