Llama 3.2 90B Instruct vs Phi-4-multimodal-instruct
Llama 3.2 90B Instruct and Phi-4-multimodal-instruct are closely matched at 5.2 and 2.8 on the LLM Stats Score. Phi-4-multimodal-instruct is 5.8x cheaper per token.
Meta · Microsoft · Updated for 2026
Which is better?
Llama 3.2 90B Instruct and Phi-4-multimodal-instruct are closely matched on the overall LLM Stats Score at 5.2 and 2.8.
In the 7 individual benchmarks reported for both models, Llama 3.2 90B Instruct wins 4; this is a narrower head-to-head signal than the composite indexes.
On price, Phi-4-multimodal-instruct is roughly 5.8x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose Llama 3.2 90B Instruct
- you value its reported benchmark strengths — it wins 4 of 7 exact shared results
Choose Phi-4-multimodal-instruct
- cost matters — it's about 5.8x cheaper per token
- you want the most recent training data — it shipped Feb 2025
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
13 reported for Llama 3.2 90B Instruct · 15 for Phi-4-multimodal-instruct
Llama 3.2 90B Instruct outperforms in 4 benchmarks (AI2D, ChartQA, MMMU, MMMU-Pro), while Phi-4-multimodal-instruct is better at 3 benchmarks (DocVQA, MathVista, TextVQA).
Llama 3.2 90B Instruct has a slight edge in benchmark performance.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, Llama 3.2 90B Instruct ($0.35/1M tokens) is 7.0x more expensive than Phi-4-multimodal-instruct ($0.05/1M tokens).
For output processing, Llama 3.2 90B Instruct ($0.40/1M tokens) is 4.0x more expensive than Phi-4-multimodal-instruct ($0.10/1M tokens).
In conclusion, Llama 3.2 90B Instruct is more expensive than Phi-4-multimodal-instruct.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Llama 3.2 90B Instruct has 84.4B more parameters than Phi-4-multimodal-instruct, making it 1507.1% larger.
Context Window
Maximum input and output token capacity
Both models have the same input context window of 128,000 tokens. Both models can generate responses up to 128,000 tokens.
Input capabilities
Documented input modalities across available providers
Both Llama 3.2 90B Instruct and Phi-4-multimodal-instruct support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
Llama 3.2 90B Instruct
Phi-4-multimodal-instruct
License
Usage and distribution terms
Llama 3.2 90B Instruct is licensed under Llama 3.2, while Phi-4-multimodal-instruct uses MIT.
License differences may affect how you can use these models in commercial or open-source projects.
Llama 3.2
Open weights
MIT
Open weights
Release Timeline
When each model was launched
Llama 3.2 90B Instruct was released on 2024-09-25, while Phi-4-multimodal-instruct was released on 2025-02-01.
Phi-4-multimodal-instruct is 4 months newer than Llama 3.2 90B Instruct.
Sep 25, 2024
2.0 years ago
Feb 1, 2025
1.7 years ago
4mo newerKnowledge Cutoff
When training data ends
Phi-4-multimodal-instruct has a documented knowledge cutoff of 2024-06-01, while Llama 3.2 90B Instruct's cutoff date is not specified.
We can confirm Phi-4-multimodal-instruct's training data extends to 2024-06-01, but cannot make a direct comparison without Llama 3.2 90B Instruct's cutoff date.
—
Jun 2024
Provider Availability
Llama 3.2 90B Instruct is available from DeepInfra, Bedrock, Fireworks, Together, Hyperbolic. Phi-4-multimodal-instruct is available from DeepInfra.
Llama 3.2 90B Instruct
Phi-4-multimodal-instruct
Outputs Comparison
Judge for yourself.
Run your own prompts against Llama 3.2 90B Instruct and Phi-4-multimodal-instruct side-by-side, then vote on the output you prefer.
FAQ
Common questions about Llama 3.2 90B Instruct vs Phi-4-multimodal-instruct.