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Gemini 1.0 Pro vs Phi-3.5-mini-instruct
Comprehensive side-by-side LLM comparison
Phi-3.5-mini-instruct leads with 37.4% higher average benchmark score. Phi-3.5-mini-instruct offers 215.0K more tokens in context window than Gemini 1.0 Pro. Phi-3.5-mini-instruct is $1.80 cheaper per million tokens. Overall, Phi-3.5-mini-instruct is the stronger choice for coding tasks.
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Gemini 1.0 Pro is a language model developed by Google. The model shows competitive results across 9 benchmarks. Notable strengths include BIG-Bench (75.0%), MMLU (71.8%), WMT23 (71.7%). The model is available through 1 API provider. Released in 2024, 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.
6 months newer
Gemini 1.0 Pro
2024-02-15
Phi-3.5-mini-instruct
Microsoft
2024-08-23
Pricing Comparison
Cost per million tokens (USD)
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Gemini 1.0 Pro
Input:$0.50
Output:$1.50
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Phi-3.5-mini-instruct
Input:$0.10
Output:$0.10($1.80 cheaper)
Performance Metrics
Context window and performance specifications
Average performance across 37 common benchmarks
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Gemini 1.0 Pro
Average Score:11.8%
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Phi-3.5-mini-instruct
Average Score:49.2%(+37.4%)
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Knowledge Cutoff
Training data recency comparison
Gemini 1.0 Pro
2024-02-01
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 1.0 Pro
1 providers
Throughput: 120 tok/s
Latency: 0.4ms
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Phi-3.5-mini-instruct
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Gemini 1.0 Pro
Avg Score:11.8%
Providers:1
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Phi-3.5-mini-instruct
Avg Score:49.2%(+37.4%)
Providers:1