HoldsHolds on claude-haiku-4-5Holds on gpt-5-miniHolds on gemini-3.1-flash-lite

Getting clean JSON back

Structured outputs: the JSON schema raised valid JSON rates

Show the model the exact shape you want back. Write out the field names instead of just asking for JSON.

Why it matters

Without a shape to copy, the model picks its own field names and your code breaks.

How to use it

Put a small example of the structure in your prompt. List every field you need and what kind of value it holds.

Pass rate rose from 0% to 100% on all three models.

What the marks mean

  • Holds
Show the per-model numbers

Claim tested: An explicit JSON schema in the prompt yields a higher valid-JSON rate than an unstructured instruction to respond in JSON.

Holds on claude-haiku-4-5, gpt-5-mini and gemini-3.1-flash-lite.

Circulates in practitioner communities. Tested because it circulates, not because it is endorsed.

This tip is OpenAddict's plain-language read of the measured result. The measurement below is the evidence, and it is what the reading has to answer to.

Ledger idC01-json-schema

What was tested

This claim circulates in practitioner communities as advice about how to write prompts. That it circulates is an input to what gets tested here. It is a reason to test the claim, and it is not evidence for or against it. The result below is the evidence, and it is the only thing on this page that carries weight.

The comparison is paired. Two prompts differ in one respect, the manipulated variable, and are otherwise identical by construction. Nothing here supports a causal reading beyond that pairing.

Result

Per-model results. Means are over valid scored records only. Invalid records are excluded from every denominator and counted in coverage.
Measureclaude-haiku-4-5gpt-5-minigemini-3.1-flash-lite
Control arm0.000n 500.000n 500.000n 50
Treatment arm1.000n 501.000n 501.000n 50
Delta+1.000+1.000+1.000
Interval, 95 percent1.000 to 1.0001.000 to 1.0001.000 to 1.000
OrbitStable100 of 100 recordsStable100 of 100 recordsStable100 of 100 records

Orbit is assigned by the frozen status_v1 rule. On this scale, deterministic pass rate, 0 to 1, the pass threshold is +0.20 and the failure floor is -0.20, each requiring an interval that excludes zero.

Method for this claim

Task set
10 short biographical snippets, varied in sentence order, length, and how directly each field is stated. Both arms ask for the same three fields; only the schema statement differs. Hardened for instrument v2: v1 asked for three obvious fields (name, role, city) that any model emits under a bare respond-in-JSON instruction. v2 requires five fields whose key names are not guessable from the prose, so an unstructured arm produces valid JSON with the wrong keys.
Runs per model per arm
5
Scoring
Deterministic, via scoreJsonValidAndShaped. A committed function scores each answer with no model in the loop.
Pass criterion as written for the pilot
Treatment valid-JSON rate exceeds control by at least 10 percentage points on the same 10 inputs at the same model and settings.

The published verdict comes from status_v1, not from the pass criterion above. The criterion is recorded because it is what the claim was registered with before the run.

Model versions, as recorded

Read from the run records, not from configuration.
ModelVersion string returnedDelta on this claimInterval
claude-haiku-4-5claude-haiku-4-5-20251001+1.0001.000 to 1.000
gpt-5-minigpt-5-mini-2025-08-07+1.0001.000 to 1.000
gemini-3.1-flash-litegemini-3.1-flash-lite+1.0001.000 to 1.000

Reading across models

Sampling was not held constant across vendors, so comparing one model column against another compares two settings as well as two models.

gpt-5-mini rejected the fixed sampling setting and ran at its own default on all 1040 of its calls. The other models ran at temperature 0.

The tip above is editorial. Every figure inside the measurement is computed at build time from the committed pilot records by the status_v1 rule, and none of it is written by hand.