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-mini-instruct

Microsoft

Phi-3.5-mini-instruct is a language model developed by Microsoft. The model shows competitive results across 31 benchmarks. It excels particularly in GSM8k (86.2%), ARC-C (84.6%), RULER (84.1%). It supports a 256K token context window for handling large documents. 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-3.5-mini-instruct

Microsoft

2024-08-23

Google

Gemini 2.5 Flash-Lite

Google

2025-06-17

9 months newer

Pricing Comparison

Cost per million tokens (USD)

Google

Gemini 2.5 Flash-Lite

Input:$0.10
Output:$0.40
Microsoft

Phi-3.5-mini-instruct

$0.30 cheaper
Input:$0.10
Output:$0.10

Performance Metrics

Context window and performance specifications

Google

Gemini 2.5 Flash-Lite

Larger context
Max Context:1.1M
Microsoft

Phi-3.5-mini-instruct

Max Context:256.0K
Parameters:3.8B

Average performance across 1 common benchmarks

Google

Gemini 2.5 Flash-Lite

+34.2%
Average Score:64.6%
Microsoft

Phi-3.5-mini-instruct

Average Score:30.4%

Performance comparison across key benchmark categories

Google

Gemini 2.5 Flash-Lite

factuality
+20.1%
84.1%
reasoning
2.5%
code
42.5%
general
35.8%
Microsoft

Phi-3.5-mini-instruct

factuality
64.0%
reasoning
+71.7%
74.2%
code
+23.7%
66.2%
general
+19.6%
55.4%
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-mini-instruct

1 providers

Azure

Throughput: 23 tok/s
Latency: 0.52ms
Google

Gemini 2.5 Flash-Lite

+34.2%
Avg Score:64.6%
Providers:1
Microsoft

Phi-3.5-mini-instruct

Avg Score:30.4%
Providers:1