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.

Meta

Llama 4 Scout

Meta

Llama 4 Scout is a multimodal language model developed by Meta. It achieves strong performance with an average score of 67.3% across 12 benchmarks. It excels particularly in DocVQA (94.4%), MGSM (90.6%), ChartQA (88.8%). The model shows particular specialization in vision tasks with an average performance of 81.9%. With a 20.0M token context window, it can handle extensive documents and complex multi-turn conversations. 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 2025, it represents Meta's latest advancement in AI technology.

Meta

Llama 3.2 11B Instruct

Meta

2024-09-25

Meta

Llama 4 Scout

Meta

2025-04-05

6 months newer

Pricing Comparison

Cost per million tokens (USD)

Meta

Llama 3.2 11B Instruct

$0.28 cheaper
Input:$0.05
Output:$0.05
Meta

Llama 4 Scout

Input:$0.08
Output:$0.30

Performance Metrics

Context window and performance specifications

Meta

Llama 3.2 11B Instruct

Max Context:256.0K
Parameters:10.6B
Meta

Llama 4 Scout

Larger context
Max Context:20.0M
Parameters:109.0B

Average performance across 8 common benchmarks

Meta

Llama 3.2 11B Instruct

Average Score:62.6%
Meta

Llama 4 Scout

+12.5%
Average Score:75.1%

Performance comparison across key benchmark categories

Meta

Llama 3.2 11B Instruct

vision
61.8%
math
57.4%
general
+3.8%
70.1%
Meta

Llama 4 Scout

vision
+20.1%
81.9%
math
+13.1%
70.5%
general
66.3%
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
Meta

Llama 4 Scout

6 providers

Together

Throughput: 106.9 tok/s
Latency: 0.54ms

DeepInfra

Throughput: 76.1 tok/s
Latency: 0.31ms

Fireworks

Throughput: 116.1 tok/s
Latency: 0.53ms

Groq

Throughput: 776.1 tok/s
Latency: 1.08ms

Novita

Throughput: 69.82 tok/s
Latency: 0.85ms

Lambda

Throughput: 139.7 tok/s
Latency: 0.43ms
Meta

Llama 3.2 11B Instruct

Avg Score:62.6%
Providers:6
Meta

Llama 4 Scout

+12.5%
Avg Score:75.1%
Providers:6