Model Comparison

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

Google

Gemini 2.5 Flash-Lite

Google

Gemini 2.5 Flash-Lite is a multimodal language model developed by Google. The model shows competitive results across 13 benchmarks. It excels particularly in FACTS Grounding (84.1%), Global-MMLU-Lite (81.1%), MMMU (72.9%). With a 1.1M token context window, it can handle extensive documents and complex multi-turn conversations. The model is available through 1 API provider. As a multimodal model, it can process and understand text, images, and other input formats seamlessly. 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

Gemini 2.5 Flash-Lite

Google

2025-06-17

6 months newer

Pricing Comparison

Cost per million tokens (USD)

Google

Gemini 2.5 Flash-Lite

Input:$0.10
Output:$0.40
Microsoft

Phi 4

$0.29 cheaper
Input:$0.07
Output:$0.14

Performance Metrics

Context window and performance specifications

Google

Gemini 2.5 Flash-Lite

Larger context
Max Context:1.1M
Microsoft

Phi 4

Max Context:32.0K
Parameters:14.7B

Average performance across 2 common benchmarks

Google

Gemini 2.5 Flash-Lite

+8.1%
Average Score:37.6%
Microsoft

Phi 4

Average Score:29.6%

Performance comparison across key benchmark categories

Google

Gemini 2.5 Flash-Lite

code
42.5%
general
35.8%
Microsoft

Phi 4

code
+33.6%
76.1%
general
+24.4%
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

Gemini 2.5 Flash-Lite

2025-01-01

More recent knowledge cutoff means awareness of newer technologies and frameworks

Provider Availability & Performance

Available providers and their performance metrics

Google

Gemini 2.5 Flash-Lite

1 providers

Google

Throughput: 5.69 tok/s
Latency: 0.44ms
Microsoft

Phi 4

1 providers

DeepInfra

Throughput: 33 tok/s
Latency: 0.2ms
Google

Gemini 2.5 Flash-Lite

+8.1%
Avg Score:37.6%
Providers:1
Microsoft

Phi 4

Avg Score:29.6%
Providers:1