---
title: "Migrating prompts to new models"
slug: "prompt-migration"
category: "prompt-engineering"
tags: ["prompt-engineering", "migration", "model-upgrade", "claude", "openai", "evals", "prefill"]
status: "stable"
last_updated: 2026-10-01
summary: "Move prompts to a newer model: baseline, drop rejected parameters (prefill, sampling, budget_tokens, forced tool_choice), prune old workarounds, re-run evals."
related: ["[[prompt-engineering/prompt-design]]", "[[prompt-engineering/reasoning-model-prompting]]", "[[prompt-engineering/prompt-evals]]", "[[prompt-engineering/prompt-caching-strategies]]", "[[ai-agents/structured-output]]", "[[ai-agents/cost-control]]"]
---

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## Overview

A prompt tuned for one model generation often fails or over-triggers on the next, because API parameters are removed and the compensating instructions you wrote for older models now overshoot. Treat a model change as a code change: baseline, apply the checklist below, then re-run the eval set. Facts below are as of October 2026; confirm against the vendor migration guide for your target model.

## Baseline the old prompt on the new model first

Run the unchanged prompt on the eval set against the new model ID and record per-slice scores (see [[prompt-engineering/prompt-evals]]). Then change one thing at a time. Adjacent versions often work without edits; Anthropic says existing Fable 5 prompts should perform well on Fable 5.1, for example.

## Remove parameters the new model rejects

- **Assistant prefill** returns 400 on Claude 4.6 and later and every 5.x model. Replace format forcing with structured outputs (`output_config: {format: {...}}`), preamble suppression with "Respond directly without preamble", and continuations with a user message that quotes the end of the interrupted reply.
- **Sampling parameters**: Claude Opus 4.7 and later, Opus 5.x, Sonnet 5.x, and Fable 5.x accept only the defaults (`temperature` 1.0, `top_p` 0.99 or higher) and return 400 for other values and any `top_k`; drop them. OpenAI GPT-6 Astra does not support custom `temperature` or `top_p`.
- **`thinking: {"type": "enabled", "budget_tokens": N}`** returns 400 on Opus 4.7 and later, Opus 5.x, Sonnet 5.x, and Fable 5.x. Use `thinking: {"type": "adaptive"}` plus `output_config.effort`; `thinking: {"type": "disabled"}` also returns 400 on Opus 5.5.
- **Forced `tool_choice`** (`any` or `tool`) returns 400 on Fable 5.1, Opus 5.5, and Sonnet 5.5. Use `auto` with `strict: true` tools, or structured outputs.
- **Top-level `output_format`** is deprecated; use `output_config.format`.
- **OpenAI `reasoning.effort: "none"`** is unsupported on GPT-6 Astra and GPT-6.1 Sol; use `low`.

## Remove instructions written to compensate for older models

- Emphatic wording ("CRITICAL: You MUST use this tool") makes models that follow the system prompt closely use tools too often. Write "Use this tool when...".
- Blanket tool defaults ("If in doubt, use the tool") and anti-laziness prompts ("be thorough") push current models into excess exploration.
- Anti-formatting rules can suppress needed structure on Fable 5.1, which already formats less; replace them with a rule for when formatting is appropriate.
- Verification boilerplate causes over-verification on Opus 5; remove it.
- "Think step by step" scaffolds add cost, and requests to reproduce reasoning in the answer can trigger `reasoning_extraction` refusals on Fable 5.x, Opus 5.x, and Sonnet 5.5. See [[prompt-engineering/reasoning-model-prompting]].

## Re-sweep effort and recount tokens

Effort level names do not map to the same thinking depth across models, so run a fresh sweep instead of copying the old setting. Claude 4.7 and later use a tokenizer that produces about 30 percent more tokens for the same text, so recheck `max_tokens`, cost estimates, and cache minimums (see [[ai-agents/cost-control]]).

## Keep history append-only on thinking models

On Fable 5.1, Opus 5.5, and Sonnet 5.5, pass thinking blocks back unchanged. Editing earlier turns, rebuilding `system` or `tools`, or summarizing old turns in place breaks them and restarts the prompt cache; use mid-conversation system messages and server-side compaction instead. See [[prompt-engineering/prompt-caching-strategies]].

## Re-run evals, then pin

Compare the new scores per slice, fix regressions with the smallest prompt change, and pin the exact model ID. Prompt caches are per model, so expect a cold start after cutover.

## Related

- [[prompt-engineering/prompt-design]]
- [[prompt-engineering/reasoning-model-prompting]]
- [[prompt-engineering/prompt-evals]]
- [[prompt-engineering/prompt-caching-strategies]]
- [[ai-agents/structured-output]]
- [[ai-agents/cost-control]]