LLM API providers compared: OpenAI vs Anthropic vs Google (2026)
All three major providers offer strong general models, tool calling, structured outputs, long context, multimodal input, prompt caching and batch processing. The differences that decide real projects are quality on your own tasks, behaviour with your tools and schemas, data terms and regions, rate limits, and price at your volume, and these change with every model release. Put a gateway in front so you can switch or route between them.
This page doesn't list model names and prices, because they change every few months, so check each provider's current documentation.
What to compare
| Criterion | What to check |
|---|---|
| Quality | Pass rate on your eval set |
| Tool use | Valid arguments and correct tool choice on your tools |
| Structured output | Schema enforcement support |
| Context and caching | Window size, caching rules and discounts |
| Data terms | Training use, retention, zero-retention options |
| Regions and clouds | Availability through your cloud provider (Azure, AWS, Google Cloud) |
| Limits | Rate limits at your tier |
| Price | Cost per successful task at your volume |
| Deprecations | How long old versions stay available |
Running the comparison
1 - Take 100 real cases from your product, with pass criteria. 2 - Run each provider's candidate models with the same prompts, adjusted only where a provider's format requires it. 3 - Score with your evals, and read the failures. 4 - Compute cost per successful task, including retries. 5 - Keep at least two providers working behind your gateway for fallback.
Why public leaderboards aren't enough
A model that leads a public benchmark can be worse on your task, your language or your tool schemas. Benchmarks also leak into training data over time, so your own cases are the test that counts. See benchmarks vs product evals.
Availability through cloud providers
Enterprise teams often reach these models through their cloud: OpenAI models through Azure, Anthropic models through AWS Bedrock and Google Cloud Vertex AI, and Gemini through Google Cloud. That can settle data terms, billing and regions faster than a new vendor contract.
From the floor
In the inference pipeline lecture, I showed the gateway as the entry point to every model call, with LiteLLM as one option, because once everything goes through a gateway, switching or routing between providers is a configuration change instead of a rewrite.