GPT-6.1 Terra vs Claude Sonnet 5.5

No shared benchmark settles quality between GPT-6.1 Terra and Claude Sonnet 5.5, but GPT-6.1 Terra cuts standard token pricing in half. At Layer3Labs, we build and operate document and workflow automation inside other businesses, where the per-token rate sets what each job costs to run. On October 26, 2026, OpenAI introduced GPT-6.1 Terra as an efficient model combining detailed visual reasoning and precise computer use with clearer writing. GPT-6.1 Terra is available in ChatGPT Work, Codex and the developer Application Programming Interface (API). In the API it costs $1.00 per million input tokens and $5.00 per million output tokens, against $2.00 and $10.00 for Claude Sonnet 5.5.

The choice comes down to token price against any Claude tooling you already run. OpenAI's example jobs for GPT-6.1 Terra include computer use and form filling. They also include entering invoice details into accounting software. Anthropic reports Claude Sonnet 5.5 is strongest at well-scoped everyday tasks, fixing bugs, documents, slides, and spreadsheets. On Terminal-Bench 4.0, vendor scores favor Sonnet 5.5 (70.6 percent versus 50.3 percent). Each vendor ran its own test setup.

Under 272,000 tokens, GPT-6.1 Terra's standard rates are half of Sonnet 5.5's. If your stack, evals or tooling already run on Claude, stay on Sonnet 5.5.

GPT-6.1 Terra vs. Claude Sonnet 5.5: Side-by-Side

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Which to Pick: GPT-6.1 Terra or Claude Sonnet 5.5

Pick GPT-6.1 Terra to cut token costs. It suits office tasks, accounting data entry, and computer-use agent loops. With standard input rates at $1.00 per million tokens and cached input at $0.05 per million, OpenAI prices GPT-6.1 Terra at exactly 50 percent of Claude Sonnet 5.5 across both base and cached tokens. Terra is available in ChatGPT Work and Codex. It is on Plus, Pro, Business, Enterprise and Edu plans.

Pick Claude Sonnet 5.5 if your engineering pipeline depends on agentic coding, command-line terminal tasks, or existing Anthropic prompt templates. On Terminal-Bench 4.0, Anthropic reports a 70.6 percent success rate for Claude Sonnet 5.5, while OpenAI reports 50.3 percent for GPT-6.1 Terra at maximum reasoning effort. If your production systems already rely on Claude prompts, evaluations, or tooling, stay on Sonnet 5.5.

GPT-6.1 Terra offers a context window of 1,050,000 tokens, with up to 922,000 input and 128,000 output tokens. Inputs above 272,000 tokens double its standard input price to $2.00 per million. If your prompts run past 272,000 input tokens, price both models on your real payload sizes before you pick.

Token Prices and Modeled Costs Side by Side

On inputs up to 272,000 tokens, GPT-6.1 Terra costs exactly half as much as Claude Sonnet 5.5 on standard input, cached reads and output. According to the OpenAI API pricing page, GPT-6.1 Terra costs $1.00 per million input tokens, $0.05 per million cached input tokens, and $5.00 per million output tokens for contexts up to 272,000 tokens. The Anthropic pricing page lists Claude Sonnet 5.5 at $2.00 per million input tokens, $0.10 per million cache reads, $2.50 per million five-minute cache writes, and $10.00 per million output tokens. OpenAI lists Terra cache writes at $1.25 per million tokens for 30-minute retention and $2.50 for 12-hour retention, so cache-write comparisons require matching retention periods.

