AI Models
Explore the leading LLMs on performance benchmarks, latency, and pricing.
Score (%)
Model
Cost
Feb/5/2026
claude-4-6-opus
1st
Overall Rank
95%
Overall Score
1st
Overall Rank
95%
Overall Score
Context Window200k
Benchmark Performance
AIMultiple
Rank
Category
Benchmarks
Score
FAQ
Consider your primary needs:
Content creation: Focus on AI reasoning and memory scores
Software development: Prioritize AI code performance
Data analysis: Look at text-to-sqlL and AI finance scores
Business automation: Consider Agentic RAG and AI Agents Performance
Factual accuracy: Emphasize low hallucination rates
Content creation: Focus on AI reasoning and memory scores
Software development: Prioritize AI code performance
Data analysis: Look at text-to-sqlL and AI finance scores
Business automation: Consider Agentic RAG and AI Agents Performance
Factual accuracy: Emphasize low hallucination rates
These represent different tiers of OpenAI's GPT-5 family:
GPT-5: Full-featured flagship model
GPT-5 Mini: Optimized for speed and cost while maintaining strong performance
GPT-5 Nano: Ultra-fast, lightweight version for high-volume applications
GPT-5: Full-featured flagship model
GPT-5 Mini: Optimized for speed and cost while maintaining strong performance
GPT-5 Nano: Ultra-fast, lightweight version for high-volume applications
Date suffixes indicate specific training cutoffs or release versions. For example, "claude-3-7-sonnet-20250219" was released on February 19, 2025, helping users track which exact version they're evaluating.
The "32b" in models like "exaone-4.0-32b" refers to 32 billion parameters. Generally, more parameters allow for better performance, but also require more computational resources and cost more to run.
Mini variants: Optimized for speed and cost, typically 65-80% the performance of full models
High variants: Maximum performance configurations, often with increased computational requirements
High variants: Maximum performance configurations, often with increased computational requirements