Match each structured-output toolkit to how it enforces the schema

Structured-output toolkits all promise the same thing: you get back JSON that matches a shape you defined. How they keep that promise splits into three camps. The **server-side constrained decoding** camp lives at the provider. OpenAI Structured Outputs in strict JSON Schema mode and Outlines for self-hosted models both mask the sampler's logits so the model literally cannot emit a token that would leave the grammar. The output is schema-valid by construction. The **server-side bias** camp also lives at the provider but is softer. Google Gemini responseSchema and Anthropic tool use input_schema bias the model's distribution toward the schema and validate at the boundary. Most outputs match; the rare miss is caught by the SDK. The **client-side validate-retry** camp lives in your application code. Instructor for Python and the Vercel AI SDK generateObject for TypeScript take a Pydantic or Zod schema, run the call, parse the response, and on validation failure retry with the error message included in the next prompt. Works across any provider because it does not depend on provider-specific enforcement. The trade is reliability versus portability. Server-side constrained decoding is tightest but ties you to one provider's feature; client-side validate-retry is most portable but pays retry latency on edge cases.

The six toolkits in this question are not interchangeable. They sit on a spectrum of how aggressively the schema is enforced and where in the stack the enforcement happens. ### The three server-side stacks **OpenAI Structured Outputs** in strict mode compiles your JSON Schema (often generated from a Pydantic model) into a token-level grammar and masks logits at every decode step. Invalid output is impossible. This is the strongest guarantee on a closed-API provider. **Anthropic Claude tool use** ships an `input_schema` with each tool definition. The model is biased to match the schema during generation and the API server validates the tool argument before returning it. Slightly looser than OpenAI strict mode but the validation step is still server-side. **Google Gemini responseSchema** lives on the generation config object. Gemini 3.1 Pro respects the schema as a structured-output mode; the enforcement is closer to a strong JSON mode plus shape validation than to OpenAI's full constrained decoding, but the practical reliability is similar in 2026. ### The three client-side stacks **Instructor (Python)** takes a `response_model: BaseModel` argument, runs the underlying provider call, parses with Pydantic, and on `ValidationError` retries the call with the error appended. Cross-provider, works against OpenAI, Anthropic, Gemini, and self-hosted models. **Vercel AI SDK generateObject** is the TypeScript counterpart. Pass a Zod schema, get a typed object back. Same validate-retry loop, packaged for Next.js and Node servers. **Outlines / SGLang** is a different animal: a local constrained-decoding library for self-hosted models. It compiles a regex or context-free grammar to a sampler mask and ships with vLLM and SGLang integrations. The only entry on this list that gives you grammar-level (not just JSON-shape) constraints on open-weight stacks. ### How to pick - One closed provider, max reliability → OpenAI Structured Outputs. - Multiple closed providers,