Model Comparison

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

Meta

Llama 3.1 8B Instruct

Meta

Llama 3.1 8B Instruct is a language model developed by Meta. It achieves strong performance with an average score of 61.3% across 18 benchmarks. It excels particularly in GSM-8K (CoT) (84.5%), ARC-C (83.4%), API-Bank (82.6%). It supports a 262K token context window for handling large documents. The model is available through 9 API providers. Released in 2024, it represents Meta's latest advancement in AI technology.

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 3.1 8B Instruct

Meta

2024-07-23

Meta

Llama 3.2 11B Instruct

Meta

2024-09-25

2 months newer

Pricing Comparison

Cost per million tokens (USD)

Meta

Llama 3.1 8B Instruct

$0.04 cheaper
Input:$0.03
Output:$0.03
Meta

Llama 3.2 11B Instruct

Input:$0.05
Output:$0.05

Performance Metrics

Context window and performance specifications

Meta

Llama 3.1 8B Instruct

Larger context
Max Context:262.1K
Parameters:8.0B
Meta

Llama 3.2 11B Instruct

Max Context:256.0K
Parameters:10.6B

Average performance across 2 common benchmarks

Meta

Llama 3.1 8B Instruct

Average Score:49.9%
Meta

Llama 3.2 11B Instruct

+3.0%
Average Score:52.9%

Performance comparison across key benchmark categories

Meta

Llama 3.1 8B Instruct

general
54.0%
math
+11.0%
68.4%
Meta

Llama 3.2 11B Instruct

general
+16.1%
70.1%
math
57.4%
Benchmark Scores - Detailed View
Side-by-side comparison of all benchmark scores
Knowledge Cutoff
Training data recency comparison

Llama 3.1 8B Instruct

2023-12-31

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 3.1 8B Instruct

9 providers

Sambanova

Throughput: 1050 tok/s
Latency: 0.5ms

Together

Throughput: 194 tok/s
Latency: 0.5ms

Hyperbolic

Throughput: 200 tok/s
Latency: 0.5ms

DeepInfra

Throughput: 118 tok/s
Latency: 0.5ms

Fireworks

Throughput: 292 tok/s
Latency: 0.5ms

Groq

Throughput: 750 tok/s
Latency: 0.5ms

Bedrock

Throughput: 100 tok/s
Latency: 0.5ms

Lambda

Throughput: 42 tok/s
Latency: 0.5ms

Cerebras

Throughput: 2047 tok/s
Latency: 0.2ms
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 3.1 8B Instruct

Avg Score:49.9%
Providers:9
Meta

Llama 3.2 11B Instruct

+3.0%
Avg Score:52.9%
Providers:6