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 3.3 70B Instruct

Meta

Llama 3.3 70B Instruct is a language model developed by Meta. It achieves strong performance with an average score of 79.9% across 9 benchmarks. It excels particularly in IFEval (92.1%), MGSM (91.1%), HumanEval (88.4%). The model shows particular specialization in code tasks with an average performance of 89.4%. It supports a 256K 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 3.1 8B Instruct

Meta

2024-07-23

Meta

Llama 3.3 70B Instruct

Meta

2024-12-06

4 months newer

Pricing Comparison

Cost per million tokens (USD)

Meta

Llama 3.1 8B Instruct

$0.34 cheaper
Input:$0.03
Output:$0.03
Meta

Llama 3.3 70B Instruct

Input:$0.20
Output:$0.20

Performance Metrics

Context window and performance specifications

Meta

Llama 3.1 8B Instruct

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

Llama 3.3 70B Instruct

Max Context:256.0K
Parameters:70.0B

Average performance across 5 common benchmarks

Meta

Llama 3.1 8B Instruct

Average Score:60.2%
Meta

Llama 3.3 70B Instruct

+17.0%
Average Score:77.2%

Performance comparison across key benchmark categories

Meta

Llama 3.1 8B Instruct

code
65.8%
math
68.4%
general
54.0%
Meta

Llama 3.3 70B Instruct

code
+23.6%
89.4%
math
+15.6%
84.0%
general
+16.7%
70.7%
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

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 3.3 70B Instruct

9 providers

Sambanova

Throughput: 1096 tok/s
Latency: 0.65ms

Together

Throughput: 65 tok/s
Latency: 0.65ms

Hyperbolic

Throughput: 42 tok/s
Latency: 0.65ms

DeepInfra

Throughput: 37 tok/s
Latency: 0.65ms

Fireworks

Throughput: 197 tok/s
Latency: 0.65ms

Groq

Throughput: 268 tok/s
Latency: 0.65ms

Bedrock

Throughput: 100 tok/s
Latency: 0.5ms

Lambda

Throughput: 42 tok/s
Latency: 0.65ms

Cerebras

Throughput: 2220 tok/s
Latency: 0.65ms
Meta

Llama 3.1 8B Instruct

Avg Score:60.2%
Providers:9
Meta

Llama 3.3 70B Instruct

+17.0%
Avg Score:77.2%
Providers:9