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DeepSeek VL2 Tiny vs Phi-3.5-mini-instruct

DeepSeek VL2 Tiny and Phi-3.5-mini-instruct are closely matched at -4.7 and -3.8 on the LLM Stats Score.

DeepSeek · Microsoft · Updated for 2026

Which is better?

DeepSeek VL2 Tiny and Phi-3.5-mini-instruct are closely matched on the overall LLM Stats Score at -4.7 and -3.8.

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-mini-instruct

  • you want predictable pricing at $0.10/M input and $0.10/M output

At a glance

The differences that matter most.

Core performance indexes
-4.7
#357
-3.8
#352
-12.7
#368
-4.7
#348
Cost, coverage & limits
Benchmark wins
Input price
— / M
$0.10 / M
Output price
— / M
$0.10 / M
Context window
128,000

Individual benchmarks

14 reported for DeepSeek VL2 Tiny · 31 for Phi-3.5-mini-instruct

No common benchmarks found

DeepSeek VL2 Tiny and Phi-3.5-mini-instructdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Human preference

Blind head-to-head votes and playground preference scores

Model Size

Parameter count comparison

800.0M diff

Phi-3.5-mini-instruct has 0.8B more parameters than DeepSeek VL2 Tiny, making it 26.7% larger.

DeepSeek
DeepSeek VL2 Tiny
3.0Bparameters
Microsoft
Phi-3.5-mini-instruct
3.8Bparameters
3.0B
DeepSeek VL2 Tiny
3.8B
Phi-3.5-mini-instruct

Context Window

Maximum input and output token capacity

Only Phi-3.5-mini-instruct specifies input context (128,000 tokens). Only Phi-3.5-mini-instruct specifies output context (128,000 tokens).

DeepSeek
DeepSeek VL2 Tiny
Input- tokens
Output- tokens
Microsoft
Phi-3.5-mini-instruct
Input128,000 tokens
Output128,000 tokens
Wed Sep 23 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

DeepSeek VL2 Tiny supports multimodal inputs, whereas Phi-3.5-mini-instruct does not.

DeepSeek VL2 Tiny can handle both text and other forms of data like images, making it suitable for multimodal applications.

DeepSeek VL2 Tiny

Text
Images
Audio
Video

Phi-3.5-mini-instruct

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek VL2 Tiny is licensed under deepseek, while Phi-3.5-mini-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-mini-instruct

MIT

Open weights

Release Timeline

When each model was launched

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

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

DeepSeek VL2 Tiny

Dec 13, 2024

1.8 years ago

3mo newer
Phi-3.5-mini-instruct

Aug 23, 2024

2.1 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-mini-instruct side-by-side, then vote on the output you prefer.

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

FAQ

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

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

DeepSeek VL2 Tiny and Phi-3.5-mini-instruct are closely matched on the LLM Stats Score at -4.7 and -3.8. DeepSeek VL2 Tiny is made by DeepSeek and Phi-3.5-mini-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-mini-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-mini-instruct scores GSM8k: 86.2%, ARC-C: 84.6%, RULER: 84.1%, PIQA: 81.0%, OpenBookQA: 79.2%.

What are the context window sizes for DeepSeek VL2 Tiny and Phi-3.5-mini-instruct?

DeepSeek VL2 Tiny supports an unknown number of tokens and Phi-3.5-mini-instruct supports 128K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

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

Key differences include LLM Stats Score (-4.7 vs -3.8), multimodal support (yes vs no), licensing (deepseek vs MIT). See the full comparison above for benchmark-by-benchmark results.

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

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