Model Comparison

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

Google

Gemini 2.0 Flash-Lite

Google

Gemini 2.0 Flash-Lite is a multimodal language model developed by Google. The model shows competitive results across 13 benchmarks. It excels particularly in MATH (86.8%), FACTS Grounding (83.6%), Global-MMLU-Lite (78.2%). 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 2.0 Flash-Lite

Google

2025-02-05

5 months newer

Pricing Comparison

Cost per million tokens (USD)

Google

Gemini 2.0 Flash-Lite

Input:$0.07
Output:$0.30
Microsoft

Phi-3.5-mini-instruct

$0.17 cheaper
Input:$0.10
Output:$0.10

Performance Metrics

Context window and performance specifications

Google

Gemini 2.0 Flash-Lite

Larger context
Max Context:1.1M
Microsoft

Phi-3.5-mini-instruct

Max Context:256.0K
Parameters:3.8B

Average performance across 3 common benchmarks

Google

Gemini 2.0 Flash-Lite

+27.9%
Average Score:70.0%
Microsoft

Phi-3.5-mini-instruct

Average Score:42.1%

Performance comparison across key benchmark categories

Google

Gemini 2.0 Flash-Lite

factuality
+19.6%
83.6%
math
+10.2%
71.0%
code
28.9%
general
+0.1%
55.5%
Microsoft

Phi-3.5-mini-instruct

factuality
64.0%
math
60.9%
code
+37.3%
66.2%
general
55.4%
Benchmark Scores - Detailed View
Side-by-side comparison of all benchmark scores
Knowledge Cutoff
Training data recency comparison

Gemini 2.0 Flash-Lite

2024-06-01

More recent knowledge cutoff means awareness of newer technologies and frameworks

Provider Availability & Performance

Available providers and their performance metrics

Google

Gemini 2.0 Flash-Lite

1 providers

Google

Throughput: 85 tok/s
Latency: 0.7ms
Microsoft

Phi-3.5-mini-instruct

1 providers

Azure

Throughput: 23 tok/s
Latency: 0.52ms
Google

Gemini 2.0 Flash-Lite

+27.9%
Avg Score:70.0%
Providers:1
Microsoft

Phi-3.5-mini-instruct

Avg Score:42.1%
Providers:1