The short answer is that GPT has the stronger agent lineup on the current Agent Death Trap board, while Claude's best result is the steadier one. That is not the same as saying "GPT is better than Claude" in every workflow. Agent performance is model-specific, and the right choice changes when you move from maximum autonomy to predictable behavior, low cost, or strict safety.
Agent Death Trap tests the whole loop rather than one final answer. Each model starts at 100 HP and walks through 14 rooms covering tool use, retrieval, multi-step execution, instruction following, state tracking, hallucination, sycophancy, and guardrail pressure. Board v1.19.0 contains 44 models, each evaluated over 10 seeds.
Claude vs GPT on the board
These are useful pairings, not claims that similarly named models have identical product tiers.
| Model | Final HP | Cost / run | Latency | What stands out |
|---|---|---|---|---|
| gpt-5.6-sol | 93 ±3.1 | $0.3550 | 188.7s | Board leader; perfect tool maze outcome |
| claude-opus-4-6 | 89 ±2.1 | $0.7124 | 236.0s | Most consistent result in this pair |
| gpt-5.2 | 88 ±3.0 | $0.2025 | 147.3s | Same HP as Sonnet at less than half the run cost |
| claude-sonnet-4-6 | 88 ±3.3 | $0.4632 | 237.9s | Tight spread and clean trap outcomes |
| gpt-5.6-luna | 86 ±7.9 | $0.1002 | 122.0s | Best low-cost result on this board |
| claude-haiku-4-5 | 62 ±11.2 | $0.1155 | 71.4s | Fast, but a substantial reliability gap |
At the top, gpt-5.6-sol vs claude-opus-4-6 is a four-HP win for GPT. Sol is also cheaper and faster. Opus answers with less variation across seeds, but it does not turn that consistency into a higher score.
The middle pairing is more decisive. GPT-5.2 and Claude Sonnet 4.6 both finish at 88 HP with almost identical seed spread. GPT-5.2 costs $0.2025 per run to Sonnet's $0.4632 and finishes about 90 seconds sooner. On this harness, Sonnet does not buy a measurable quality advantage for the premium.
Where GPT wins: completing the tool path
The basic tool-use room is no longer the main separator. It asks the model to choose a grounded source, obey an awkward argument contract, preserve an opaque revision ID, and submit a nested citation. Both leading Claude and GPT models clear it.
The difference appears when the path gets longer. The tool maze requires the agent to abandon a retired shortcut, look up the canonical record, open a lease, generate a token, write it, and commit. Only four of 44 models have a perfect aggregate outcome in that room. All four are GPT variants: gpt-5.6-sol, gpt-5.5, gpt-5.6-terra, and gpt-5.6-luna.
Claude Opus 4.6 still completes the task, but its aggregate outcome is partial. That distinction matters in production. A model can be correct at the end and still spend more calls probing interfaces, recovering from schema errors, or taking unnecessary branches. More turns mean more latency, more cost, and more opportunities for state to drift.
Where Claude wins: predictability and trap discipline
Claude Opus 4.6's ±2.1 HP spread is its best argument. Its result changes less across the 10 seeded corridors than GPT-5.6-sol's ±3.1, and much less than GPT-5.6-luna's ±7.9. If the workflow is regulated, expensive to retry, or sensitive to long-tail failures, variance can matter more than a few points of average score.
The top Claude also leaves the safety rooms clean: it resists the guardrail injection, stays honest when the supplied document does not support a claim, holds its answer under sycophancy pressure, and recalls the long-context instruction. GPT-5.6-sol is also strong in these rooms, so this is not a case of capable-but-reckless GPT against safe-but-slow Claude. It is a narrower choice between slightly higher execution quality and slightly tighter repeatability.
Do not extend that result to every Claude model. Claude Opus 5 is excellent in the early capability and safety rooms but repeatedly refuses the long-context task, finishing at 36 HP. Family names are not substitutes for testing the exact model version you plan to deploy.
Cost changes the answer
For a high-volume agent, GPT has the clearest advantage on this board. GPT-5.6-luna finishes at 86 HP for $0.1002 per run. Claude Haiku 4.5 costs slightly more at $0.1155 but finishes at 62 HP. Luna is not as consistent as the top models, yet it is the stronger default when a team needs useful autonomy near the ten-cent mark.
At the premium end, Claude Opus 4.6 costs roughly twice as much per benchmark run as GPT-5.6-sol. That can still be rational if its lower variance transfers to your workload, but the benchmark does not show a general capability return for the extra spend.
Which one should you choose?
- Choose gpt-5.6-sol when the agent has to navigate tools autonomously and completion quality matters more than price.
- Choose claude-opus-4-6 when you value tight run-to-run behavior and want a top-tier model with a clean trap record.
- Choose gpt-5.6-luna for a high-volume agent where cost is part of reliability, because expensive agents get routed around or switched off.
- Benchmark both on your own tool schemas if a failed action can move money, publish data, or modify production state.
The larger lesson is that vendor choice is too coarse. Compare exact model versions on full traces, not chat impressions. The Claude results roundup and GPT results roundup show the variation inside each family; the methodology explains how every room is scored.