Model Comparison

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

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 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 3.3 70B Instruct

Meta

2024-12-06

Meta

Llama 4 Scout

Meta

2025-04-05

4 months newer

Pricing Comparison

Cost per million tokens (USD)

Meta

Llama 3.3 70B Instruct

Input:$0.20
Output:$0.20
Meta

Llama 4 Scout

$0.02 cheaper
Input:$0.08
Output:$0.30

Performance Metrics

Context window and performance specifications

Meta

Llama 3.3 70B Instruct

Max Context:256.0K
Parameters:70.0B
Meta

Llama 4 Scout

Larger context
Max Context:20.0M
Parameters:109.0B

Average performance across 5 common benchmarks

Meta

Llama 3.3 70B Instruct

+4.3%
Average Score:74.7%
Meta

Llama 4 Scout

Average Score:70.4%

Performance comparison across key benchmark categories

Meta

Llama 3.3 70B Instruct

code
+39.1%
89.4%
math
+13.5%
84.0%
general
+4.4%
70.7%
Meta

Llama 4 Scout

code
50.3%
math
70.5%
general
66.3%
Benchmark Scores - Detailed View
Side-by-side comparison of all benchmark scores

Provider Availability & Performance

Available providers and their performance metrics

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 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 3.3 70B Instruct

+4.3%
Avg Score:74.7%
Providers:9
Meta

Llama 4 Scout

Avg Score:70.4%
Providers:6