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DeepSeek VL2 Tiny vs Phi-3.5-vision-instruct

DeepSeek VL2 Tiny and Phi-3.5-vision-instruct are closely matched at -4.5 and -3.2 on the LLM Stats Score.

DeepSeek · Microsoft · Updated for 2026

Which is better?

DeepSeek VL2 Tiny and Phi-3.5-vision-instruct are closely matched on the overall LLM Stats Score at -4.5 and -3.2.

In the 6 individual benchmarks reported for both models, Phi-3.5-vision-instruct wins 4; 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 Tiny

  • you want the most recent training data — it shipped Dec 2024

Choose Phi-3.5-vision-instruct

  • you value its reported benchmark strengths — it wins 4 of 6 exact shared results

At a glance

The differences that matter most.

DeepSeek VL2 Tiny
DeepSeek
Phi-3.5-vision-instruct
Microsoft
Core performance indexes
LLM Stats Score
-4.5
#340
-3.2
#332
Reasoning
-12.4
#349
-4.6
#329
Cost, coverage & limits
Benchmark wins
2 of 6
4 of 6
Input price
— / M
— / M
Output price
— / M
— / M
Context window
—
—

Capability indexes

Additional strengths measured across groups of related public benchmarks

2 shared
Index
DeepSeek VL2 Tiny
Phi-3.5-vision-instruct
Vision
-4.6#196
-3.1#190
Multimodal
-2.5#157
-0.6#151
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

14 reported for DeepSeek VL2 Tiny · 9 for Phi-3.5-vision-instruct

6 shared

DeepSeek VL2 Tiny outperforms in 2 benchmarks (MathVista, TextVQA), while Phi-3.5-vision-instruct is better at 4 benchmarks (AI2D, ChartQA, MMBench, MMMU).

Phi-3.5-vision-instruct shows notably better performance in the majority of benchmarks.

Sat Sep 05 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Model Size

Parameter count comparison

1.2B diff

Phi-3.5-vision-instruct has 1.2B more parameters than DeepSeek VL2 Tiny, making it 40.0% larger.

DeepSeek
DeepSeek VL2 Tiny
3.0Bparameters
Microsoft
Phi-3.5-vision-instruct
4.2Bparameters
3.0B
DeepSeek VL2 Tiny
4.2B
Phi-3.5-vision-instruct

Input capabilities

Documented input modalities across available providers

Both DeepSeek VL2 Tiny 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 Tiny

Text
Images
Audio
Video

Phi-3.5-vision-instruct

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek VL2 Tiny 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 VL2 Tiny

deepseek

Open weights

Phi-3.5-vision-instruct

MIT

Open weights

Release Timeline

When each model was launched

DeepSeek VL2 Tiny was released on 2024-12-13, while Phi-3.5-vision-instruct was released on 2024-08-23.

DeepSeek VL2 Tiny is 4 months newer than Phi-3.5-vision-instruct.

DeepSeek VL2 Tiny

Dec 13, 2024

1.7 years ago

3mo newer
Phi-3.5-vision-instruct

Aug 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.

No cutoff dates available

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Judge for yourself.

Run your own prompts against DeepSeek VL2 Tiny and Phi-3.5-vision-instruct side-by-side, then vote on the output you prefer.

DeepSeek VL2 Tiny
✓ Preferred
Phi-3.5-vision-instruct
Open in Playground

FAQ

Common questions about DeepSeek VL2 Tiny vs Phi-3.5-vision-instruct.

Which is better, DeepSeek VL2 Tiny or Phi-3.5-vision-instruct?

DeepSeek VL2 Tiny and Phi-3.5-vision-instruct are closely matched on the LLM Stats Score at -4.5 and -3.2. DeepSeek VL2 Tiny is made by DeepSeek and Phi-3.5-vision-instruct is made by Microsoft. The best choice depends on your use case — compare their capability indexes, individual benchmarks, pricing, and limits above.

How does DeepSeek VL2 Tiny compare to Phi-3.5-vision-instruct in benchmarks?

DeepSeek VL2 Tiny scores DocVQA: 88.9%, ChartQA: 81.0%, OCRBench: 80.9%, TextVQA: 80.7%, AI2D: 71.6%. Phi-3.5-vision-instruct scores ScienceQA: 91.3%, POPE: 86.1%, MMBench: 81.9%, ChartQA: 81.8%, AI2D: 78.1%.

What are the main differences between DeepSeek VL2 Tiny and Phi-3.5-vision-instruct?

Key differences include LLM Stats Score (-4.5 vs -3.2), licensing (deepseek vs MIT). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek VL2 Tiny and Phi-3.5-vision-instruct?

DeepSeek VL2 Tiny is developed by DeepSeek and Phi-3.5-vision-instruct is developed by Microsoft.

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