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-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.

Meta

Llama 3.1 8B Instruct

Meta

2024-07-23

Microsoft

Phi-3.5-mini-instruct

Microsoft

2024-08-23

1 month newer

Pricing Comparison

Cost per million tokens (USD)

Meta

Llama 3.1 8B Instruct

$0.14 cheaper
Input:$0.03
Output:$0.03
Microsoft

Phi-3.5-mini-instruct

Input:$0.10
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-3.5-mini-instruct

Max Context:256.0K
Parameters:3.8B

Average performance across 5 common benchmarks

Meta

Llama 3.1 8B Instruct

+2.0%
Average Score:60.8%
Microsoft

Phi-3.5-mini-instruct

Average Score:58.8%

Performance comparison across key benchmark categories

Meta

Llama 3.1 8B Instruct

reasoning
+9.2%
83.4%
math
+7.6%
68.4%
code
65.8%
general
54.0%
Microsoft

Phi-3.5-mini-instruct

reasoning
74.2%
math
60.9%
code
+0.4%
66.2%
general
+1.4%
55.4%
Benchmark Scores - Detailed View
Side-by-side comparison of all benchmark scores
Knowledge Cutoff
Training data recency comparison

Llama 3.1 8B Instruct

2023-12-31

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-3.5-mini-instruct

1 providers

Azure

Throughput: 23 tok/s
Latency: 0.52ms
Meta

Llama 3.1 8B Instruct

+2.0%
Avg Score:60.8%
Providers:9
Microsoft

Phi-3.5-mini-instruct

Avg Score:58.8%
Providers:1