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-3.5-mini-instruct

Microsoft

Phi-3.5-mini-instruct is a language model developed by Microsoft. The model shows competitive results across 31 benchmarks. It excels particularly in GSM8k (86.2%), ARC-C (84.6%), RULER (84.1%). It supports a 256K token context window for handling large documents. 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-3.5-mini-instruct

Microsoft

2024-08-23

Meta

Llama 4 Scout

Meta

2025-04-05

7 months newer

Pricing Comparison

Cost per million tokens (USD)

Meta

Llama 4 Scout

Input:$0.08
Output:$0.30
Microsoft

Phi-3.5-mini-instruct

$0.18 cheaper
Input:$0.10
Output:$0.10

Performance Metrics

Context window and performance specifications

Meta

Llama 4 Scout

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

Phi-3.5-mini-instruct

Max Context:256.0K
Parameters:3.8B

Average performance across 6 common benchmarks

Meta

Llama 4 Scout

+17.8%
Average Score:70.0%
Microsoft

Phi-3.5-mini-instruct

Average Score:52.1%

Performance comparison across key benchmark categories

Meta

Llama 4 Scout

math
+9.7%
70.5%
general
+10.9%
66.3%
code
50.3%
Microsoft

Phi-3.5-mini-instruct

math
60.9%
general
55.4%
code
+15.9%
66.2%
Benchmark Scores - Detailed View
Side-by-side comparison of all benchmark scores

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-3.5-mini-instruct

1 providers

Azure

Throughput: 23 tok/s
Latency: 0.52ms
Meta

Llama 4 Scout

+17.8%
Avg Score:70.0%
Providers:6
Microsoft

Phi-3.5-mini-instruct

Avg Score:52.1%
Providers:1