GPT-6.1 Terra outperforms Claude Opus 5.5 in cost and speed benchmarks

OpenAI released GPT-6.1 Terra on October 26, positioning it as a high-efficiency alternative to Anthropic’s Claude Opus 5.5. Independent testing conducted shortly after launch reveals that Terra not only matches but often exceeds the performance of its competitor in specific complex workflows, all while operating at a fraction of the cost.

What happened

Following OpenAI’s claims that Terra offers superior token efficiency, independent tests were designed to evaluate the models in three demanding scenarios: release triage, scientific cell lineage tracking, and continuous integration history audits. The tests were run at maximum effort settings for both models, using their respective APIs without capping the number of interaction turns. The goal was to measure accuracy, completion rates, time-to-solution, and estimated costs under standardized conditions.

The results indicate a significant divergence in performance and economics. In the release triage test, both models achieved a perfect score, but Terra completed the task in less time and with far fewer output tokens. In the cell lineage challenge, which required writing and executing Python code to track dividing cells in microscope footage, Terra scored higher and finished in roughly half the time. Most notably, in the CI history audit involving messy data tables, Terra provided a near-perfect answer while Claude Opus 5.5 failed to produce any final result before hitting its output token limit.

How it works

The efficiency gap stems from how each model manages token usage during multi-step reasoning tasks. GPT-6.1 Terra is reported to be more than three times as token-efficient as Opus 5.5 for standard inputs and outputs. Its pricing structure reflects this, with input tokens costing $1 per million and output tokens $5 per million, compared to Opus 5.5’s $4 and $20 respectively. This means Terra would need to use four times as many tokens to match Opus’s base cost, yet in these tests, it used significantly fewer.

In practical terms, Terra’s approach involves tighter control over interaction loops. For example, in the browser-based release triage, Terra relied more on clicking interface elements rather than typing URLs repeatedly, which drastically reduced the volume of output tokens generated. Opus, by contrast, typed URLs over one hundred times, inflating its token count and cost without improving accuracy. Both models include reasoning tokens in their output totals, but Terra’s ability to reach correct conclusions with fewer intermediate steps drives its lower overall expense.

Key details

• Cost Efficiency: Across all three tests, Terra’s estimated uncached token cost was $25.99, compared to $214.96 for Opus 5.5, representing an 8.27-to-one cost ratio.
• Speed: Terra completed the combined workload in 2,418 seconds, while Opus took 3,962 seconds, saving approximately 25 minutes.
• Release Triage: Both models scored 50/50, but Terra achieved this at 17.1% of Opus’s estimated uncached token cost.
• Cell Lineage: Terra scored 48/50 and finished in 15 minutes, whereas Opus scored 43.5/50 and took 36 minutes.
• CI Audit: Terra scored 47/50 on a complex data table task; Opus failed to return a final answer within its 128,000-token output limit.
• Pricing: Terra’s standard rates are $1/million input tokens and $5/million output tokens, versus $4 and $20 for Opus 5.5.

Why it matters

For engineering teams building AI agents, these findings suggest that model selection should heavily weigh token efficiency alongside raw accuracy. A model that requires fewer turns and less verbose output to reach a correct solution can drastically reduce operational costs, especially in high-volume applications. The difference between $25 and $215 for a single complex workflow is substantial when scaled across thousands of daily tasks. Developers can now consider Terra for workloads previously reserved for more expensive models, provided the specific task aligns with its strengths in code execution and structured data handling.

However, the tests also highlight persistent challenges with large-scale data processing. While Terra completed the CI audit, it still made minor errors in counting statuses and misidentified unanswerable questions. This reinforces the need for robust validation layers in AI applications. Teams cannot rely solely on the model’s self-reported confidence or output format; external verification scripts remain essential for numerical accuracy and data integrity, regardless of the model’s sophistication.

What you can do

• Benchmark Terra against your current model using max effort settings for complex, multi-step agent tasks.
• Implement strict output validation for numerical data, as even top-tier models may miscount or misinterpret ambiguous entries.
• Monitor token usage patterns, specifically looking for excessive URL typing or verbose reasoning that drives up costs.
• Test caching strategies, as repeated inputs like screenshots can significantly lower costs if managed correctly.
• Evaluate whether your workload benefits more from Terra’s speed and cost efficiency or Opus’s potential strengths in other domains not covered here.
• Start with Terra for browser automation and code-generation tasks to leverage its demonstrated efficiency in these areas.