GPT-6.1 Terra vs. Claude Opus 5.5: Cheaper and faster in three tests at max effort

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Direct benchmark comparison of latest AI models, highly relevant for AI/ML practitioners.

AI/ML thenewstack.io

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Why It Matters

For a solutions architect evaluating AI agents in SDLC and data engineering workflows, this cost-performance trade-off directly impacts decisions on model selection for automated coding, testing, and data analysis pipelines.

Key Takeaways

Benchmark Terra against Opus on your specific agentic tasks—especially those involving numerical computation or large datasets—before committing to a cheaper model that may sacrifice accuracy.

GPT-6.1 Terra vs. Claude Opus 5.5: Cheaper and faster in three tests at max effort

Summary

OpenAI's GPT-6.1 Terra claims near-parity with Anthropic's Claude Opus 5.5 on scientific coding and computer-use benchmarks at a fraction of the cost—$1.01 per task vs. $8.82 on Agents' Last Exam, with a 3.2-point score gap. Independent testing by The New Stack found a 50–50 tie on a CI audit task, but Terra showed weaknesses on numerical totals and incorrect answers in large-dataset analysis. At standard API rates, Terra costs $1/$5 per million input/output tokens versus Opus's $4/$20, though actual task cost depends on token usage and caching.

Author

Jessica Wachtel