Model Comparison

DeepSeek-R1 vs Phi-3.5-vision-instructWhich is better in 2026?

Comparing DeepSeek-R1 and Phi-3.5-vision-instruct across benchmarks, pricing, and capabilities.

Verdict: DeepSeek-R1 vs Phi-3.5-vision-instruct — which is better?

DeepSeek-R1 (by DeepSeek) and Phi-3.5-vision-instruct (by Microsoft) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.

Choose DeepSeek-R1 if…

  • you want the most recent training data — it shipped Jan 2025

Choose Phi-3.5-vision-instruct if…

  • you are already invested in the Microsoft ecosystem
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Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

DeepSeek-R1 and Phi-3.5-vision-instruct don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Arena Performance

Human preference votes

Model Size

Parameter count comparison

666.8B diff

DeepSeek-R1 has 666.8B more parameters than Phi-3.5-vision-instruct, making it 15876.2% larger.

DeepSeek
DeepSeek-R1
671.0Bparameters
Microsoft
Phi-3.5-vision-instruct
4.2Bparameters
671.0B
DeepSeek-R1
4.2B
Phi-3.5-vision-instruct

Context Window

Maximum input and output token capacity

Only DeepSeek-R1 specifies input context (131,072 tokens). Only DeepSeek-R1 specifies output context (131,072 tokens).

DeepSeek
DeepSeek-R1
Input131,072 tokens
Output131,072 tokens
Microsoft
Phi-3.5-vision-instruct
Input- tokens
Output- tokens
Sun Jun 07 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Phi-3.5-vision-instruct supports multimodal inputs, whereas DeepSeek-R1 does not.

Phi-3.5-vision-instruct can handle both text and other forms of data like images, making it suitable for multimodal applications.

DeepSeek-R1

Text
Images
Audio
Video

Phi-3.5-vision-instruct

Text
Images
Audio
Video

License

Usage and distribution terms

Both models are licensed under MIT.

Both models share the same licensing terms, providing consistent usage rights.

DeepSeek-R1

MIT

Open weights

Phi-3.5-vision-instruct

MIT

Open weights

Release Timeline

When each model was launched

DeepSeek-R1 was released on 2025-01-20, while Phi-3.5-vision-instruct was released on 2024-08-23.

DeepSeek-R1 is 5 months newer than Phi-3.5-vision-instruct.

DeepSeek-R1

Jan 20, 2025

1.4 years ago

5mo newer
Phi-3.5-vision-instruct

Aug 23, 2024

1.8 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→

Key Takeaways

DeepSeek
Larger context window (131,072 tokens)
Microsoft
Supports multimodal inputs

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-R1
Microsoft
Phi-3.5-vision-instruct

FAQ

Common questions about DeepSeek-R1 vs Phi-3.5-vision-instruct.

Which is better, DeepSeek-R1 or Phi-3.5-vision-instruct?

DeepSeek-R1 (DeepSeek) and Phi-3.5-vision-instruct (Microsoft) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.

How does DeepSeek-R1 compare to Phi-3.5-vision-instruct in benchmarks?

Phi-3.5-vision-instruct scores ScienceQA: 91.3%, POPE: 86.1%, MMBench: 81.9%, ChartQA: 81.8%, AI2D: 78.1%.

What are the context window sizes for DeepSeek-R1 and Phi-3.5-vision-instruct?

DeepSeek-R1 supports 131K tokens and Phi-3.5-vision-instruct supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between DeepSeek-R1 and Phi-3.5-vision-instruct?

Key differences include multimodal support (no vs yes). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-R1 and Phi-3.5-vision-instruct?

DeepSeek-R1 is developed by DeepSeek and Phi-3.5-vision-instruct is developed by Microsoft.

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