AI-First
GPT-5.5 landed at OpenAI in April 2026 under the codename "Spud", 2× faster, 3× fewer output tokens. Its main playground is Codex, OpenAI's agentic coding tool, the direct counterpart to Anthropic's Claude Code. For those running agents through OpenClaw, the question is straightforward: does this new model justify rethinking your stack, or is it yet another launch to wait out before extracting real value? Four side-by-side experiments with Opus 4.7 paint a more nuanced picture than the official benchmarks.
- 🔑 GPT-5.5 generates 3 times fewer output tokens than Opus 4.7 for comparable results.
- ⚠️ The price has doubled compared to GPT-5.4: check your unit costs before migrating.
- 💡 OpenClaw can orchestrate GPT-5.5 and Opus 4.7 in parallel on the same multi-agent workflow.
- 🚀 On SWE Bench Pro (real GitHub issues), Opus 4.7 keeps the lead: 64.3% vs 58.6% for GPT-5.5.
What the official benchmarks actually say
On Terminal-Bench 2.0, GPT-5.5 scores 82.7% versus 69.4% for Opus 4.7. On SWE-bench Pro, Opus takes back the lead: 64.3% vs 58.6%. In practice, GPT-5.5 dominates terminal-based system tasks; Claude Code retains the edge on resolving real GitHub bugs. The rest of this section breaks down why the two benchmarks measure fundamentally different things.
OpenAI's numbers look impressive on paper. On Terminal-Bench 2.0, GPT-5.5 scores 82.7% versus 69.4% for Opus 4.7 and 75.1% for GPT-5.4. On GDP Val, which measures an agent's ability to complete tasks across 44 real-world professions, the model reaches 84.9%. On OS World, which tests computer control (clicks, typing, navigation), GPT-5.5 hits 78.7%, above the human baseline.
Where things get complicated: SWE Bench Pro, the benchmark that solves real GitHub issues, remains Claude Opus 4.7's advantage. OpenAI didn't include it in their official comparison, which speaks volumes. The takeaway here: aggregate benchmarks don't replace testing on your specific use case.
What OpenAI is really highlighting is token efficiency. The central argument of the launch isn't "this model is better at everything" but "it does the same with less." Fewer tokens per task, fewer iterations, more autonomy on vague prompts. Perplexity validated this point internally: according to Denis Yarats, Perplexity's CTO, GPT-5.5 used 56% fewer tokens than previous models for the same production tasks.
Codex vs Claude Code: test results across four projects
Nate Herk ran four parallel experiments, one identical prompt in Codex with GPT-5.5 and in Claude Code with Opus 4.7, with no iterations. A personal branding site, a solar system simulation, a 3D space shooter game, and an ecosystem simulation. Here's what the raw numbers look like across all four projects:
| Metric | GPT-5.5 (Codex) | Opus 4.7 (Claude Code) |
|---|---|---|
| Total time (4 projects) | 20 min 49 s | 40 min 43 s |
| Input tokens | 2.7 M | 2.5 M |
| Output tokens | 70,000 | 250,000 |
| Estimated total cost | ~$12 | ~$15 |
| SWE Bench Pro | 58.6% | 64.3% (+5.7 pp) |
| SWE Bench Verified | N/A | 87.6% |
| Context window | 400,000 tokens | 1,000,000 tokens |
The output token ratio is striking. GPT-5.5 produced the same deliverables with roughly 70,000 tokens versus 250,000 for Opus. The result: twice as fast, three to four dollars cheaper across these four tests. On visual output quality, opinions diverge depending on the project: Codex won on the shooter game in terms of fluidity, Claude Code on the planetary simulation. Nothing conclusive on the design front.
One caveat worth noting: the context window caps at 400,000 tokens in Codex, versus 1 million for Claude. On projects with a large codebase or detailed system instructions, this difference can matter.
OpenClaw with GPT-5.5: the hybrid strategy
The real strength of OpenClaw in this context is that it doesn't force you to pick a single model. You can assign GPT-5.5 to execution-heavy agents (coding, scraping, data analysis) and keep Opus 4.7 on agents handling conversation, long-form writing, or CRM management. OpenAI positions GPT-5.5 as its flagship model for agentic workflows, a positioning that sits at the core of what OpenClaw orchestrates.
In practice, it looks like this: a GPT-5.5 agent runs overnight on product iterations or scheduled scraping, while an Opus 4.7 agent handles text outputs, copywriting, or content workflows. Both communicate in a Discord or Telegram group, orchestrated by OpenClaw. This setup leverages each model's strengths without locking your stack into a single provider.
