GPT-5.2 and Codex: Complete OpenAI Model Guide 2026
OpenAI launches GPT-5.2 and GPT-5.2-Codex optimized for agentic coding as GPT-4o retires February 13, 2026. Complete migration and implementation guide.
Context Window
Knowledge Cutoff
SWE-Bench Pro (Codex)
Models Retiring Feb 13
Key Takeaways
OpenAI's model lineup underwent a significant transformation between December 2025 and January 2026. GPT-5.2 launched on December 11, 2025, introducing three operational modes designed for different use cases. Five weeks later, GPT-5.2-Codex arrived on January 14, 2026, bringing specialized agentic coding capabilities with context compaction and enhanced cybersecurity features. Now, on February 13, 2026, six older models are being retired from ChatGPT entirely.
This guide covers the full picture: what GPT-5.2 and its variants offer, how GPT-5.2-Codex changes the development workflow, what the model retirements mean for your business, and how to plan your migration. Whether you are evaluating these models for enterprise AI adoption or migrating existing integrations, you will find the technical details and strategic context needed to make informed decisions. For background on the broader AI transformation landscape, our services page covers how agencies and businesses can adapt to rapid model evolution.
GPT-5.2 Overview: Architecture and Capabilities
GPT-5.2 represents OpenAI's most capable general-purpose model series for professional knowledge work. Released under what multiple outlets reported as an internal acceleration prompted by competition from Google's Gemini 3 Pro, it arrived approximately three weeks after Gemini 3 Pro's launch. The model delivers measurable improvements in spreadsheet creation, presentation building, code generation, image understanding, long-context processing, and multi-step project execution.
Over 3x the context of GPT-4o (128K), enabling processing of full codebases, complete legal documents, and lengthy research papers in a single request.
Substantially longer output generation capacity, allowing the model to produce detailed reports, complete code files, and thorough analyses without truncation.
Knowledge cutoff of August 31, 2025, nearly a full year ahead of GPT-5.1's September 2024 cutoff. The model is aware of events through late summer 2025.
All three GPT-5.2 modes (Instant, Thinking, and Pro) share the same core architecture, knowledge cutoff, and context window. The differences lie in reasoning depth, response latency, and pricing. This tiered approach lets developers and users match model capability to task complexity rather than defaulting to a single model for every use case.
Instant, Thinking, and Pro: Choosing the Right Mode
GPT-5.2's three-mode architecture reflects the reality that different tasks require different levels of computational investment. A quick translation does not need the same reasoning depth as debugging a race condition in distributed code. Selecting the right mode balances output quality, response speed, and cost.
Responds immediately without pausing to reason through intermediate steps. Best for info-seeking questions, how-to guides, technical writing, translation, and routine content generation where speed matters more than deep analysis.
Pauses to reason through problems before responding. Includes a configurable Reasoning Effort setting (Standard, High, Extra High) that controls how long the model deliberates. Excels at coding, long-document summarization, file analysis, math, logic, and structured planning.
The slowest and most expensive mode, designed for scenarios where fewer errors and stronger domain performance justify the wait and cost. Early testing shows reduced major error rates in complex domains like programming and scientific analysis. Canvas and image generation are not available in Pro mode within ChatGPT.
| Feature | Instant | Thinking | Pro |
|---|---|---|---|
| Response Speed | Fastest | Moderate | Slowest |
| Reasoning Depth | None (default) | Configurable (3 levels) | Maximum |
| Input Cost (/1M tokens) | $1.75 | $1.75 | $21.00 |
| Output Cost (/1M tokens) | $14.00 | $14.00 + reasoning | $168.00 |
| Context Window | 400K | 400K | 400K |
| Canvas / Image Gen | Available | Available | Not available |
| Best For | Everyday tasks | Complex work | High-stakes tasks |
A practical approach for most teams is to default to Instant for routine work, use Thinking for coding and analytical tasks, and reserve Pro for decisions where accuracy is critical and latency is acceptable. API developers can automate this selection based on task classification, routing simple queries to Instant and complex ones to Thinking or Pro.
