Model Comparison

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

Google

Gemini 1.5 Pro

Google

Gemini 1.5 Pro is a multimodal language model developed by Google. It achieves strong performance with an average score of 72.6% across 23 benchmarks. It excels particularly in XSTest (98.8%), HellaSwag (93.3%), GSM8k (90.8%). With a 2.1M token context window, it can handle extensive documents and complex multi-turn conversations. The model is available through 1 API provider. As a multimodal model, it can process and understand text, images, and other input formats seamlessly. Released in 2024, it represents Google'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.

Google

Gemini 1.5 Pro

Google

2024-05-01

Meta

Llama 4 Scout

Meta

2025-04-05

11 months newer

Pricing Comparison

Cost per million tokens (USD)

Google

Gemini 1.5 Pro

Input:$2.50
Output:$10.00
Meta

Llama 4 Scout

$12.12 cheaper
Input:$0.08
Output:$0.30

Performance Metrics

Context window and performance specifications

Google

Gemini 1.5 Pro

Max Context:2.1M
Meta

Llama 4 Scout

Larger context
Max Context:20.0M
Parameters:109.0B

Average performance across 7 common benchmarks

Google

Gemini 1.5 Pro

+5.2%
Average Score:75.5%
Meta

Llama 4 Scout

Average Score:70.3%

Performance comparison across key benchmark categories

Google

Gemini 1.5 Pro

vision
72.3%
math
+4.4%
74.9%
code
+24.2%
74.5%
general
+2.7%
68.9%
Meta

Llama 4 Scout

vision
+9.6%
81.9%
math
70.5%
code
50.3%
general
66.3%
Benchmark Scores - Detailed View
Side-by-side comparison of all benchmark scores
Knowledge Cutoff
Training data recency comparison

Gemini 1.5 Pro

2023-11-01

More recent knowledge cutoff means awareness of newer technologies and frameworks

Provider Availability & Performance

Available providers and their performance metrics

Google

Gemini 1.5 Pro

1 providers

Google

Throughput: 85 tok/s
Latency: 0.7ms
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
Google

Gemini 1.5 Pro

+5.2%
Avg Score:75.5%
Providers:1
Meta

Llama 4 Scout

Avg Score:70.3%
Providers:6