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-MoE-instruct

Microsoft

Phi-3.5-MoE-instruct is a language model developed by Microsoft. It achieves strong performance with an average score of 65.6% across 31 benchmarks. It excels particularly in ARC-C (91.0%), OpenBookQA (89.6%), GSM8k (88.7%). The model shows particular specialization in reasoning tasks with an average performance of 85.4%. 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-MoE-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-MoE-instruct

Max Context:-
Parameters:60.0B

Average performance across 4 common benchmarks

Meta

Llama 3.2 11B Instruct

Average Score:56.6%
Microsoft

Phi-3.5-MoE-instruct

+1.8%
Average Score:58.5%

Performance comparison across key benchmark categories

Meta

Llama 3.2 11B Instruct

general
+9.2%
70.1%
math
57.4%
Microsoft

Phi-3.5-MoE-instruct

general
60.9%
math
+11.5%
69.0%
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-MoE-instruct

0 providers
Meta

Llama 3.2 11B Instruct

Avg Score:56.6%
Providers:6
Microsoft

Phi-3.5-MoE-instruct

+1.8%
Avg Score:58.5%
Providers:0