There's only one published coding benchmark where GPT-5.5 meaningfully leads Composer 2.5, and it leads by 13 points. That's Terminal-Bench 2.0 — the benchmark for shell-driven autonomous task completion — at 82.7% for GPT-5.5 against Composer 2.5's 69.3%. On almost every other coding axis, the cheaper model leads. The interesting question is what that one gap actually means for the workloads you ship.
This is the head-to-head, framed around that single asymmetry. For the underlying Composer 2.5 release detail, the original builder's guide is the reference; for GPT-5.5's broader capabilities, what changed in GPT-5.5 is the predecessor. This piece is about where the two collide.
The Benchmark Picture: Where Each Leads
The clean comparison, side by side:
| Benchmark | Composer 2.5 | GPT-5.5 | Winner |
|---|---|---|---|
| Terminal-Bench 2.0 | 69.3% | 82.7% | GPT-5.5 by 13 points |
| SWE-Bench Multilingual | 79.8% | 77.8% | Composer 2.5 by 2 |
| CursorBench v3.1 (default) | 63.2% | 59.2% | Composer 2.5 by 4 |
| Per-task cost (typical run) | ~$0.07 (standard) / ~$0.44 (fast) | ~$4.82 (xhigh) | Composer 2.5 by 10–60× |
Two things to internalise. First, the asymmetry is real but narrow — GPT-5.5 leads decisively on one specific category (shell-driven autonomous work) and trails everywhere else that has been independently measured. Second, the cost ratio is bigger than the vs-Opus comparison: 10–60× depending on tier, against ~14× for the Opus 4.7 head-to-head. That moves the routing decision sharper in one direction.
The Terminal-Bench Gap, Examined
What Terminal-Bench Actually Measures
Terminal-Bench is the benchmark for "can the model autonomously complete real-world shell-driven tasks in a Linux environment over many tool calls." The tasks span infrastructure (debugging a misconfigured service), data work (cleaning and querying logs), build systems (figuring out why a CI run failed), and incident-style work (tracing a problem from symptom to root cause through shell commands).
Crucially, it measures the whole task chain — not single-shell-command accuracy, but the ability to plan a multi-command investigation, interpret intermediate output, course-correct on errors, and arrive at a working final state. The 13-point gap shows up in exactly the place that's most visible: long shell investigations with branching paths.
Why GPT-5.5 Leads
This isn't surprising in retrospect. OpenAI's Codex line has been deeply tuned on terminal-mediated work since well before the agentic-coding wave arrived, and the harness optimisations (file I/O behaviour, shell-command argument parsing, error-message interpretation) have years of refinement behind them. GPT-5.5 inherits that lineage.
Composer 2.5 was trained inside Cursor's harness with a strong bias toward in-IDE tool-call behaviour — file edits, code application, project navigation. Shell-mediated investigation is a different muscle, and Cursor's training distribution underweights it relative to OpenAI's. The architecture is good; the training signal for that specific category isn't where OpenAI's is.
What the Gap Means in Practice
Concretely, in the workloads I've shipped:
- Debugging a flaky CI run through shell investigation: GPT-5.5 reaches root cause more reliably. Composer 2.5 often arrives at the right answer but takes more iterations and occasionally loses the thread.
- Multi-step log analysis: GPT-5.5 leads. The benchmark gap shows up here clearly.
- Infrastructure debugging (Kubernetes, network issues, permission problems): GPT-5.5 is materially better on the first pass. Composer 2.5 needs more hand-holding.
- DevOps automation (write a script, run it, interpret output, refine): GPT-5.5 wins on first-iteration quality.
In each of these, the question is whether the 13-point capability gap is worth the 10–60× cost difference. The answer depends on volume and stakes; the right answer is "sometimes."
Where Composer 2.5 Leads (And Where the Cost Ratio Decides)
Outside the terminal category, the picture inverts. Composer 2.5 leads on SWE-Bench Multilingual (79.8 vs 77.8) and CursorBench v3.1 (63.2 vs 59.2 default), and the leads compound:
- Multi-file refactors inside an IDE: Composer 2.5 leads — this is exactly the workload Cursor optimised for.
- In-editor code completion and chat-mode coding: Composer 2.5 leads on latency too (the 4× speed advantage of the underlying Flash-tier architecture).
