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Phi-3.5-vision-instruct

Phi-3.5-vision-instruct is Microsoft's 4.2B multimodal model with 128K context and MIT-licensed weights.

Multimodal Language ModelTextImage Free
In plain English

What is this model and why does it matter?

Phi-3.5-vision-instruct is Microsoft's 4.2B multimodal model with 128K context and MIT-licensed weights.

Image understandingOCRChartsMulti-image reasoning
Model overview

Phi-3.5-vision-instruct: features, use cases and important details

Phi-3.5-vision-instruct is a Microsoft Phi-3.5 checkpoint released in August 2024. It is a 4.2B multimodal model.

Verified model facts

Microsoft documents a 128K context window, MIT licensing and a static knowledge cutoff of March 15, 2024.

Best fit

It is best suited to image understanding|ocr|charts|multi-image reasoning.

Limitations

It is a static model, can produce inaccurate outputs and should be evaluated against newer Phi models before a new production deployment.

Get started

How to use this model

  1. Download microsoft/Phi-3.5-vision-instruct from Microsoft's official repository.
  2. Install a supported Transformers/runtime version.
  3. Use the documented chat format.
  4. Provision GPU memory appropriate to the model.
  5. Evaluate output quality and safety before production.
Copy and try

Example prompts

  • Summarize this long technical document.
  • Explain this code and identify bugs.
  • Analyze the supplied image or chart and describe the important information.
Capabilities

What it can do

  • 128K context
  • Instruction following
  • Reasoning
  • Coding support
  • Image understanding
Best for

Practical use cases

  • Image understanding
  • OCR
  • Charts
  • Multi-image reasoning
Pricing

What does it cost?

MIT-licensed downloadable checkpoint; no fixed Microsoft per-token price applies to the model weights.

Simple summaryWeights are MIT licensed; deployment cost depends on hardware or cloud infrastructure.

What stands out

  • MIT license
  • 128K context
  • Official Microsoft weights
  • Strong efficiency focus

Things to consider

  • Static training cutoff
  • Older Phi generation
  • Capability depends on model size and task
Limitations

Important restrictions and trade-offs

  • Can hallucinate or make reasoning errors
  • Not a live knowledge system
  • Requires evaluation on domain-specific tasks
SimplifyAITools verdict

Our editorial take

A well-documented Microsoft checkpoint for image understanding|ocr|charts|multi-image reasoning, especially when permissive licensing and self-hosting matter.

References

Primary sources

  1. Open source 1 ↗
  2. Open source 2 ↗
  3. Open source 3 ↗
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