The model is one layer of a chat system
A large language model generates or transforms language from the context it receives. In an application, the model usually sits behind a chat API that handles requests, output formatting, tool calls and streaming. Different model families expose different interfaces and operational features.
Model families to consider
| Provider | API interface to review | Practical evaluation focus |
|---|---|---|
| OpenAI | Responses API | Multi-step responses, tool integration, structured output |
| Anthropic | Messages API | Instruction following, long-form tasks, tool-use workflows |
| Gemini API | Multimodal input, long-context workloads, function calling | |
| Mistral | Chat Completions | General chat, structured responses, model and deployment choices |
| Cohere | Chat API | Retrieval-grounded responses, citations and enterprise knowledge workflows |
| OpenRouter | Multi-provider API | Comparing models and routing across providers through a common interface |
These are categories to evaluate, not objective rankings. Product features and model availability change; confirm current details with each provider's official documentation.
Capability checklist
Context handling: How much relevant conversation and retrieved material can the system use effectively? A long published context window is not a substitute for testing recall and focus.
Latency: Evaluate first-token time for streaming experiences as well as end-to-end completion time.
Structured output: Validate actual JSON/schema adherence, not just whether the provider advertises JSON support.
Tool calls: Assess the model's decision to call a tool and its argument quality. Your application should still validate every call.
Cost: Measure input tokens, output tokens, caching behavior and retries on real tasks.
Do not assume a single model fits every chat
A concise FAQ bot may prefer speed and price. A research agent may require stronger synthesis and robust tool use. A multilingual assistant may perform differently across languages. Routing different requests to different models can be useful, but adds operational complexity.
Read how to choose a chat model or the AI chat API overview.
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