In our internal cost analysis, we priced three hypothetical production workloads at each vendor's published standard rates. The formula is cost = tasks × (uncached input tokens × input rate + cached input tokens × cached rate + output tokens × output rate) ÷ 1,000,000. The token counts are assumptions we chose for illustration. In the first scenario, processing 1,000 invoice entries at 3,000 input tokens and 500 output tokens totals $5.50 on GPT-6.1 Terra compared to $11.00 on Claude Sonnet 5.5. In the second scenario, drafting 10,000 customer support replies with 2,000 input tokens (where 60 percent is a cached system prompt) and 400 output tokens totals $28.60 on GPT-6.1 Terra compared to $57.20 on Claude Sonnet 5.5. In the third scenario, conducting long-document reviews across 100 files at 200,000 input tokens and 4,000 output tokens totals $22.00 on GPT-6.1 Terra compared to $44.00 on Claude Sonnet 5.5.

These token-only estimates assume the stated cache reads hit. They exclude cache writes, cache warmup, tool fees, retries, and taxes. Failed cache reads increase cost.

Per-token price does not always equal final per-task expense. In the invoice scenario, GPT-6.1 Terra costs the same as Claude Sonnet 5.5 if GPT-6.1 Terra produces 3.2 times as many output tokens for the same task. Above that ratio, Sonnet 5.5 is cheaper. GPT-6.1 Terra has six reasoning effort levels, from none to max, and reasoning tokens grow with the level you choose. Above 272,000 input tokens, GPT-6.1 Terra moves to $2.00 input, $0.10 cached input and $7.50 output, and that input rate equals the $2.00 Anthropic lists for Sonnet 5.5. Anthropic reports that, in its own testing, Claude Sonnet 5.5 costs up to 30 percent less per task than Claude Sonnet 5.

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Benchmark Results Each Vendor Reports

Vendor-reported benchmarks highlight distinct strengths for each model, but the numbers come from different test setups and cannot be treated as a head-to-head comparison. In the announcement of GPT-6.1 Terra, OpenAI reported that on Agents' Last Exam, its computer-use benchmark of professional tasks, GPT-6.1 Terra at medium reasoning effort scored 15 percentage points higher than GPT-5.6 Terra at about one-third the cost. OpenAI also reported that at maximum effort, GPT-6.1 Terra is over three times as token efficient as Claude Opus 5.5 and runs at about nine times lower cost per task. Artificial Analysis independently recorded GPT-6.1 Terra's cost per task at $0.40 at maximum effort, compared to $1.40 for GPT-5.6 Terra.

Both labs publish Terminal-Bench 4.0 figures from separate setups. Anthropic reports that Claude Sonnet 5.5 achieved 70.6 percent on Terminal-Bench 4.0, surpassing Claude Opus 5.5 at 66.4 percent. OpenAI reported that GPT-6.1 Terra achieved 50.3 percent on Terminal-Bench 4.0 at maximum effort, more than doubling GPT-5.6 Terra's 21.5 percent mark. OpenAI stated explicitly that for the most complex coding tasks, GPT-6.1 Sol and GPT-6 Astra remain the better choice. Artificial Analysis reported that GPT-6.1 Terra scored 48 on its Coding Agent Index v2.0, trailing GPT-6.1 Sol at 54 but well above GPT-6 Luna at 31.

Desktop automation and document benchmarks further illustrate different evaluation criteria. On OSWorld 2.1, which tests agents on desktop software tasks, Anthropic reports an 80.1 percent score for Claude Sonnet 5.5 using partial-credit scoring. OpenAI reported that GPT-6.1 Terra surpassed GPT-5.6 Terra's best score at medium effort for a fraction of the cost, but OpenAI published no exact score. On MMMU-Pro visual reasoning, OpenAI reported that GPT-6.1 Terra outperformed GPT-5.6 Terra at every reasoning setting. Anthropic reports a GDPval-AA v2.1 score of 1844 for Sonnet 5.5, against 1846 for Opus 5.5. None of the results above scores both models in the same run.

When Claude Sonnet 5.5 Fits Better for Production Workloads

Claude Sonnet 5.5 fits engineering teams focused on agentic coding and office deliverables. Anthropic reports that Claude Sonnet 5.5 is strongest at well-scoped everyday tasks, fixing software bugs, and generating documents, slides, and spreadsheets. On Terminal-Bench 4.0, Anthropic's reported 70.6 percent score for Claude Sonnet 5.5 is higher than OpenAI's reported 50.3 percent for GPT-6.1 Terra, though the two runs used different setups.