GPT-5.2-Codex: Agentic Coding for Real-World Engineering
GPT-5.2-Codex, released January 14, 2026, is a purpose-built derivative of GPT-5.2 optimized for agentic coding workflows. Where GPT-5.2 Thinking handles individual coding problems well, GPT-5.2-Codex is designed for sustained, multi-step software engineering sessions: large refactors, codebase migrations, feature implementations, and security audits that span many files and require the model to maintain coherent understanding across extended interactions.
Compresses earlier context while preserving critical task state, enabling multi-hour coding sessions without losing track of project scope. The model can continue iterating on complex tasks even when plans change mid-session.
Stronger performance on large code changes like refactors, migrations, and multi-file feature builds. Can recover from failed attempts and adjust approach without restarting the entire task from scratch.
OpenAI states GPT-5.2-Codex has stronger cybersecurity capabilities than any previous model, including vulnerability detection during code generation. OpenAI acknowledges this also raises new dual-use considerations.
Improved performance in Windows development environments, addressing a gap where previous models were predominantly optimized for Unix-based toolchains and workflows.
GPT-5.2-Codex is available in all Codex surfaces for paid ChatGPT users, with API access available for building custom agentic coding applications. It uses the same base pricing as GPT-5.2 ($1.75/M input, $14/M output), though agentic sessions tend to consume significantly more tokens due to multi-step tool calling and extended reasoning chains. For more details on how GPT-5.2-Codex compares to alternatives like Claude Sonnet 5 and other frontier models, the competitive landscape is evolving rapidly.
Benchmarks and Performance
GPT-5.2 achieves state-of-the-art or near-state-of-the-art results across multiple benchmark categories. The numbers below represent the strongest reported scores across GPT-5.2 variants; note that Instant, Thinking, and Pro produce different scores on each benchmark based on their reasoning depth.
| Benchmark | Domain | GPT-5.2 Score | Notes |
|---|---|---|---|
| GPQA Diamond | Graduate-level science | 93.2% (Pro) | Thinking: 92.4% |
| AIME 2025 | Math competition | 100% | Perfect score, no tools |
| SWE-Bench Verified | Software engineering | 80.0% (Thinking) | Up from ~26% (GPT-4o) |
| SWE-Bench Pro | Agentic coding | 56.4% (Codex) | vs 55.6% base GPT-5.2 |
| Terminal-Bench 2.0 | Terminal environments | 64.0% (Codex) | vs 62.2% base GPT-5.2 |
| ARC-AGI-1 | Abstract reasoning | 90%+ | First model above 90% |
| FrontierMath | Advanced mathematics | 40.3% | Challenging frontier benchmark |
- SWE-Bench Verified: ~26% to 80% (3x improvement)
- Context window: 128K to 400K tokens
- Knowledge cutoff: Apr 2024 to Aug 2025
- Configurable reasoning with 5 effort levels
- SWE-Bench Pro: 56.4% vs 55.6% (base)
- Terminal-Bench 2.0: 64.0% vs 62.2% (base)
- Native context compaction for long sessions
- Enhanced cybersecurity detection
These benchmarks reflect controlled testing conditions. Real-world performance depends on prompt quality, task complexity, and domain specifics. The most meaningful evaluation for your use case is running your own test suite against GPT-5.2 and comparing results to your current model. Benchmarks provide directional guidance, not guarantees.
Pricing and Context Windows
GPT-5.2's pricing structure reflects a premium over GPT-5.1 (approximately 40% more expensive at base rates) justified by the capability improvements. However, the 90% cached input discount and 50% Batch API discount make effective pricing competitive or even cheaper for workloads that benefit from caching.
| Model | Input (/1M) | Output (/1M) | Cached Input (/1M) | Context |
|---|---|---|---|---|
| GPT-5.2 Instant | $1.75 | $14.00 | $0.175 | 400K in / 128K out |
| GPT-5.2 Thinking | $1.75 | $14.00* | $0.175 | 400K in / 128K out |
| GPT-5.2 Pro | $21.00 | $168.00 | $2.10 | 400K in / 128K out |
| GPT-5.2-Codex | $1.75 | $14.00 | $0.175 | 400K in / 128K out |
| GPT-5.1 (previous) | $1.25 | $10.00 | $0.125 | 256K in / 64K out |
* Thinking mode generates internal reasoning tokens billed at the output rate ($14.00/M). Actual costs per request depend on reasoning depth configuration.
