The model you pick determines your cost, quality ceiling, and vendor lock-in. There’s no single best model — only the right model for your specific use case.
The Big Two (Cloud APIs)
OpenAI GPT-4o / GPT-4.1
- Best for: general-purpose tasks, code generation, function calling, vision
- Cost: $2.50–10 per million tokens (varies by model)
- Strengths: largest ecosystem, best tool-use/function-calling, fastest iteration
- Weaknesses: closed source, rate limits, data retention policies
Anthropic Claude (Sonnet/Opus)
- Best for: long documents, nuanced analysis, safety-critical applications, coding
- Cost: $3–15 per million tokens
- Strengths: 200K context window, superior instruction following, excellent at coding and analysis
- Weaknesses: closed source, can be over-cautious for some applications
Open Source (Self-Hosted)
Meta Llama 3.1 / 3.3
- Best for: cost-sensitive deployments, data privacy requirements, fine-tuning
- Cost: hardware only (GPU rental $0.50–2/hour for 70B model)
- Strengths: truly open, fine-tunable, no API costs at scale, data stays on your servers
- Weaknesses: requires GPU infrastructure, 70B models need significant VRAM
Mistral / Mixtral
- Best for: European data compliance, efficient inference, multilingual
- Strengths: excellent quality-to-size ratio, strong multilingual support, MoE architecture is efficient
Decision Framework
- Prototype: use Claude or GPT-4o. Speed of development matters more than cost
- Production (<1000 requests/day): stay on cloud APIs. The infrastructure cost of self-hosting exceeds API costs
- Production (>10,000 requests/day): consider self-hosted open source. Break-even is usually around 5,000–10,000 daily requests
- Data-sensitive: self-hosted is the only option that guarantees data doesn’t leave your infrastructure