Model Comparison

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

Mistral AI

Mistral Large 2

Mistral AI

Mistral Large 2 is a language model developed by Mistral AI. This model demonstrates exceptional performance with an average score of 87.6% across 5 benchmarks. It excels particularly in GSM8k (93.0%), HumanEval (92.0%), MT-Bench (86.3%). It supports a 256K token context window for handling large documents. The model is available through 2 API providers. Released in 2024, it represents Mistral AI'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.

Mistral AI

Mistral Large 2

Mistral AI

2024-07-24

Microsoft

Phi 4

Microsoft

2024-12-12

4 months newer

Pricing Comparison

Cost per million tokens (USD)

Mistral AI

Mistral Large 2

Input:$2.00
Output:$6.00
Microsoft

Phi 4

$7.79 cheaper
Input:$0.07
Output:$0.14

Performance Metrics

Context window and performance specifications

Mistral AI

Mistral Large 2

Larger context
Max Context:256.0K
Parameters:123.0B
Microsoft

Phi 4

Max Context:32.0K
Parameters:14.7B

Average performance across 2 common benchmarks

Mistral AI

Mistral Large 2

+4.3%
Average Score:88.0%
Microsoft

Phi 4

Average Score:83.7%

Performance comparison across key benchmark categories

Mistral AI

Mistral Large 2

math
+12.5%
93.0%
code
+15.9%
92.0%
roleplay
+38.7%
86.3%
general
+23.2%
83.4%
Microsoft

Phi 4

math
80.5%
code
76.1%
roleplay
47.6%
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

Mistral AI

Mistral Large 2

2 providers

Google

Throughput: 42 tok/s
Latency: 0.4ms

Mistral AI

Throughput: 0.1 tok/s
Latency: 0.5ms
Microsoft

Phi 4

1 providers

DeepInfra

Throughput: 33 tok/s
Latency: 0.2ms
Mistral AI

Mistral Large 2

+4.3%
Avg Score:88.0%
Providers:2
Microsoft

Phi 4

Avg Score:83.7%
Providers:1