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-3.5-vision-instruct

Microsoft

Phi-3.5-vision-instruct is a multimodal language model developed by Microsoft. It achieves strong performance with an average score of 68.3% across 9 benchmarks. It excels particularly in ScienceQA (91.3%), POPE (86.1%), MMBench (81.9%). The model shows particular specialization in general tasks with an average performance of 75.9%. 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 2024, it represents Microsoft's latest advancement in AI technology.

Microsoft

Phi-3.5-vision-instruct

Microsoft

2024-08-23

Meta

Llama 3.2 11B Instruct

Meta

2024-09-25

1 month newer

Performance Metrics

Context window and performance specifications

Meta

Llama 3.2 11B Instruct

Larger context
Max Context:256.0K
Parameters:10.6B
Microsoft

Phi-3.5-vision-instruct

Max Context:-
Parameters:4.2B

Average performance across 4 common benchmarks

Meta

Llama 3.2 11B Instruct

+7.5%
Average Score:69.2%
Microsoft

Phi-3.5-vision-instruct

Average Score:61.7%

Performance comparison across key benchmark categories

Meta

Llama 3.2 11B Instruct

general
70.1%
vision
+4.3%
61.8%
math
+13.5%
57.4%
Microsoft

Phi-3.5-vision-instruct

general
+5.8%
75.9%
vision
57.5%
math
43.9%
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

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-3.5-vision-instruct

0 providers
Meta

Llama 3.2 11B Instruct

+7.5%
Avg Score:69.2%
Providers:6
Microsoft

Phi-3.5-vision-instruct

Avg Score:61.7%
Providers:0