GPT-4o and GPT-4.1 Retire January 14 — Migrate to Luna Now

OpenAI will retire GPT-4o, GPT-4o mini, GPT-4.1, and GPT-4.1 mini from its API on January 14, 2027, with new fine-tuning jobs on those base models freezing on January 6, 2027, according to the company's deprecation notice. The replacement model, gpt-6-luna, costs $0.10 per million input tokens and $0.50 per million output tokens — 96% less than GPT-4o's $2.50/$10.00 — with a 1,050,000-token context window versus GPT-4o's 128,000 and a composite quality index 167% higher. Migration requires removing the temperature and top_p parameters, which Luna rejects, and switching function-calling requests with reasoning enabled from /v1/chat/completions to /v1/responses.

OpenAI filed the paperwork today: GPT-4o, GPT-4o mini, GPT-4.1, and GPT-4.1 mini are being pulled from the API on January 14, 2027 . If you have fine-tuned models on any of these, the window is tighter — new training jobs freeze on January 6 . The replacement is gpt-6-luna https://developers.openai.com/api/docs/deprecations , which is 96% cheaper and outperforms GPT-4o on quality benchmarks. Here is what you need to change and when. Which Models Are Affected These are the specific model IDs going dark on January 14: - gpt-4o-2024-08-06 - gpt-4o-2024-11-20 - gpt-4o-mini-2024-07-18 - gpt-4.1-2025-04-14 - gpt-4.1-mini-2025-04-14 Any fine-tuned model built on these bases follows the same schedule. If you hardcoded a snapshot ID in your application, your code breaks on that date. If you used a generic alias like gpt-4o , OpenAI will auto-redirect you — but verify that behavior in your environment before trusting it. Two Dates That Matter Most coverage will focus on January 14. The date that matters more if you own fine-tuned models is earlier: January 6, 2027 is the last day to create new fine-tuning jobs on any of the deprecated base models. After that, you can still run inference on existing fine-tuned models until the 14th — but you cannot retrain or create new variants. Write both dates down. Miss January 6 and you have no path to iterate your fine-tuned model before the lights go out. GPT-6 Luna: The Replacement Luna is not just the default fallback — it is genuinely better across most dimensions. At $0.10 per million input tokens and $0.50 per million output tokens, it costs 96% less than GPT-4o $2.50/$10.00 . Its context window is 1,050,000 tokens, roughly eight times larger than GPT-4o’s 128,000. On the composite quality index, it scores 167% higher than GPT-4o. See the full benchmark breakdown https://www.activepieces.com/blog/gpt-6-luna-pricing-benchmarks-and-performance-in-2026 for a detailed comparison. The one real tradeoff is streaming latency. Luna is a reasoning model — it thinks before it outputs, so per-token streaming is slower than GPT-4o. For interactive, low-latency applications, test carefully. For batch processing, agents, and anything where quality matters more than time-to-first-token, Luna is the obvious upgrade. How to Migrate OpenAI’s official migration guide https://help.openai.com/en/articles/20001600-migrating-from-gpt-4x-to-gpt-6-luna recommends four steps. Follow them in order and do not skip the baseline phase — you will need it to catch regressions. 1. Baseline. Record a set of representative production requests and their current outputs. This is your ground truth. 2. Evaluate with Luna. Run the same requests through gpt-6-luna with reasoning effort set to "none" . This mode makes Luna behave closest to GPT-4x without engaging the reasoning engine. 3. Adjust. Compare outputs. Tune prompts, adjust verbosity expectations, and check formatting assumptions. Most prompts migrate cleanly; some verbose outputs need trimming instructions added. 4. Gradual rollout. Route 5–10% of production traffic to Luna, monitor quality metrics, latency, and error rates, then increase incrementally. Breaking Changes to Catch Before You Ship Two specific API parameters that will break your calls: temperature and top p : Luna rejects both. If your code passes these parameters, your API call returns an error. Remove them from any request targeting Luna. Function tools with reasoning enabled: If you use function calling with any reasoning effort setting other than "none" , you must switch from /v1/chat/completions to /v1/responses . Starting with reasoning effort: "none" lets you stay on the completions endpoint during initial migration. Before breaks January 14, 2027 response = client.chat.completions.create model="gpt-4o-2024-08-06", temperature=0.7, Remove this top p=0.9, Remove this too messages= {"role": "user", "content": prompt} After working Luna call response = client.chat.completions.create model="gpt-6-luna", reasoning effort="none" for closest GPT-4x parity messages= {"role": "user", "content": prompt} If You Have Fine-Tuned Models This is the group that needs to move fastest. Before January 6: 1. Export your training data from the OpenAI platform 2. Start a new fine-tuning job targeting gpt-6-luna 3. Evaluate the new fine-tuned Luna model against your benchmarks 4. Update your endpoint references from ft:gpt-4.1-... to the new fine-tuned Luna model ID Fine-tuning on Luna is available now. There is no technical reason to wait — and the cost of training is lower given Luna’s pricing. The only reason people will miss January 6 is procrastination. Start Now 91 days sounds like enough time until your Q4 roadmap fills up. The migration itself is straightforward — OpenAI did the work to make Luna the clear upgrade path. What takes time is evaluating output quality, catching the temperature / top p parameter errors in your codebase, and retraining any fine-tuned models. Check the GPT-4o vs Luna side-by-side comparison https://www.orcarouter.ai/compare/gpt-4o-vs-gpt-6-luna if you need to build a case internally for the switch. The official deprecation page https://developers.openai.com/api/docs/deprecations lists all affected models and dates. January 14 is a hard cutoff with no grace period.