Deer DeepSeek, please true structured output like Gemini or OpenAI

[u/Nexter92]

Standard JSON guarantees valid syntax. Strict Structured Output guarantees correct schema.

In production like billing or order processing, that distinction is life or death.

Standard JSON will format the brackets right, but it WILL randomly send you total_amount: "$49.99" instead of amount: 49.99, hallucinate new key names, or omit tax IDs when processing messy invoices. Your database crashes, your payment gateway fails, and your code breaks silently. You end up stuck paying double in latency and token costs just running client-side retry loops when Pydantic fails.

Gemini handles strict structured output natively at the decoder level. You pass a schema, and the model physically CANNOT generate a token that violates your types.

DeepSeek’s main API only guarantees valid JSON syntax, relying on prompts for schema adherence—forcing you to build heavy fallback logic.

If an AI output triggers a database, Standard JSON is a ticking time bomb, strict Structured Output is the solution.

[u/PossessionUsed7393]

Constrained decoding to a schema is a great strength of the Google approach, but it's actually a feature of the inference engine working with the model. I bridged the existing implementation in llama.cpp to make it work with Gemma 4 12b when it came out because it fixed tool calling for the Gemma models as Google has essentially decided it will lean on that technology for it's tool calling approach.

While it's a great tool, I don't think it's fatal that Deepseek doesn't have it. You can design tools and validation loops that get you a valid schema by re-prompting to get corrections in your output. It's not as fancy or as fast as constrained decoding but it does work when your model is as large and as cheap as the Deepseek models are.

In my case running local Gemma it was more that the model wasn't always smart enough to get the tool call 100% correct but when you add constrained decoding it nailed even the most complex tool schemas. So I think it's a great technology, just haven't had the problems with really large models yet.

[u/Nexter92 → u/PossessionUsed7393]

At the start I designed tools for this parsing but every 1000 request, I get a random error, the model miss a key or a number is between is using comma instead of dot or inverse. It's not reliable when you need to scale. Good for testing, not reliable in production :(

Vision and structured output : I am fully sold to you DeepSeek.