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-3.5-MoE-instruct

Microsoft

Phi-3.5-MoE-instruct is a language model developed by Microsoft. It achieves strong performance with an average score of 65.6% across 31 benchmarks. It excels particularly in ARC-C (91.0%), OpenBookQA (89.6%), GSM8k (88.7%). The model shows particular specialization in reasoning tasks with an average performance of 85.4%. 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-3.5-MoE-instruct

Microsoft

2024-08-23

Google

Gemini 2.5 Flash-Lite

Google

2025-06-17

9 months newer

Performance Metrics

Context window and performance specifications

Google

Gemini 2.5 Flash-Lite

Larger context
Max Context:1.1M
Microsoft

Phi-3.5-MoE-instruct

Max Context:-
Parameters:60.0B

Average performance across 1 common benchmarks

Google

Gemini 2.5 Flash-Lite

+27.8%
Average Score:64.6%
Microsoft

Phi-3.5-MoE-instruct

Average Score:36.8%

Performance comparison across key benchmark categories

Google

Gemini 2.5 Flash-Lite

reasoning
2.5%
factuality
+6.6%
84.1%
code
42.5%
general
35.8%
Microsoft

Phi-3.5-MoE-instruct

reasoning
+82.9%
85.4%
factuality
77.5%
code
+33.2%
75.8%
general
+25.1%
60.9%
Benchmark Scores - Detailed View
Side-by-side comparison of all benchmark scores
Knowledge Cutoff
Training data recency comparison

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-3.5-MoE-instruct

0 providers
Google

Gemini 2.5 Flash-Lite

+27.8%
Avg Score:64.6%
Providers:1
Microsoft

Phi-3.5-MoE-instruct

Avg Score:36.8%
Providers:0