Model Comparison

DeepSeek VL2 vs Phi-3.5-MoE-instruct

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

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Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

DeepSeek VL2 and Phi-3.5-MoE-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
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
Microsoft
Phi-3.5-MoE-instruct
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

33.0B diff

Phi-3.5-MoE-instruct has 33.0B more parameters than DeepSeek VL2, making it 122.2% larger.

DeepSeek
DeepSeek VL2
27.0Bparameters
Microsoft
Phi-3.5-MoE-instruct
60.0Bparameters
27.0B
DeepSeek VL2
60.0B
Phi-3.5-MoE-instruct

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).

DeepSeek
DeepSeek VL2
Input129,280 tokens
Output129,280 tokens
Microsoft
Phi-3.5-MoE-instruct
Input- tokens
Output- tokens
Wed Apr 22 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

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

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

DeepSeek VL2

Text
Images
Audio
Video

Phi-3.5-MoE-instruct

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek VL2 is licensed under deepseek, while Phi-3.5-MoE-instruct uses MIT.

License differences may affect how you can use these models in commercial or open-source projects.

DeepSeek VL2

deepseek

Open weights

Phi-3.5-MoE-instruct

MIT

Open weights

Release Timeline

When each model was launched

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

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

DeepSeek VL2

Dec 13, 2024

1.4 years ago

3mo newer
Phi-3.5-MoE-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
Larger context window (129,280 tokens)
Supports multimodal inputs
Microsoft

Detailed Comparison

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

FAQ

Common questions about DeepSeek VL2 vs Phi-3.5-MoE-instruct

DeepSeek VL2 (DeepSeek) and Phi-3.5-MoE-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 scores DocVQA: 93.3%, ChartQA: 86.0%, TextVQA: 84.2%, AI2D: 81.4%, OCRBench: 81.1%. Phi-3.5-MoE-instruct scores ARC-C: 91.0%, OpenBookQA: 89.6%, GSM8k: 88.7%, PIQA: 88.6%, RULER: 87.1%.
DeepSeek VL2 supports 129K tokens and Phi-3.5-MoE-instruct supports an unknown number of 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 is developed by DeepSeek and Phi-3.5-MoE-instruct is developed by Microsoft.