Model Comparison

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

Meta

Llama 3.1 8B Instruct

Meta

Llama 3.1 8B Instruct is a language model developed by Meta. It achieves strong performance with an average score of 61.3% across 18 benchmarks. It excels particularly in GSM-8K (CoT) (84.5%), ARC-C (83.4%), API-Bank (82.6%). It supports a 262K token context window for handling large documents. The model is available through 9 API providers. Released in 2024, it represents Meta's latest advancement in AI technology.

Microsoft

Phi-4-multimodal-instruct

Microsoft

Phi-4-multimodal-instruct is a multimodal language model developed by Microsoft. It achieves strong performance with an average score of 72.0% across 15 benchmarks. It excels particularly in ScienceQA Visual (97.5%), DocVQA (93.2%), MMBench (86.7%). The model shows particular specialization in general tasks with an average performance of 75.8%. It supports a 256K token context window for handling large documents. The model is available through 1 API provider. As a multimodal model, it can process and understand text, images, and other input formats seamlessly. It's licensed for commercial use, making it suitable for enterprise applications. Released in 2025, it represents Microsoft's latest advancement in AI technology.

Meta

Llama 3.1 8B Instruct

Meta

2024-07-23

Microsoft

Phi-4-multimodal-instruct

Microsoft

2025-02-01

6 months newer

Pricing Comparison

Cost per million tokens (USD)

Meta

Llama 3.1 8B Instruct

$0.09 cheaper
Input:$0.03
Output:$0.03
Microsoft

Phi-4-multimodal-instruct

Input:$0.05
Output:$0.10

Performance Metrics

Context window and performance specifications

Meta

Llama 3.1 8B Instruct

Larger context
Max Context:262.1K
Parameters:8.0B
Microsoft

Phi-4-multimodal-instruct

Max Context:256.0K
Parameters:5.6B

Performance comparison across key benchmark categories

Meta

Llama 3.1 8B Instruct

general
54.0%
math
+6.0%
68.4%
Microsoft

Phi-4-multimodal-instruct

general
+21.8%
75.8%
math
62.4%
Knowledge Cutoff
Training data recency comparison

Llama 3.1 8B Instruct

2023-12-31

Phi-4-multimodal-instruct

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 3.1 8B Instruct

9 providers

Sambanova

Throughput: 1050 tok/s
Latency: 0.5ms

Together

Throughput: 194 tok/s
Latency: 0.5ms

Hyperbolic

Throughput: 200 tok/s
Latency: 0.5ms

DeepInfra

Throughput: 118 tok/s
Latency: 0.5ms

Fireworks

Throughput: 292 tok/s
Latency: 0.5ms

Groq

Throughput: 750 tok/s
Latency: 0.5ms

Bedrock

Throughput: 100 tok/s
Latency: 0.5ms

Lambda

Throughput: 42 tok/s
Latency: 0.5ms

Cerebras

Throughput: 2047 tok/s
Latency: 0.2ms
Microsoft

Phi-4-multimodal-instruct

1 providers

DeepInfra

Throughput: 25 tok/s
Latency: 0.5ms
Meta

Llama 3.1 8B Instruct

Avg Score:0.0%
Providers:9
Microsoft

Phi-4-multimodal-instruct

Avg Score:0.0%
Providers:1