Last updated 2026-05-13. Pricing, model names, and provider policies change frequently.
Quick answer
Choose an LLM API by starting from the job your product needs done, then testing model quality, latency, cost, capability support, operational reliability, and vendor transparency. The best provider for a coding agent may not be the best provider for RAG, extraction, or a low-cost chatbot.
Use this guide when
You already know the product workflow
This guide is strongest when you know whether you are building chat, coding help, RAG, extraction, or agents and now need to turn that into provider criteria.
You are overwhelmed by provider lists
Use this article when broad directories and best lists feel noisy. It helps you narrow the market by decision lens instead of by brand popularity.
You need a shortlist you can defend internally
It is useful before explaining the stack choice to product, finance, or security because it structures the decision around measurable tradeoffs.
Start with the workflow, not the brand
Define whether your app needs conversation, coding, retrieval, extraction, summarization, tool use, or multimodal input. A focused use case turns provider selection from a vague ranking exercise into a measurable evaluation.
Build a small eval before scaling traffic
Collect 30 to 100 realistic examples from your product. Compare correctness, refusal behavior, format stability, latency, and cost. Your own eval set is more useful than generic benchmark claims.
Design for change
Model catalogs, prices, and rate limits change often. Keep provider-specific code behind a small adapter, log model/version decisions, and avoid hard-coding assumptions across your application.
Example decision paths
Coding agent for a startup team
A coding assistant often starts with OpenAI or Anthropic as the quality baseline, then tests a cheaper or faster fallback only after tool reliability and output format stability are acceptable.
RAG support bot with cost pressure
A retrieval-heavy support workflow may compare DeepSeek-V4, Gemini, Cohere, and an open-model inference route because context length, embeddings, reranking, and ongoing token cost all matter differently.
Voice or real-time UX
A real-time app may shortlist Groq earlier than a broad benchmark ranking would suggest, because responsiveness is part of the product itself.
Provider examples to compare
| Provider | Category | Supported models | OpenAI-compatible | Starting price | Context | Tool calling | Vision | Streaming | Status | Trust | Links |
|---|---|---|---|---|---|---|---|---|---|---|---|
| OpenAI | Official APIs | GPT, reasoning models, embeddings, image | Yes | Budget to premium GPT tiers | Short to very long, model based | Yes | Yes | Yes | Available | 12/15 | |
| Anthropic | Official APIs | Claude, Claude Haiku, Claude Sonnet, Claude Opus | No | Mid to premium Claude tiers | Long context options | Yes | Yes | Yes | Available | 10/15 | |
| DeepSeek | Official APIs | DeepSeek-V4-Flash, DeepSeek-V4-Pro | Yes | Low-cost flash to discounted pro tiers | 1M context, up to 384K output | Yes | No | Yes | Available | 11/15 | |
| Google Gemini | Official APIs | Gemini, embedding models, multimodal models | Yes | Low-cost flash to premium tiers | Short to million-token-class options | Yes | Yes | Yes | Available | 11/15 | |
| Cohere | Official APIs | Command, Embed, Rerank | No | Enterprise and task-specific tiers | Task and model based | Yes | No | Yes | Available | 10/15 | |
| Portkey | LLM API Aggregators | GPT, Claude, Gemini, DeepSeek-V4 | Yes | Plan dependent plus provider spend | Provider dependent | No | Yes | Yes | Available | 11/15 |
Compare next
Checklist
- Define required capabilities: tools, JSON, streaming, vision, embeddings, long context, or audio.
- Estimate monthly token cost with realistic input and output sizes.
- Check status page, support channel, rate limits, and billing clarity.
- Test at least one fallback or alternative provider before launch.
Recommended next step
Use the endpoint finder to turn your use case and priorities into a provider shortlist.
FAQ
How many providers should I test?
For an initial rollout, test two or three serious candidates. More than that can slow decisions unless you have a clear eval pipeline.
Should startups use an aggregator first?
Aggregators can be useful for experimentation and fallback, but production teams should understand the extra dependency and data path.
What matters more: price or quality?
The answer depends on the task. For extraction and routing, cheaper models may work well. For coding, agents, and high-value user flows, quality failures can cost more than tokens.