OpenAI Retiring GPT-4o: Complete Migration Guide
OpenAI retires GPT-4o, GPT-4.1, and o4-mini from ChatGPT on February 13, 2026. Complete migration guide to GPT-5.2 for businesses and developers.
Models Retiring
Daily GPT-4o Users
Retirement Date
API Changes
Key Takeaways
On January 29, 2026, OpenAI announced the retirement of six models from ChatGPT: GPT-4o, GPT-4.1, GPT-4.1 mini, o4-mini, GPT-5 Instant, and GPT-5 Thinking. The cutoff date is February 13, 2026. After that date, these models will no longer appear in the ChatGPT model selector, and all existing conversations and custom GPTs will default to GPT-5.2.
This retirement marks the definitive end of the GPT-4 era. The model that powered ChatGPT's mainstream breakthrough in 2024 and early 2025 has been superseded so thoroughly that only 0.1% of daily users still actively select it. For most ChatGPT users, this change will be invisible. For developers and businesses with workflows built around specific model behaviors, the transition requires careful planning. This guide provides everything you need to migrate smoothly, whether you are a casual ChatGPT user or a developer with production API integrations.
What Is Happening: The Complete Model Retirement
OpenAI is consolidating its ChatGPT model lineup around GPT-5.2, removing six older models that have been gradually losing user adoption since GPT-5.2's release in December 2025. The retirement reflects a pattern OpenAI has followed before: once a new model demonstrates clear superiority and adoption reaches critical mass, older models get deprecated to simplify the product and reduce infrastructure costs.
The flagship GPT-4 variant
Instruction-following update
Lightweight GPT-4.1 variant
Compact reasoning model
Fast-response GPT-5 mode
Deep-reasoning GPT-5 mode
This is not the first time OpenAI has walked back a retirement only to follow through later. GPT-4o was briefly removed in late 2025, then reinstated after users voiced concerns about losing its conversational warmth and creative capabilities. OpenAI used that feedback window to develop GPT-5.2's Personality feature, which lets users customize the model's communication style. With that bridge in place, the permanent retirement can now proceed. For a deeper look at the GPT-5 generation that preceded this consolidation, see our complete guide to GPT-5.
Full Retirement Timeline
Understanding the sequence of events helps you plan your migration without unnecessary urgency or missed deadlines.
January 29, 2026 — Announcement
OpenAI publicly announces the retirement of six models from ChatGPT, giving users approximately two weeks to prepare.
February 1-12, 2026 — Transition Window
Users can still select retiring models in ChatGPT. Use this period to test your workflows with GPT-5.2, update custom GPT instructions, and export any critical conversation histories.
February 13, 2026 — ChatGPT Retirement
All six models removed from ChatGPT. Existing chats and custom GPTs automatically default to GPT-5.2. No further access to these models via the ChatGPT interface.
TBD — API Deprecation (Expected)
OpenAI has not announced an API retirement date yet. Based on historical patterns, expect a deprecation notice with at least 3-6 months lead time. Start planning now.
API vs ChatGPT: Understanding the Different Impact
One of the most important distinctions in this announcement is the separation between ChatGPT (the consumer product) and the OpenAI API (the developer platform). These follow different deprecation schedules, and understanding the difference prevents unnecessary panic or premature migration.
| Aspect | ChatGPT (Consumer) | API (Developer) |
|---|---|---|
| Retirement Date | February 13, 2026 | No changes announced |
| Affected Models | GPT-4o, GPT-4.1, GPT-4.1 mini, o4-mini, GPT-5 Instant/Thinking | None (currently) |
| Existing Conversations | Default to GPT-5.2 | N/A |
| Custom GPTs | Auto-migrate to GPT-5.2 | N/A |
| Action Required | Test workflows with GPT-5.2 | Plan ahead, no immediate action |
For developers building applications on the OpenAI API, this announcement is a signal rather than an emergency. The pattern is clear: ChatGPT retirements precede API deprecations. Use this window to begin testing GPT-5.2 in staging environments, benchmark your specific use cases, and prepare migration plans. For a detailed look at GPT-5.2's capabilities and pricing, see our complete GPT-5.2 guide.
Migration Checklist: What to Do Before February 13
Whether you are a ChatGPT power user or a developer with API integrations, this checklist covers the essential steps for a smooth transition.
