Model Comparison

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

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.

Alibaba

QwQ-32B-Preview

Alibaba

QwQ-32B-Preview is a language model developed by Alibaba. It achieves strong performance with an average score of 64.0% across 4 benchmarks. It excels particularly in MATH-500 (90.6%), GPQA (65.2%), AIME 2024 (50.0%). The model is available through 4 API providers. It's licensed for commercial use, making it suitable for enterprise applications. Released in 2024, it represents Alibaba's latest advancement in AI technology.

Alibaba

QwQ-32B-Preview

Alibaba

2024-11-28

Microsoft

Phi 4

Microsoft

2024-12-12

14 days newer

Pricing Comparison

Cost per million tokens (USD)

Microsoft

Phi 4

$0.14 cheaper
Input:$0.07
Output:$0.14
Alibaba

QwQ-32B-Preview

Input:$0.15
Output:$0.20

Performance Metrics

Context window and performance specifications

Microsoft

Phi 4

Max Context:32.0K
Parameters:14.7B
Alibaba

QwQ-32B-Preview

Larger context
Max Context:65.5K
Parameters:32.5B

Average performance across 1 common benchmarks

Microsoft

Phi 4

Average Score:56.1%
Alibaba

QwQ-32B-Preview

+9.1%
Average Score:65.2%

Performance comparison across key benchmark categories

Microsoft

Phi 4

math
80.5%
code
+26.1%
76.1%
general
+2.6%
60.2%
Alibaba

QwQ-32B-Preview

math
+10.1%
90.6%
code
50.0%
general
57.6%
Benchmark Scores - Detailed View
Side-by-side comparison of all benchmark scores
Knowledge Cutoff
Training data recency comparison

Phi 4

2024-06-01

QwQ-32B-Preview

2024-11-28

More recent knowledge cutoff means awareness of newer technologies and frameworks

Provider Availability & Performance

Available providers and their performance metrics

Microsoft

Phi 4

1 providers

DeepInfra

Throughput: 33 tok/s
Latency: 0.2ms
Alibaba

QwQ-32B-Preview

4 providers

Together

Throughput: 62.14 tok/s
Latency: 0.74ms

Hyperbolic

Throughput: 31.9 tok/s
Latency: 1.05ms

DeepInfra

Throughput: 76.04 tok/s
Latency: 0.44ms

Fireworks

Throughput: 99.15 tok/s
Latency: 0.53ms
Microsoft

Phi 4

Avg Score:56.1%
Providers:1
Alibaba

QwQ-32B-Preview

+9.1%
Avg Score:65.2%
Providers:4