OpenAI released ChatGPT 5.5 on April 23, 2026 – just six weeks after GPT-5.4 – and it immediately reclaimed the top spot on 14 benchmarks. The model scores 82.7% on Terminal-Bench 2.0, uses 40% fewer tokens than its predecessor, and ships with a 1M-token context window. In this guide, we’ll cover where GPT-5.5 beats Claude, the best prompts to unlock its full capability, and how to run it as a Codex agent.
ChatGPT 5.5 Benchmarks vs Claude Opus 4.7 Benchmarks
Claude Opus 4.7 released on April 16, 2026, and briefly held the coding crown with 64.3% on SWE-bench Pro. One week later, ChatGPT 5.5 responded with stronger numbers across nearly every agentic category.
| Benchmark | GPT-5.5 | Claude Opus 4.7 | Gemini 3.1 Pro |
|---|---|---|---|
| Terminal-Bench 2.0 | 82.7% | 69.4% | 68.5% |
| GDPval (Knowledge Work) | 84.9% | – | – |
| OSWorld-Verified (Computer Use) | 78.7% | – | – |
| FrontierMath Tier 4 | 35.4% | ~17% | – |
| BrowseComp | 90.1% | – | – |
| MRCR v2 @ 1M tokens | 74.0% | 32.2% | – |
| SWE-bench Pro (Code Gen) | – | 64.3% | – |
The verdict: GPT-5.5 leads in agentic coding, computer use, and long-context retrieval. Claude Opus 4.7 holds an edge in pure code generation and reasoning without tools. Neither model wins everything.
Key Capabilities: What’s Actually New
1M-Token Context Window
The API ships with a 1M-token context window – the first from OpenAI at this scale. Long-context retrieval doubled: MRCR v2 at 1M tokens jumped from 36.6% (GPT-5.4) to 74.0%.
Practical use: Feed entire codebases, multi-document contracts, or 6-month data archives in a single call.
ChatGPT 5.5 Thinking Mode
A dedicated reasoning layer inside ChatGPT that uses extra compute before responding. It’s available to all paid tiers. Performance on Expert-SWE (long-horizon coding with a median 20-hour human completion time) is notably stronger here.
ChatGPT 5.5 Pro
Reserved for Pro ($200/month), Business, and Enterprise users. Early testers describe significantly more comprehensive, well-structured outputs – especially in legal, financial, education, and data science workflows.
Native Omnimodality
ChatGPT 5.5 is the first fully retrained base model since GPT-4.5 and handles text, im
ages, code, and tools natively – not via post-training add-ons.
ChatGPT 5.5 Prompts for Best Output
ChatGPT 5.5 is optimized for agentic, multi-step work. Prompts that give it a clear goal, tools to use, and an explicit success condition get the best results.
For Agentic Coding Tasks
You are a senior engineer. Analyze this codebase, identify the root cause of [error],
propose a fix, implement it, and write a test that confirms it. Explain your reasoning
at each step. Do not stop until the test passes.
For Long-Context Document Analysis
Here are [N] documents. Identify all conflicting clauses, rank them by legal risk,
and produce a structured summary with page references. Flag anything that requires
human legal review.
For GDPval-Style Knowledge Work
Act as a [role: analyst / CFO / researcher]. Given this dataset: [paste data].
Produce: (1) a 3-statement financial model, (2) a risk matrix, (3) a board-ready
one-page summary. Format as a structured report.
For Computer Use Tasks (ChatGPT Agent)
Open [application]. Navigate to [section]. Extract all rows where [condition is true].
Export to a spreadsheet. Summarize the output in 5 bullet points.
Pro Tip: Trigger Thinking Mode
Add "Use maximum reasoning effort before responding." at the start of any high-stakes prompt. This activates GPT-5.5’s internal verification loop.
OpenAI ChatGPT 5.5 Agent: How to Use ChatGPT 5.5 Codex
What Codex Does Now
GPT-5.5 is the default model in OpenAI’s Codex product – both the CLI tool and the web interface. Codex is no longer just autocomplete; it’s a full agentic coding environment where GPT-5.5 can:
- 📌 Plan multi-file refactors autonomously
- 📌 Navigate a codebase, find failure points, and implement fixes
- 📌 Merge complex branches with hundreds of changes
- 📌 Run and iterate on tests without human intervention
Context Window in Codex
Codex runs on a 400K context window (vs. 1M in the API). For most production codebases under 300K tokens, this is sufficient.
