# GPT-4o and GPT-4.1 Retire January 14 — Migrate to Luna Now > Source: > Published: 2026-10-14 21:10:35+00:00 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.