Model Comparison

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

Google

Gemini 2.5 Pro Preview 06-05

Google

Gemini 2.5 Pro Preview 06-05 is a multimodal language model developed by Google. It achieves strong performance with an average score of 68.8% across 13 benchmarks. It excels particularly in Global-MMLU-Lite (89.2%), AIME 2025 (88.0%), FACTS Grounding (87.8%). 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 Pro Preview 06-05

Google

2025-06-05

9 months newer

Performance Metrics

Context window and performance specifications

Google

Gemini 2.5 Pro Preview 06-05

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 Pro Preview 06-05

+49.6%
Average Score:86.4%
Microsoft

Phi-3.5-MoE-instruct

Average Score:36.8%

Performance comparison across key benchmark categories

Google

Gemini 2.5 Pro Preview 06-05

factuality
+10.3%
87.8%
code
68.1%
general
+8.9%
69.8%
Microsoft

Phi-3.5-MoE-instruct

factuality
77.5%
code
+7.6%
75.8%
general
60.9%
Benchmark Scores - Detailed View
Side-by-side comparison of all benchmark scores
Knowledge Cutoff
Training data recency comparison

Gemini 2.5 Pro Preview 06-05

2025-01-31

More recent knowledge cutoff means awareness of newer technologies and frameworks

Provider Availability & Performance

Available providers and their performance metrics

Google

Gemini 2.5 Pro Preview 06-05

1 providers

Google

Throughput: 85 tok/s
Latency: 0.7ms
Microsoft

Phi-3.5-MoE-instruct

0 providers
Google

Gemini 2.5 Pro Preview 06-05

+49.6%
Avg Score:86.4%
Providers:1
Microsoft

Phi-3.5-MoE-instruct

Avg Score:36.8%
Providers:0