For the OpenClaw skills you've already built (writing, design, business workflows), Opus remains more reliable because Claude Code's skills and projects system is more mature than its Codex equivalent. For more advanced builds or raw execution tasks, GPT-5.5 is starting to pull ahead.
Which strategy fits your profile
Pricing is the parameter you can't overlook. GPT-5.5 costs twice as much as GPT-5.4 via API: $5 per million input tokens, $30 per million output tokens. Opus 4.7 comes in at roughly the same level on input, but $5 cheaper on output. If GPT-5.5 truly uses three times fewer output tokens, the total cost tips in its favor on long-running execution tasks. On short or conversational tasks, the advantage fades.
The right question isn't "which model is best" but "for which task does which model spend less for an identical result." The creators getting the most value from GPT-5.5 today are those using it on high-frequency call workflows, where the reduction in output tokens compounds fast.
For freelancers and SMBs using OpenClaw on autonomous lead generation or CRM processes, migration isn't urgent if your Opus setup is running well. GPT-5.5 deserves testing on a specific workflow before making the call. The persistent memory and project-based configuration logic remains more accessible on the Claude Code side, which matters if your team needs to maintain the system without custom development.
Quick decision table
| Your primary need | Recommended model |
|---|---|
| Raw execution, short iterations, cost per token | GPT-5.5 in Codex |
| Complex planning, real GitHub bugs, long context | Opus 4.7 in Claude Code |
| Existing OpenClaw skills, persistent memory | Opus 4.7 as primary |
| High volume on end-to-end workflows | Hybrid OpenClaw (GPT-5.5 execution + Opus 4.7 coordination) |
The real advice: build your memory system to be portable, so it can plug into Codex or Claude Code depending on whichever model leads at any given moment. The market will keep flip-flopping between the two labs with every release. What stays stable is the architecture you control.
How to access GPT-5.5 in Codex
GPT-5.5 has been available since April 23, 2026 in Codex, the OpenAI API, and ChatGPT, with no subscription change required if you already have OpenAI API access. In Codex, the model is selectable directly in the interface. Via the API, it's called gpt-5.5 with a 1-million-token context window, versus 400,000 tokens in the Codex environment (current limit, with the OpenAI community requesting an increase).
For OpenClaw users, you simply specify gpt-5.5 as the model in an agent's configuration. The migration is non-destructive: your Opus 4.7 agents stay active in parallel until you've validated GPT-5.5's behavior on your specific workflows.
FAQ
Can GPT-5.5 be used in OpenClaw?
Yes. OpenClaw orchestrates any model accessible via API, including GPT-5.5. You can assign it to execution agents in your configuration while keeping Opus 4.7 on coordination, writing, or CRM agents.
Does Claude Code still beat GPT-5.5 on real GitHub bugs?
Yes, and the gap is measurable: Opus 4.7 scores 64.3% on SWE-bench Pro versus 58.6% for GPT-5.5 in Codex, a 5.7-point lead. On SWE-bench Verified (a broader benchmark), Opus reaches 87.6% (source). That's where Claude Code earns its place on complex projects.
Does GPT-5.5 actually end up cheaper?
On long-running, high-frequency tasks, yes: 3× fewer output tokens offsets the higher per-token rate ($30 vs $25/M output tokens). On short or conversational tasks, the advantage disappears. Test on a specific workflow before migrating your entire stack.
Why does Codex have a smaller context window than Claude Code?
GPT-5.5 in Codex is limited to 400,000 context tokens versus 1 million for Claude Code (in beta). On projects with a large codebase or detailed system instructions, this limit can force you to split tasks into subtasks, which partially negates the speed advantage.
What's the difference between GPT-5.5 via API and GPT-5.5 in Codex?
Via the API, GPT-5.5 has a 1-million-token context window. In Codex, that window is currently limited to 400,000 tokens, a product decision by OpenAI, independent of the model's own capabilities. For projects with a very large codebase, this difference may require segmenting tasks. The pricing gap is also worth noting: beyond 272,000 input tokens via API, the cost jumps to 2× the standard rate ($10/M input tokens for GPT-5.5).
Vidéos YouTube
- I Tested GPT 5.5 vs Opus 4.7: What You Need to Know · Nate Herk | AI Automation
- GPT-5.5 in 7 Minutes · Developers Digest
- Introducing GPT-5.5 with Perplexity · OpenAI
- ChatGPT 5.5 is INSANE... [I'm Switching Back] · AI Edge