Model Comparison

Jamba 1.5 Large vs Phi-3.5-vision-instruct

Comparing Jamba 1.5 Large and Phi-3.5-vision-instruct across benchmarks, pricing, and capabilities.

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Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

Jamba 1.5 Large 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

393.8B diff

Jamba 1.5 Large has 393.8B more parameters than Phi-3.5-vision-instruct, making it 9376.2% larger.

AI21 Labs
Jamba 1.5 Large
398.0Bparameters
Microsoft
Phi-3.5-vision-instruct
4.2Bparameters
398.0B
Jamba 1.5 Large
4.2B
Phi-3.5-vision-instruct

Context Window

Maximum input and output token capacity

Only Jamba 1.5 Large specifies input context (256,000 tokens). Only Jamba 1.5 Large specifies output context (256,000 tokens).

AI21 Labs
Jamba 1.5 Large
Input256,000 tokens
Output256,000 tokens
Microsoft
Phi-3.5-vision-instruct
Input- tokens
Output- tokens
Thu May 14 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Phi-3.5-vision-instruct supports multimodal inputs, whereas Jamba 1.5 Large does not.

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

Jamba 1.5 Large

Text
Images
Audio
Video

Phi-3.5-vision-instruct

Text
Images
Audio
Video

License

Usage and distribution terms

Jamba 1.5 Large is licensed under Jamba Open Model License, while Phi-3.5-vision-instruct uses MIT.

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

Jamba 1.5 Large

Jamba Open Model License

Open weights

Phi-3.5-vision-instruct

MIT

Open weights

Release Timeline

When each model was launched

Jamba 1.5 Large was released on 2024-08-22, while Phi-3.5-vision-instruct was released on 2024-08-23.

Phi-3.5-vision-instruct is 0 month newer than Jamba 1.5 Large.

Jamba 1.5 Large

Aug 22, 2024

1.7 years ago

Phi-3.5-vision-instruct

Aug 23, 2024

1.7 years ago

1d newer

Knowledge Cutoff

When training data ends

Jamba 1.5 Large has a documented knowledge cutoff of 2024-03-05, while Phi-3.5-vision-instruct's cutoff date is not specified.

We can confirm Jamba 1.5 Large's training data extends to 2024-03-05, but cannot make a direct comparison without Phi-3.5-vision-instruct's cutoff date.

Jamba 1.5 Large

Mar 2024

Phi-3.5-vision-instruct

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Outputs Comparison

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Key Takeaways

AI21 Labs
Larger context window (256,000 tokens)
Microsoft
Supports multimodal inputs

Detailed Comparison

AI Model Comparison Table
Feature
AI21 Labs
Jamba 1.5 Large
Microsoft
Phi-3.5-vision-instruct

FAQ

Common questions about Jamba 1.5 Large vs Phi-3.5-vision-instruct.

Which is better, Jamba 1.5 Large or Phi-3.5-vision-instruct?

Jamba 1.5 Large (AI21 Labs) 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 Jamba 1.5 Large compare to Phi-3.5-vision-instruct in benchmarks?

Jamba 1.5 Large scores ARC-C: 93.0%, GSM8k: 87.0%, MMLU: 81.2%, Arena Hard: 65.4%, TruthfulQA: 58.3%. 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 Jamba 1.5 Large and Phi-3.5-vision-instruct?

Jamba 1.5 Large supports 256K 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 Jamba 1.5 Large and Phi-3.5-vision-instruct?

Key differences include multimodal support (no vs yes), licensing (Jamba Open Model License vs MIT). See the full comparison above for benchmark-by-benchmark results.

Who makes Jamba 1.5 Large and Phi-3.5-vision-instruct?

Jamba 1.5 Large is developed by AI21 Labs and Phi-3.5-vision-instruct is developed by Microsoft.