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Summary
GPT-5 (high) from OpenAI scored 42.5 on Terminal-Bench (Terminus-2), placing it top 10 (rank 10 of 21) on this benchmark. This places the model in the below-frontier band for Terminal-Bench (Terminus-2). Acceptable for cost-sensitive workloads or as part of a multi-model ensemble. Not a default choice for high-stakes routing.
Model details
- Provider: OpenAI
- Model key:
openai/gpt-5-high - Context length: 400,000 tokens
- License: Proprietary
- Open weights: no
Benchmark methodology
Benchmark goal: Evaluate the LLM's ability to solve a diverse set of programming and reasoning tasks.
Scoring metrics:
- Score: Binary success (1) or failure (0) based on task completion and correctness.
- Duration: Time taken to complete the task (in seconds).
Analysis
Key takeaways:
- The LLM demonstrates proficiency in completing individual programming and environment setup tasks, but struggles with complex, multi-stage problems.
- Planning and Robustness in adapting to unseen dependencies remain significant challenges.
- The model shows inconsistent performance in code generation tasks, failing to develop robust algorithms or adapt to environmental constraints.
Failure modes observed
Common failure modes:
- Failure to follow complex instructions involving multiple tools and configurations.
- Incomplete or incorrect code generation leading to non-functional solutions.
- Not able to plan multistep task to arrive at expected goal (e.g. Creating a website profile from info.md)
- Not able to reengineer binaries with exact same behavior
- Dependency errors: Not able to fix fasttext installation
Example: Failing to build a complete end-to-end system, such as setting up a Git server with deployment or creating a fully functional web scraper. In the Linux Kernel building, the agent fails to follow the custom configuration.
Secondary metrics
- Readability score: 0.0
- Toxicity score: 0.000
- Ethics score: 0.000
Run this evaluation yourself
LayerLens evaluates GPT-5 (high) continuously across 11+ benchmarks. To replicate this Terminal-Bench (Terminus-2) evaluation on your own model, traces, or a different benchmark configuration, open the model in $LayerLens/$.
Source: LayerLens evaluation 68fff48651dd2ed6199046b2. Updated 2025-10-28.