Model Comparison

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

Google

Gemini 2.0 Flash Thinking

Google

Gemini 2.0 Flash Thinking is a multimodal language model developed by Google. It achieves strong performance with an average score of 74.3% across 3 benchmarks. Notable strengths include MMMU (75.4%), GPQA (74.2%), AIME 2024 (73.3%). 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.

Google

Gemini 2.0 Flash Thinking

Google

2025-01-21

Meta

Llama 4 Scout

Meta

2025-04-05

2 months newer

Performance Metrics

Context window and performance specifications

Google

Gemini 2.0 Flash Thinking

Max Context:-
Meta

Llama 4 Scout

Larger context
Max Context:20.0M
Parameters:109.0B

Average performance across 2 common benchmarks

Google

Gemini 2.0 Flash Thinking

+11.5%
Average Score:74.8%
Meta

Llama 4 Scout

Average Score:63.3%

Performance comparison across key benchmark categories

Google

Gemini 2.0 Flash Thinking

vision
75.4%
general
+7.5%
73.8%
Meta

Llama 4 Scout

vision
+6.5%
81.9%
general
66.3%
Benchmark Scores - Detailed View
Side-by-side comparison of all benchmark scores
Knowledge Cutoff
Training data recency comparison

Gemini 2.0 Flash Thinking

2024-08-01

More recent knowledge cutoff means awareness of newer technologies and frameworks

Provider Availability & Performance

Available providers and their performance metrics

Google

Gemini 2.0 Flash Thinking

0 providers
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 2.0 Flash Thinking

+11.5%
Avg Score:74.8%
Providers:0
Meta

Llama 4 Scout

Avg Score:63.3%
Providers:6