Model Comparison

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

Meta

Llama 4 Maverick

Meta

Llama 4 Maverick is a multimodal language model developed by Meta. It achieves strong performance with an average score of 71.8% across 13 benchmarks. It excels particularly in DocVQA (94.4%), MGSM (92.3%), ChartQA (90.0%). The model shows particular specialization in vision tasks with an average performance of 75.8%. With a 2.0M token context window, it can handle extensive documents and complex multi-turn conversations. The model is available through 7 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 Maverick

Meta

2025-04-05

3 months newer

Pricing Comparison

Cost per million tokens (USD)

Meta

Llama 4 Maverick

Input:$0.17
Output:$0.60
Microsoft

Phi 4

$0.56 cheaper
Input:$0.07
Output:$0.14

Performance Metrics

Context window and performance specifications

Meta

Llama 4 Maverick

Larger context
Max Context:2.0M
Parameters:400.0B
Microsoft

Phi 4

Max Context:32.0K
Parameters:14.7B

Average performance across 5 common benchmarks

Meta

Llama 4 Maverick

+3.4%
Average Score:77.9%
Microsoft

Phi 4

Average Score:74.5%

Performance comparison across key benchmark categories

Meta

Llama 4 Maverick

math
75.7%
code
60.5%
general
+11.3%
71.5%
Microsoft

Phi 4

math
+4.8%
80.5%
code
+15.6%
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 Maverick

7 providers

Sambanova

Throughput: 638.7 tok/s
Latency: 2.04ms

Together

Throughput: 97.93 tok/s
Latency: 0.2ms

DeepInfra

Throughput: 83.59 tok/s
Latency: 0.38ms

Fireworks

Throughput: 63.03 tok/s
Latency: 0.62ms

Groq

Throughput: 307.3 tok/s
Latency: 0.27ms

Novita

Throughput: 69.42 tok/s
Latency: 0.62ms

Lambda

Throughput: 93.69 tok/s
Latency: 0.65ms
Microsoft

Phi 4

1 providers

DeepInfra

Throughput: 33 tok/s
Latency: 0.2ms
Meta

Llama 4 Maverick

+3.4%
Avg Score:77.9%
Providers:7
Microsoft

Phi 4

Avg Score:74.5%
Providers:1