Model Comparison

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

Google

Gemini 2.5 Pro Preview 06-05

Google

Gemini 2.5 Pro Preview 06-05 is a multimodal language model developed by Google. It achieves strong performance with an average score of 68.8% across 13 benchmarks. It excels particularly in Global-MMLU-Lite (89.2%), AIME 2025 (88.0%), FACTS Grounding (87.8%). With a 1.1M 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 2025, 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.

Meta

Llama 4 Scout

Meta

2025-04-05

Google

Gemini 2.5 Pro Preview 06-05

Google

2025-06-05

2 months newer

Pricing Comparison

Cost per million tokens (USD)

Google

Gemini 2.5 Pro Preview 06-05

Input:$1.25
Output:$10.00
Meta

Llama 4 Scout

$10.87 cheaper
Input:$0.08
Output:$0.30

Performance Metrics

Context window and performance specifications

Google

Gemini 2.5 Pro Preview 06-05

Max Context:1.1M
Meta

Llama 4 Scout

Larger context
Max Context:20.0M
Parameters:109.0B

Average performance across 3 common benchmarks

Google

Gemini 2.5 Pro Preview 06-05

+26.0%
Average Score:79.1%
Meta

Llama 4 Scout

Average Score:53.1%

Performance comparison across key benchmark categories

Google

Gemini 2.5 Pro Preview 06-05

vision
+0.9%
82.8%
general
+3.5%
69.8%
code
+17.8%
68.1%
Meta

Llama 4 Scout

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

Gemini 2.5 Pro Preview 06-05

2025-01-31

More recent knowledge cutoff means awareness of newer technologies and frameworks

Provider Availability & Performance

Available providers and their performance metrics

Google

Gemini 2.5 Pro Preview 06-05

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 2.5 Pro Preview 06-05

+26.0%
Avg Score:79.1%
Providers:1
Meta

Llama 4 Scout

Avg Score:53.1%
Providers:6