How to Set Up GPT-5.5 in Codex CLI
# Install or update Codex CLI
npm install -g @openai/codex
# Set your API key
export OPENAI_API_KEY=your_key_here
# Run with GPT-5.5
codex --model gpt-5.5 "Refactor the authentication module to use JWT"
Fast Mode in Codex
Codex has a Fast Mode that runs 1.5× faster, but costs 2.5× the standard rate. Use it for time-sensitive CI/CD pipelines. Default mode is sufficient for most development work.
Real-World Example
Pietro Schirano (CEO, MagicPath) used GPT-5.5 to merge a branch with hundreds of refactor changes into main – in a single, 20-minute pass. Dan Shipper (CEO, Every) called it “the first coding model with serious conceptual clarity,” using it to debug a system failure that had previously required a full team rewrite.
Pricing & Access
| Tier | Access | Cost |
|---|---|---|
| ChatGPT Plus | GPT-5.5 Thinking | $20/month |
| ChatGPT Pro | GPT-5.5 Thinking + Pro | $200/month |
| Business / Enterprise | Both tiers | Custom |
| API – gpt-5.5 | Coming very soon | $5 / $30 per 1M tokens (in/out) |
| API – gpt-5.5-pro | Coming very soon | $30 / $180 per 1M tokens |
| Batch / Flex (API) | – | 50% off standard |
| Priority (API) | – | 2.5× standard |
Note: API pricing is double GPT-5.4’s rates ($2.50/$15 → $5/$30). OpenAI argues effective cost is ~20% higher, not 2×, due to 40% fewer tokens per task. Run your own token spend math before migrating.
Limitations
- API not live at launch. ChatGPT and Codex users have access; API rollout is pending additional safety requirements.
- Codex context cap. The 400K window in Codex is smaller than the 1M API window. Very large monorepos may still need chunking strategies.
- Price doubled. Teams optimizing for cost should benchmark GPT-5.5’s token efficiency before switching from GPT-5.4 or Claude.
- Claude still leads on raw code generation. SWE-bench Pro remains a Claude Opus 4.7 stronghold. For agentic-free coding tasks, Claude is still competitive.
- Safety-gated API deployment. Cybersecurity has been elevated to the High risk tier. Some use cases require approved access through OpenAI’s Trusted Access for Cyber program.
FAQ
Q: Does ChatGPT 5.5 beat Claude Opus 4.7 overall?
GPT-5.5 leads on agentic coding, computer use, long-context retrieval, and knowledge work (14 benchmarks). Claude Opus 4.7 still leads on pure software engineering (SWE-bench Pro) and reasoning without external tools. The “best model” depends on your specific task.
Q: What are the best prompts for ChatGPT 5.5?
Prompts that work best are goal-oriented, multi-step, and include a clear success condition. Give GPT-5.5 a messy task with tool access and let it plan rather than micro-managing every step. Adding “Use maximum reasoning effort” triggers the Thinking mode explicitly.
Q: How do I use ChatGPT 5.5 in Codex as an agent?
Install the Codex CLI, set --model gpt-5.5, and give it a high-level engineering task. GPT-5.5 will plan, execute, test, and iterate autonomously. The 400K context window in Codex handles most production codebases.
Q: Is ChatGPT 5.5 available on the free plan?
No. GPT-5.5 Thinking is available to paid ChatGPT plans (Plus, Pro, Business, Enterprise). GPT-5.5 Pro is limited to Pro, Business, and Enterprise tiers.
Q: What is GPT-5.5’s context window?
The API ships with a 1M-token context window. Codex operates at 400K. Long-context retrieval performance at 1M tokens more than doubled compared to GPT-5.4, making it suitable for entire-codebase and multi-document workflows.
Conclusion
ChatGPT 5.5 is the most capable publicly available model as of April 2026, with dominant performance in agentic coding, computer use, and long-context work. It does not eliminate Claude – Opus 4.7 holds real advantages in code generation and tool-free reasoning. The right stack depends on your workflow: use GPT-5.5 for complex, multi-step agentic tasks; keep Claude in the mix for SWE-bench-style engineering and MCP-heavy work. API pricing is double its predecessor, but token efficiency partially offsets that. Benchmark it on your own tasks before committing.
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