Model Comparison

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

Meta

Llama 3.1 70B Instruct

Meta

Llama 3.1 70B Instruct is a language model developed by Meta. It achieves strong performance with an average score of 74.7% across 18 benchmarks. It excels particularly in GSM-8K (CoT) (95.1%), ARC-C (94.8%), API-Bank (90.0%). It supports a 256K 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 Reasoning

Microsoft

Phi 4 Reasoning is a language model developed by Microsoft. It achieves strong performance with an average score of 75.1% across 11 benchmarks. It excels particularly in FlenQA (97.7%), HumanEval+ (92.9%), IFEval (83.4%). The model shows particular specialization in code tasks with an average performance of 76.7%. 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 70B Instruct

Meta

2024-07-23

Microsoft

Phi 4 Reasoning

Microsoft

2025-04-30

9 months newer

Performance Metrics

Context window and performance specifications

Meta

Llama 3.1 70B Instruct

Larger context
Max Context:256.0K
Parameters:70.0B
Microsoft

Phi 4 Reasoning

Max Context:-
Parameters:14.0B

Average performance across 3 common benchmarks

Meta

Llama 3.1 70B Instruct

Average Score:65.2%
Microsoft

Phi 4 Reasoning

+9.3%
Average Score:74.5%

Performance comparison across key benchmark categories

Meta

Llama 3.1 70B Instruct

math
+6.7%
83.3%
code
76.3%
general
68.7%
Microsoft

Phi 4 Reasoning

math
76.6%
code
+0.4%
76.7%
general
+5.5%
74.3%
Benchmark Scores - Detailed View
Side-by-side comparison of all benchmark scores
Knowledge Cutoff
Training data recency comparison

Phi 4 Reasoning

2025-03-01

More recent knowledge cutoff means awareness of newer technologies and frameworks

Provider Availability & Performance

Available providers and their performance metrics

Meta

Llama 3.1 70B Instruct

9 providers

Sambanova

Throughput: 74 tok/s
Latency: 0.5ms

Together

Throughput: 94 tok/s
Latency: 0.5ms

Hyperbolic

Throughput: 100 tok/s
Latency: 0.5ms

DeepInfra

Throughput: 25 tok/s
Latency: 0.5ms

Fireworks

Throughput: 32 tok/s
Latency: 0.5ms

Groq

Throughput: 250 tok/s
Latency: 0.5ms

Bedrock

Throughput: 100 tok/s
Latency: 0.5ms

Lambda

Throughput: 42 tok/s
Latency: 0.5ms

Cerebras

Throughput: 1204 tok/s
Latency: 0.2ms
Microsoft

Phi 4 Reasoning

0 providers
Meta

Llama 3.1 70B Instruct

Avg Score:65.2%
Providers:9
Microsoft

Phi 4 Reasoning

+9.3%
Avg Score:74.5%
Providers:0