AI Model Recommender
Find the perfect AI model for your use case. Compare 12+ models across providers based on capabilities, cost, and your specific requirements.
1. Select Your Use Case
2. Set Your Priority
3. Estimate Volume
GPT-4o Mini
OpenAI
Extremely cost-effective for simpler tasks while maintaining good quality.
Gemini 2.0 Flash
Incredible value with 1M token context. Best for budget-conscious use cases.
All Models Ranked
Based on your use case and priorities
GPT-4o Mini
OpenAIExtremely cost-effective for simpler tasks while maintaining good quality.
Gemini 2.0 Flash
GoogleIncredible value with 1M token context. Best for budget-conscious use cases.
DeepSeek V3
DeepSeekExceptional coding capabilities at very low cost. Strong open-source alternative.
Claude 3.5 Haiku
AnthropicFast and affordable with surprisingly good capabilities for its price.
Mistral Large
MistralStrong European alternative with excellent multilingual support.
o3-mini
OpenAIStrong reasoning capabilities at a fraction of o1's cost.
Claude Sonnet 4
AnthropicExcellent balance of capability and cost. Great for most production use cases.
Gemini 1.5 Pro
Google2M token context window. Ideal for analyzing entire codebases or long videos.
GPT-4o
OpenAIOpenAI's flagship model with excellent all-around capabilities and vision support.
Grok-2
xAIReal-time knowledge from X/Twitter. Good for current events and casual tone.
Claude Opus 4
AnthropicAnthropic's most capable model. Exceptional at nuanced tasks and long context.
o1
OpenAIBest-in-class reasoning with chain-of-thought. Ideal for hard problems.
Track Your Actual Model Costs
Burnwise monitors which models you use and shows exactly what each costs.
How to Choose an AI Model
What factors matter most?
Consider your use case complexity, required speed, budget, and whether you need multimodal capabilities (images, audio). Simple tasks rarely need expensive models.
Should I use multiple models?
Yes! Smart model routing can cut costs by 30-45%. Use cheaper models for simple queries and reserve premium models for complex tasks.
What about fine-tuned models?
Fine-tuned models can outperform larger base models on specific tasks at lower cost. Consider fine-tuning if you have consistent, domain-specific needs.
How accurate are these recommendations?
These rankings are based on benchmark data and real-world usage patterns. However, performance varies by task. We recommend testing 2-3 models for your specific use case.