Anthropic to OpenAI API Migration
How hard is it to migrate from Anthropic to OpenAI?
Config change: 8 parameters need attention when moving from Anthropic to OpenAI, 3 with no equivalent at all. Anthropic's `thinking. Blended cost per million tokens drops 77.5% on the default model pair.
Should you?
Teams move from Anthropic to OpenAI for the wider tool ecosystem — more OpenAI-compatible hosts, more third-party libraries that assume the Chat Completions shape by default — or because a workload needs OpenAI-only surface area like the `n` parameter for generating multiple candidate completions per call. It also shows up when a team standardises on the `openai` SDK across every provider it uses (most of the other providers on this site speak it natively) and would rather absorb one migration than keep a second SDK alive for Claude alone.
No operational facts lost vs the source provider.
The parameter mapping
Derived from Anthropic's and OpenAI's API surface, sourced 2026-08-09. Breaking rows first.
| Concept | Anthropic | OpenAI | Status | Note |
|---|---|---|---|---|
| topK | top_k | — | No equivalent | No top_k equivalent — use top_p for nucleus sampling. |
| reasoningBudget | thinking.budget_tokens | — | No equivalent | No token-budget knob — reasoning depth is the effort enum only. |
| cacheControl | cache_control (per content block) | — | No equivalent | Caching is automatic on repeated prefixes — no explicit field. |
| systemPrompt | system | messages[].role="system" | Reshaped | |
| temperature | temperature | temperature | Constrained | Range is 0-2 on the target vs. 0-1 on the source. |
| maxOutputTokens | max_tokens | max_completion_tokens | Renamed | |
| stopSequences | stop_sequences | stop | Renamed | |
| toolDefinitions | tools (input_schema) | tools (function.parameters) | Renamed | |
| reasoningEffort | — | reasoning_effort | Gained | Enum (low/medium/high), not a token budget. |
| jsonMode | — | response_format.type="json_object" | Gained | |
| structuredOutput | — | response_format.json_schema | Gained | |
| seed | — | seed | Gained | Best-effort determinism only, not guaranteed. |
| nCompletions | — | n | Gained | |
| frequencyPenalty | — | frequency_penalty | Gained | |
| logprobs | — | logprobs | Gained | |
| topP | top_p | top_p | Identical | |
| stream | stream | stream | Identical | |
| toolChoice | tool_choice | tool_choice | Identical |
The diff
− Anthropic (claude-haiku-4-5) · + OpenAI (gpt-5.6-luna)
- const client = new Anthropic({ apiKey: process.env.ANTHROPIC_API_KEY });+ const client = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });- const response = await client.messages.create({+ const response = await client.chat.completions.create({model: MODEL,- max_tokens: 1024, // maxOutputTokens: renamedmessages: [{ role: 'user', content: 'Say hello in one sentence.' }],});- const block = response.content[0];- console.log(block.type === 'text' ? block.text : '');+ console.log(response.choices[0].message.content);
What doesn't port at all
Anthropic's `thinking.budget_tokens` has no OpenAI equivalent beyond the coarse `reasoning_effort` enum, so a workload tuned to a specific extended-thinking token budget loses that precision; and any code relying on Anthropic's mandatory, always-present `max_tokens` field to bound spend has to re-verify OpenAI's optional field is actually being set, since nothing forces it there.
- topK (
top_k) — No top_k equivalent — use top_p for nucleus sampling. - reasoningBudget (
thinking.budget_tokens) — No token-budget knob — reasoning depth is the effort enum only. - cacheControl (
cache_control (per content block)) — Caching is automatic on repeated prefixes — no explicit field.
The cost delta
Claude Haiku 4.5 ($2.00/M blended) to GPT-5.6 Luna ($0.45/M blended): blended cost drops 77.5%. Full GPT-5.6 Luna pricing →
Recompute on your own token shape at the cost calculator.
Which OpenAI model to move to
gpt-5.6-luna — $0.45/M blended. Pricing → Alternatives →
gpt-5.6-terra — $4.50/M blended. Pricing → Alternatives →
gpt-5.6-sol — $11.25/M blended. Pricing → Alternatives →
The cutover checklist
- Dual-run both providers on the same prompts and diff the outputs before cutting traffic over.
- Expect a rate-limit cold start on a brand-new OpenAI key — see OpenAI rate limits.
- Fix every row marked "No equivalent" or "Now required" above before switching a single production call.
- Keep the Anthropic client and key live behind a feature flag until the OpenAI path has run in production for at least a week.
Route it through All AI Ask instead
This is not a base-URL swap — the All AI Ask gateway uses its own request shape (a `prompt` string and a `models` array, not OpenAI's message format), documented in full at /api-docs. In exchange, this one call can also run the prompt across other models side by side.
curl https://allaiask.com/api/v1/prompt \
-H "Authorization: Bearer $ALLAIASK_API_KEY" \
-H "Content-Type: application/json" \
-d '{"prompt": "Say hello in one sentence.", "models": ["gpt-5.6-luna"]}'FAQ
Is Anthropic to OpenAI a drop-in migration?
No — it's a config change. 8 of 18 tracked parameters need attention; see the mapping table above for exactly which ones and why.
What breaks first when I port Anthropic code to OpenAI?
topK — Anthropic's `top_k` has no OpenAI equivalent (No top_k equivalent — use top_p for nucleus sampling.).