Model Comparison

DeepSeek VL2 Small vs Phi-3.5-mini-instruct

Comparing DeepSeek VL2 Small and Phi-3.5-mini-instruct across benchmarks, pricing, and capabilities.

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Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

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

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Cost data unavailable.

Lowest available price from all providers
Wed Apr 22 2026 • llm-stats.com
DeepSeek
DeepSeek VL2 Small
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
Microsoft
Phi-3.5-mini-instruct
Input tokens$0.10
Output tokens$0.10
Best providerAzure
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Model Size

Parameter count comparison

12.2B diff

DeepSeek VL2 Small has 12.2B more parameters than Phi-3.5-mini-instruct, making it 321.1% larger.

DeepSeek
DeepSeek VL2 Small
16.0Bparameters
Microsoft
Phi-3.5-mini-instruct
3.8Bparameters
16.0B
DeepSeek VL2 Small
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 Small
Input- tokens
Output- tokens
Microsoft
Phi-3.5-mini-instruct
Input128,000 tokens
Output128,000 tokens
Wed Apr 22 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

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

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

DeepSeek VL2 Small

Text
Images
Audio
Video

Phi-3.5-mini-instruct

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek VL2 Small 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 Small

deepseek

Open weights

Phi-3.5-mini-instruct

MIT

Open weights

Release Timeline

When each model was launched

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

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

DeepSeek VL2 Small

Dec 13, 2024

1.4 years ago

3mo newer
Phi-3.5-mini-instruct

Aug 23, 2024

1.7 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

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Key Takeaways

DeepSeek
Supports multimodal inputs
Microsoft
Larger context window (128,000 tokens)

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek VL2 Small
Microsoft
Phi-3.5-mini-instruct

FAQ

Common questions about DeepSeek VL2 Small vs Phi-3.5-mini-instruct

DeepSeek VL2 Small (DeepSeek) and Phi-3.5-mini-instruct (Microsoft) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.
DeepSeek VL2 Small scores DocVQA: 92.3%, ChartQA: 84.5%, OCRBench: 83.4%, TextVQA: 83.4%, MMBench: 80.3%. Phi-3.5-mini-instruct scores GSM8k: 86.2%, ARC-C: 84.6%, RULER: 84.1%, PIQA: 81.0%, OpenBookQA: 79.2%.
DeepSeek VL2 Small 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.
Key differences include multimodal support (yes vs no), licensing (deepseek vs MIT). See the full comparison above for benchmark-by-benchmark results.
DeepSeek VL2 Small is developed by DeepSeek and Phi-3.5-mini-instruct is developed by Microsoft.