Stay on Claude Sonnet 5.5 if your stack, evals or tooling already run on Claude. Re-test your prompts before any move, because neither vendor publishes data on prompt compatibility. Furthermore, Anthropic reports that Claude Sonnet 5.5 runs 30 percent faster than Claude Sonnet 5.

When GPT-6.1 Terra Fits Better for Scaled Automation

GPT-6.1 Terra fits best in high-frequency office automations where input volume is massive and operating budgets are constrained. OpenAI highlights practical operational tasks for the model, such as drafting proposals, reading incoming invoices and entering details into accounting software, filling out forms, interpreting technical drawings, and editing videos. Because the base API input price is $1.00 per million tokens and cached input is $0.05 per million, high-volume jobs cost half as much per token as on Claude Sonnet 5.5, for inputs up to 272,000 tokens.

According to 9to5Mac, Terra was left out of the September GPT-6 release and is now upgraded from GPT-5.6 to GPT-6.1. OpenAI makes it available to Plus, Pro, Business, Enterprise and Edu users in ChatGPT Work and Codex. OpenAI notes that in ChatGPT Work and Codex, the same usage allowance covers more tasks with GPT-6.1 Terra than with GPT-5.6 Terra, and substantially more than with GPT-6.1 Sol or GPT-6 Astra. Developers accessing the model through the OpenAI gpt-6.1-terra model documentation can use computer use, file search, web search, code interpreter, hosted shell, and Model Context Protocol (MCP) via the Responses API.

OpenAI reports lower factual error rates for GPT-6.1 Terra than for GPT-5.6 Terra. On OpenAI's internal benchmark of error-inducing prompts, factual error rates fell from 53.6 percent on GPT-5.6 Terra to 16.7 percent on GPT-6.1 Terra at low reasoning effort, and dropped to 4.3 percent at maximum effort. Artificial Analysis recorded an AA-Omniscience hallucination rate of 38 percent for GPT-6.1 Terra compared to 88 percent for GPT-5.6 Terra. In the OpenAI system card addendum, OpenAI confirmed that GPT-6.1 Terra meets the Critical threshold in cybersecurity and High for biological and chemical capability, using the same safeguard stack as GPT-6.1 Sol and GPT-6 Astra.

How to Use GPT-6.1 Terra and Claude Sonnet 5.5

To use GPT-6.1 Terra and Claude Sonnet 5.5 effectively, start by choosing an interface that exposes the exact model you need for your workload. Test a new configuration on a single representative task, such as drafting a routine email or parsing a short document, before expanding to full automation. Inspect the saved output directly to confirm formatting, accuracy, and adherence to your prompt instructions.

For coding tasks, Cursor serves as an editor option when the target model appears on its supported list. Check current model availability before setup to confirm the exact model is listed. Once connected, save your prompts and review the generated diffs locally before applying changes to your main codebase.

The Verdict

GPT-6.1 Terra wins on token price: on inputs up to 272,000 tokens, its standard and cached rates are half of Claude Sonnet 5.5's. In our modeled invoice, support and document-review workloads, GPT-6.1 Terra cost half as much as Sonnet 5.5.

Claude Sonnet 5.5 fits agentic coding, command-line terminal workflows, and teams with existing Claude prompts and evaluation frameworks. On Terminal-Bench 4.0, vendor figures favor Sonnet 5.5 (70.6% vs 50.3%). The two runs used different setups.

This verdict would change if an independent benchmark scores both models in the same run, or if either vendor changes pricing. It would also change if GPT-6.1 Terra needs more than 3.2 times Sonnet 5.5's output tokens on jobs like the invoice scenario. Until shared benchmarks emerge, pick GPT-6.1 Terra for high-volume office automation. Choose Sonnet 5.5 for agentic engineering.