Model Comparison

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

Google

Gemma 3 27B

Google

Gemma 3 27B is a multimodal language model developed by Google. It achieves strong performance with an average score of 65.4% across 26 benchmarks. It excels particularly in GSM8k (95.9%), IFEval (90.4%), MATH (89.0%). The model shows particular specialization in math tasks with an average performance of 78.2%. It supports a 262K token context window for handling large documents. The model is available through 2 API providers. 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 Google'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.

Microsoft

Phi 4

Microsoft

2024-12-12

Google

Gemma 3 27B

Google

2025-03-12

3 months newer

Pricing Comparison

Cost per million tokens (USD)

Google

Gemma 3 27B

Input:$0.10
Output:$0.20
Microsoft

Phi 4

$0.09 cheaper
Input:$0.07
Output:$0.14

Performance Metrics

Context window and performance specifications

Google

Gemma 3 27B

Larger context
Max Context:262.1K
Parameters:27.0B
Microsoft

Phi 4

Max Context:32.0K
Parameters:14.7B

Average performance across 6 common benchmarks

Google

Gemma 3 27B

+5.3%
Average Score:64.5%
Microsoft

Phi 4

Average Score:59.2%

Performance comparison across key benchmark categories

Google

Gemma 3 27B

math
78.2%
code
73.4%
general
53.5%
Microsoft

Phi 4

math
+2.3%
80.5%
code
+2.8%
76.1%
general
+6.7%
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

Google

Gemma 3 27B

2 providers

DeepInfra

Throughput: 33 tok/s
Latency: 0.2ms

Novita

Throughput: 33 tok/s
Latency: 0.2ms
Microsoft

Phi 4

1 providers

DeepInfra

Throughput: 33 tok/s
Latency: 0.2ms
Google

Gemma 3 27B

+5.3%
Avg Score:64.5%
Providers:2
Microsoft

Phi 4

Avg Score:59.2%
Providers:1