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Gemini 2.0 Flash Thinking vs Phi-3.5-mini-instruct
Comprehensive side-by-side LLM comparison
Phi-3.5-mini-instruct leads with 48.4% higher average benchmark score. Gemini 2.0 Flash Thinking supports multimodal inputs. Overall, Phi-3.5-mini-instruct is the stronger choice for coding tasks.
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Gemini 2.0 Flash Thinking is a multimodal language model developed by Google. It achieves strong performance with an average score of 74.3% across 3 benchmarks. Notable strengths include MMMU (75.4%), GPQA (74.2%), AIME 2024 (73.3%). 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.
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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.
5 months newer
Phi-3.5-mini-instruct
Microsoft
2024-08-23
Gemini 2.0 Flash Thinking
2025-01-21
Performance Metrics
Context window and performance specifications
Average performance across 33 common benchmarks
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Gemini 2.0 Flash Thinking
Average Score:6.8%
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Phi-3.5-mini-instruct
Average Score:55.2%(+48.4%)
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Knowledge Cutoff
Training data recency comparison
Gemini 2.0 Flash Thinking
2024-08-01
More recent knowledge cutoff means awareness of newer technologies and frameworks
Provider Availability & Performance
Available providers and their performance metrics
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Gemini 2.0 Flash Thinking
0 providers
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Phi-3.5-mini-instruct
1 providers
Azure
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Gemini 2.0 Flash Thinking
Avg Score:6.8%
Providers:0
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Phi-3.5-mini-instruct
Avg Score:55.2%(+48.4%)
Providers:1