Model Comparison

Comprehensive side-by-side analysis of model capabilities and performance

Meta

Llama 3.2 11B Instruct

Meta

Llama 3.2 11B Instruct is a multimodal language model developed by Meta. It achieves strong performance with an average score of 63.6% across 11 benchmarks. It excels particularly in AI2D (91.1%), DocVQA (88.4%), ChartQA (83.4%). It supports a 256K token context window for handling large documents. The model is available through 6 API providers. As a multimodal model, it can process and understand text, images, and other input formats seamlessly. Released in 2024, it represents Meta's latest advancement in AI technology.

Microsoft

Phi-4-multimodal-instruct

Microsoft

Phi-4-multimodal-instruct is a multimodal language model developed by Microsoft. It achieves strong performance with an average score of 72.0% across 15 benchmarks. It excels particularly in ScienceQA Visual (97.5%), DocVQA (93.2%), MMBench (86.7%). The model shows particular specialization in general tasks with an average performance of 75.8%. It supports a 256K token context window for handling large documents. The model is available through 1 API provider. As a multimodal model, it can process and understand text, images, and other input formats seamlessly. It's licensed for commercial use, making it suitable for enterprise applications. Released in 2025, it represents Microsoft's latest advancement in AI technology.

Meta

Llama 3.2 11B Instruct

Meta

2024-09-25

Microsoft

Phi-4-multimodal-instruct

Microsoft

2025-02-01

4 months newer

Pricing Comparison

Cost per million tokens (USD)

Meta

Llama 3.2 11B Instruct

$0.05 cheaper
Input:$0.05
Output:$0.05
Microsoft

Phi-4-multimodal-instruct

Input:$0.05
Output:$0.10

Performance Metrics

Context window and performance specifications

Meta

Llama 3.2 11B Instruct

Max Context:256.0K
Parameters:10.6B
Microsoft

Phi-4-multimodal-instruct

Max Context:256.0K
Parameters:5.6B

Average performance across 6 common benchmarks

Meta

Llama 3.2 11B Instruct

Average Score:66.4%
Microsoft

Phi-4-multimodal-instruct

+2.5%
Average Score:68.8%

Performance comparison across key benchmark categories

Meta

Llama 3.2 11B Instruct

general
70.1%
vision
61.8%
math
57.4%
Microsoft

Phi-4-multimodal-instruct

general
+5.7%
75.8%
vision
+7.8%
69.7%
math
+5.0%
62.4%
Benchmark Scores - Detailed View
Side-by-side comparison of all benchmark scores
Knowledge Cutoff
Training data recency comparison

Llama 3.2 11B Instruct

2023-12-31

Phi-4-multimodal-instruct

2024-06-01

More recent knowledge cutoff means awareness of newer technologies and frameworks

Provider Availability & Performance

Available providers and their performance metrics

Meta

Llama 3.2 11B Instruct

6 providers

Sambanova

Throughput: 100 tok/s
Latency: 0.5ms

Together

Throughput: 168 tok/s
Latency: 0.5ms

DeepInfra

Throughput: 108 tok/s
Latency: 0.5ms

Fireworks

Throughput: 125 tok/s
Latency: 0.2ms

Groq

Throughput: 100 tok/s
Latency: 0.5ms

Bedrock

Throughput: 100 tok/s
Latency: 0.5ms
Microsoft

Phi-4-multimodal-instruct

1 providers

DeepInfra

Throughput: 25 tok/s
Latency: 0.5ms
Meta

Llama 3.2 11B Instruct

Avg Score:66.4%
Providers:6
Microsoft

Phi-4-multimodal-instruct

+2.5%
Avg Score:68.8%
Providers:1