90% discount on repeated system prompts and reference documents. This makes large-context workflows with stable system prompts substantially cheaper than per-token pricing suggests.
50% discount for non-time-sensitive workloads, bringing effective costs to $0.875 input and $7.00 output per million tokens. Ideal for bulk content processing, data extraction, and overnight batch jobs.
Pro pricing ($21/$168 per 1M) is 12x more expensive than Instant/Thinking for output tokens. Reserve it for tasks where the reduced error rate has quantifiable business value, such as legal analysis or financial modeling.
Model Retirement Timeline: What Is Being Removed
On January 29, 2026, OpenAI announced the retirement of six models from ChatGPT, effective February 13, 2026. The decision follows a clear adoption pattern: OpenAI reports that only 0.1% of daily ChatGPT users still actively select GPT-4o. The vast majority have already migrated to GPT-5.2 organically. For full migration guidance, see our GPT-4o retirement migration guide.
Flagship GPT-4 variant
Instruction-following update
Lightweight GPT-4.1
Compact reasoning model
Fast-response GPT-5 mode
Deep-reasoning GPT-5 mode
December 11, 2025 — GPT-5.2 Launched
Three modes (Instant, Thinking, Pro) released to paid ChatGPT plans and API developers. Marked a generational leap over GPT-5.1 with 400K context and August 2025 knowledge cutoff.
January 14, 2026 — GPT-5.2-Codex Released
Specialized coding variant with context compaction, long-horizon task handling, Windows support, and state-of-the-art cybersecurity capabilities deployed to all Codex surfaces.
January 29, 2026 — Retirement Announced
OpenAI publicly announces the removal of six older models from ChatGPT, giving users approximately two weeks to prepare for the transition.
February 13, 2026 — ChatGPT Models Removed
All six models permanently removed from ChatGPT. Existing conversations and custom GPTs automatically default to GPT-5.2. No API changes at this time.
Migration Strategies for Teams and Developers
Whether you are a ChatGPT power user, an API developer, or a business leader planning AI adoption, the migration from older models to GPT-5.2 follows predictable patterns. The strategies below are organized by role and urgency.
- Test common workflows with GPT-5.2 before the deadline
- Export important conversation histories you want to retain
- Review and update custom GPT instructions for compatibility
- Configure Personality settings to match your preferred communication style
- Identify which mode (Instant, Thinking, Pro) fits each of your use cases
- Audit hardcoded model references across your codebase
- Deploy GPT-5.2 in staging and run your evaluation suite
- Implement model selection via configuration, not code
- Benchmark latency, output quality, and token costs
- Evaluate GPT-5.2-Codex for agentic coding workflows
Change only the model parameter when first testing GPT-5.2. Keep all prompts, system messages, and parameters identical. This isolates model-level differences from prompt-level changes, preventing you from attributing model behavior differences to unrelated prompt edits.
GPT-5.2 defaults to no reasoning unless configured. If your GPT-4o workflows relied on the model working through complex problems step by step, you must set reasoning effort to medium, high, or extra high in the API. In ChatGPT, select the Thinking mode and choose the appropriate effort level (Standard, High, or Extra High).
Where GPT-4o required truncating or summarizing large inputs to fit within 128K tokens, GPT-5.2 can process over 3x more context. Include complete documents, full brand guidelines, and detailed reference material without compression. This single change can significantly improve output quality for complex tasks.
Abstract model selection behind configuration layers. Store model identifiers in environment variables or configuration files rather than hardcoding them. Implement evaluation pipelines that can test new models against your quality benchmarks automatically. This ensures future model transitions require a config change rather than a code deployment.
The pace of model releases is accelerating across all major providers. Organizations that invest in model-agnostic architecture now will absorb future transitions with minimal disruption. Those that couple their workflows tightly to specific model behaviors will face repeated migration costs. If your team needs guidance on building resilient AI integrations, our AI and digital transformation services are designed for exactly this kind of strategic planning.
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