Model Comparison

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

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.

Microsoft

Phi 4

Microsoft

Phi 4 is a language model developed by Microsoft. It achieves strong performance with an average score of 66.0% across 13 benchmarks. It excels particularly in MMLU (84.8%), HumanEval+ (82.8%), HumanEval (82.6%). The model shows particular specialization in math tasks with an average performance of 80.5%. The model is available through 1 API provider. It's licensed for commercial use, making it suitable for enterprise applications. Released in 2024, it represents Microsoft's latest advancement in AI technology.

Microsoft

Phi 4

Microsoft

2024-12-12

Meta

Llama 4 Scout

Meta

2025-04-05

3 months newer

Pricing Comparison

Cost per million tokens (USD)

Meta

Llama 4 Scout

Input:$0.08
Output:$0.30
Microsoft

Phi 4

$0.17 cheaper
Input:$0.07
Output:$0.14

Performance Metrics

Context window and performance specifications

Meta

Llama 4 Scout

Larger context
Max Context:20.0M
Parameters:109.0B
Microsoft

Phi 4

Max Context:32.0K
Parameters:14.7B

Average performance across 5 common benchmarks

Meta

Llama 4 Scout

Average Score:70.4%
Microsoft

Phi 4

+4.1%
Average Score:74.5%

Performance comparison across key benchmark categories

Meta

Llama 4 Scout

math
70.5%
code
50.3%
general
+6.1%
66.3%
Microsoft

Phi 4

math
+10.0%
80.5%
code
+25.8%
76.1%
general
60.2%
Benchmark Scores - Detailed View
Side-by-side comparison of all benchmark scores
Knowledge Cutoff
Training data recency comparison

Phi 4

2024-06-01

More recent knowledge cutoff means awareness of newer technologies and frameworks

Provider Availability & Performance

Available providers and their performance metrics

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
Microsoft

Phi 4

1 providers

DeepInfra

Throughput: 33 tok/s
Latency: 0.2ms
Meta

Llama 4 Scout

Avg Score:70.4%
Providers:6
Microsoft

Phi 4

+4.1%
Avg Score:74.5%
Providers:1