- Long agent loops within a project: Composer 2.5's textual-feedback RL training shows up here. The building-reliable-AI-agent patterns I've written about — particularly tool-use-over-reasoning architectures — pair more naturally with Composer 2.5's harness behaviour.
- High-volume batch coding work: the cost ratio decides this category regardless of capability gap.
For each of these, GPT-5.5 is not bad; it's that Composer 2.5 is better on the benchmark, comparable or better on real workloads, and 10–60× cheaper. The decision is forced.
The 60× Cost Ratio
The headline cost ratio is wider than the vs-Opus comparison because GPT-5.5's xhigh reasoning tier is unusually expensive per task — published numbers land around $4.82 per task on CursorBench, against ~$0.07 for Composer 2.5 at the standard tier or ~$0.44 at the fast tier. That works out to roughly 10× cheaper if you're comparing against the fast tier where latency is comparable, and roughly 60× cheaper if you're comparing against the standard async tier.
Two things to keep in mind:
- The 60× figure is real but specific. It compares Composer 2.5's standard async tier (cheap and slow) against GPT-5.5's xhigh interactive tier (expensive and fast). For interactive workloads where latency matters, the apples-to-apples figure is closer to 10× — still substantial, but very different math.
- The cost ratio compounds with iteration count. Long agent loops multiply per-step cost by step count. The wider the ratio, the more dramatically a long loop's bill differs between the two models. On a 30-step loop, the difference between 60× and 10× cost ratios shows up as a literal order of magnitude on the monthly invoice.
The general framework that applies to broader cheap-frontier-model comparisons — the one I wrote about for Gemini 3.5 Flash — applies here too: the cost-vs-capability boundary keeps moving as the cheap tier improves, and most teams' routing logic lags behind by a quarter.
Routing Playbook
The decision tree I'm running:
Shell-heavy work to GPT-5.5
If the agent's daily work is more than ~30% shell-driven — terminal investigations, infrastructure debugging, log analysis, DevOps automation — route those workloads to GPT-5.5. The 13-point gap is real enough on this category to outweigh the cost ratio for most use cases.
The exception is high-volume DevOps automation where the cost compounds aggressively. If you're running thousands of shell-mediated tasks a day, the eval gap closes (your evals will show you which model gets to the right answer reliably enough at your specific workload) and the cost ratio dominates anyway. Test both at your volume before pinning.
Everything else to Composer 2.5
In-IDE refactoring, multi-file changes, agent loops on project work, batch processing of repository-level tasks, code review, test scaffolding, documentation generation. The benchmark gap favours Composer 2.5 modestly; the cost ratio favours it dramatically. Default to it. If you haven't yet, start with the Composer 2.5 builder's guide for the production patterns that make the cost ratio actually hold.
Mixed workloads: route per task
If your production system handles a mix of categories (shell-heavy and project-heavy), the right answer is per-task routing, not per-system pinning. A router that looks at the task category and chooses the model accordingly captures the right trade-off on every request. This is the engineering investment that pays back fast when the cost ratio is this wide.
What's Not Changed
The unchanging caveats:
- Both models still hallucinate. Less than they did, but the validation discipline doesn't change.
- Benchmarks are directional. Composer 2.5's numbers come from Cursor's harness; GPT-5.5's are self-reported by OpenAI. Run your own evals at your specific workload before re-routing real traffic.
- GPT-5.5 will improve. Composer 2.5's lead on SWE-Bench and CursorBench is current-state, not permanent. Re-benchmark quarterly.
- OpenAI's tier structure is unstable. The 60× headline depends on the xhigh tier pricing that OpenAI may revise. Treat the cost ratio as accurate today and re-check on each meaningful release.
The Practitioner's Take
The single most-quoted line in this whole comparison is "Composer 2.5 wins everywhere except Terminal-Bench." That's directionally true and tactically misleading. Terminal-Bench correlates with a real category of work (shell-driven autonomous tasks) that some teams do a lot of and some teams almost never do. The gap is real for teams in the first category and irrelevant for teams in the second.
The right move this quarter is to measure your own task distribution — what percentage of your agent's daily work is actually shell-heavy versus project-heavy — and let that measurement decide. The teams routing all of their work to GPT-5.5 because of the Terminal-Bench gap are leaving most of the cost savings on the floor. The teams routing all of it to Composer 2.5 because of the cost ratio are paying for that with degraded quality on the one workload category where the gap matters.
The interesting engineering work isn't picking a winner. It's the routing layer that picks per task.