- Test your common workflows with GPT-5.2 before the deadline
- Export important conversation histories you want to preserve
- Review and update custom GPT instructions for GPT-5.2 compatibility
- Explore Personality settings to match your preferred communication style
- Save any custom system prompts or instructions used with GPT-4o
- Audit all hardcoded model references in your codebase
- Set up GPT-5.2 in a staging environment and run your evaluation suite
- Build model-switching logic via configuration, not hardcoded values
- Compare output quality, latency, and token costs between models
- Monitor OpenAI's deprecation page for API retirement announcements
GPT-4o vs GPT-5.2: What You Gain (and Lose)
The migration from GPT-4o to GPT-5.2 is overwhelmingly positive on technical merits. GPT-5.2 outperforms its predecessor across reasoning, coding, factual accuracy, and context handling. However, some users valued specific GPT-4o characteristics that require different prompting approaches in GPT-5.2. Understanding both sides helps you set realistic expectations. For a broader comparison across AI models, see our ChatGPT vs Claude vs Gemini vs Grok comparison.
| Capability | GPT-4o | GPT-5.2 | Verdict |
|---|---|---|---|
| Context Window | 128K tokens | 400K tokens | 3x larger in GPT-5.2 |
| Reasoning Depth | Standard | 5 levels (none to xhigh) | Configurable in GPT-5.2 |
| Coding (SWE-Bench) | ~26% | 80.0% | Major improvement |
| Factual Accuracy | Good | 70.9% GDPval | Substantially better |
| Conversational Warmth | Natural, warm | Configurable via Personality | Preference-dependent |
| Creative Freedom | Less filtered | More guardrails | GPT-4o preferred by some |
| Response Compaction | Not available | Supported | New in GPT-5.2 |
| Cached Input Pricing | Not available | 90% discount | Major cost savings |
- 3x larger context window (400K vs 128K)
- Adaptive reasoning with 5 configurable depth levels
- Response compaction for workflows exceeding context limits
- 90% cached input discount for repeated system prompts
- Substantially better coding, reasoning, and factual accuracy
- Default tone is more measured; use Personality settings to adjust
- Creative writing may feel more structured; refine prompts accordingly
- Existing prompts may produce different output formats
- GPT-5.2 defaults to no reasoning unless explicitly set
Prompt Adaptation Strategies for GPT-5.2
Most GPT-4o prompts will work with GPT-5.2 without modification. However, if you relied on specific GPT-4o behaviors, these adaptation strategies help bridge the gap.
GPT-5.2 defaults to reasoning_effort: none, which means it behaves as a non-reasoning model unless you specify otherwise. If your GPT-4o workflows relied on the model "thinking through" complex problems, you need to explicitly set reasoning effort to medium or high for comparable behavior.
API example: reasoning: { effort: "high" }
If you miss GPT-4o's conversational warmth, navigate to Settings and select the "Friendly" or "Candid" personality preset. For creative writing workflows, try "Quirky" to approximate GPT-4o's more expressive output style. You can also add tone instructions directly in your system prompt for API-based applications.
GPT-4o's 128K context window often required you to truncate or summarize input data. With GPT-5.2's 400K token context, you can include complete documents, full codebases, and detailed brand guidelines without compression. This alone can significantly improve output quality for complex tasks.
Switch the model parameter first while keeping everything else identical. Evaluate outputs against your quality benchmarks. Only then begin optimizing prompts for GPT-5.2's specific strengths. Changing the model and prompts simultaneously makes it impossible to identify the source of any output differences.
What This Means for Businesses
The GPT-4o retirement signals a broader industry shift: the era of running multiple AI model generations in parallel is ending. OpenAI is consolidating around fewer, more capable models, and competitors like Anthropic and Google are following similar patterns. For businesses, this has practical implications for AI strategy, vendor management, and long-term planning.
GPT-5.2 is objectively more capable than GPT-4o. Teams still running GPT-4o workflows will see immediate improvements in output quality, reasoning depth, and context handling after migration. The 90% cached input discount can also reduce API costs substantially.
Teams with tightly coupled GPT-4o integrations may encounter output format changes, different default behaviors (especially around reasoning), or content policy differences. Testing before the deadline is essential. Budget for prompt engineering time.
Build model-agnostic architectures. Abstract your model selection behind configuration layers so future retirements require a config change rather than a code change. Consider multi-model strategies for different use cases.
This retirement also underscores the importance of having an AI strategy that anticipates model evolution. Organizations that built their workflows around a specific model's quirks rather than general AI capabilities now face harder migrations. Those with abstracted, model-agnostic integrations can switch models with minimal disruption. If your business needs guidance on AI integration and digital transformation, building this kind of resilient architecture is exactly where professional guidance pays for itself.
Need Help Migrating Your AI Workflows?
Our team can help you transition from GPT-4o to GPT-5.2, optimize your prompts, and build model-agnostic architectures that handle future retirements smoothly.
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