Choosing an LLM API for a production application is not the same decision as choosing one for experimentation. In production, what matters is reliability, cost at scale, latency for your specific use case, and the context window and output quality for your specific task type — not benchmark performance on academic datasets.
OpenAI (GPT-4o, GPT-4o mini): the default choice for most teams starting out. Strongest ecosystem, most available tooling, most developer familiarity. GPT-4o mini is the most cost-effective option for high-volume tasks that do not require frontier model performance. Anthropic (Claude 3.5 Sonnet, Claude 3 Haiku): strongest on long-context tasks, document analysis, and tasks requiring careful instruction-following. Haiku is fast and cheap; Sonnet is the best mid-tier option for most production tasks. Google (Gemini 1.5 Pro, Flash): strongest multimodal performance and the largest context window (1M tokens). Flash is cost-competitive with GPT-4o mini and Haiku.
At 1M tokens/day input + 200K tokens/day output: GPT-4o mini: ~$600-800/month. Claude 3 Haiku: ~$500-700/month. Gemini Flash: ~$400-600/month. At frontier tier: GPT-4o: ~$8,000-12,000/month. Claude 3.5 Sonnet: ~$9,000-13,000/month. Gemini 1.5 Pro: ~$7,000-11,000/month.
For streaming responses where time-to-first-token matters: GPT-4o mini and Haiku are fastest. For batch processing where throughput matters more than latency: all three are competitive.
Start with GPT-4o mini or Claude 3 Haiku for high-volume tasks. Use frontier models only where the quality difference is measurable and the cost is justified by the output value. Build your application to be model-agnostic from day one — swapping models should be a configuration change, not a rewrite.
Axented helps product teams integrate LLMs into production applications, including model selection, prompt engineering, and evaluation pipelines. → axented.com/ai-solutions
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