OpenAI拥有发布 GPT-6.1 Terra，是视觉推理，计算机使用和写作的快速模型。 它取代了GPT-5.6 Terra，并且作为gpt-6.1-terra在API中直播。 标准定价为每百万美元输入令牌1元，每百万产出令牌5元。 对于在音量上实现屏幕，表格和文档自动化的团队，GPT-6.1 Terra会改变成本数学。
TL；数据
- 大小：1.05M-托肯语境，最大输出128K。
- 运行于：OpenAI - 仅托管(API,ChatGPT Work,Codex). 闭塞重量。
- 性能：在视觉和计算机使用方面接近GPT-6.1 Sol和GPT-6 Astra，成本较低。
- 最佳：85.5%的MMU-Pro(视星等)最大功率，比GPT-6 Astra低1.2分。
- 最糟糕的是：在GPT-6.1 Sol(55.9%)和Claude Sonnet 5.5(70.6%)之后的Bench-4.0号终点站上50.3%。
GPT-6.1 Terra是什么东西？
GPT-6.1 Terra是OpenAI推理模型，为高容量的视觉和计算机使用工作而建造。 它接受文本和图像输入并返回文本。 上下文窗口为10.5万个令牌，输出令牌高达12.8万个。 其知识截止日期为2026年6月1日。
理性努力从零到最大，以中度为默认。 工具调用需要响应 API。 支持的工具包括计算机使用，主机 shell，代码解释器，网络搜索，文件搜索和MCP. 兹Q 1Z列出的使用案例包括将产品照片与目录条目匹配，并解释技术图纸。
GPT-6.1 Terra在电脑使用上表现如何？
OpenAI的主要影响测试是Agents ' Last Exam v1，这是一套专业的计算机任务。 一项任务将建筑图纸转化为3D模型。
在中度努力下，Terra的得分是57.6%，每任务0.29美元。 GPT-5.6 Terra在同一场景中以0.84的成绩得分42.6%. GPT-6 Astra也以中等成绩评分57.6%，但每任务成本4.82美元。
在最大努力，Terra达到60.0%，每个任务为1.01美元。 Claude Opus 5.5的分数为63.2%，同图每任务8.82分。 OpenAI 称 Terra完成的任务比GPT-6 Astra和GPT-6.1 Sol快3倍以上。
在OSWorld 2.1的离线集中，Terra的得分是66.7%，最高为74.2%。 GPT-5.6 Terra峰值达到57.1%. GPT-6.1 Sol达到78.0%,GPT-6 Astra达到80.9%,Claude Sonnet达到5.5 83.9%.
它的视觉推理有多强？
在MMMU-Pro(Vision)上，Terra以最大努力得分85.5%。 GPT-6.1 Sol得分86.3%,GPT-6 Astra得分86.7%. GPT-5.6 Terra出局81.7%. Terra的最大功劳是每次任务0.012美元，而Astra为0.065美元。
事实和编码是否有所改善？
在OpenAI内部的一套诱导错误的提示上，低效错误率从53.6%下降到16.7%。 最大努力率下降了18个百分点，达到4.3%。 这套设定在设计上是对抗性的，因此生产误差率会有所不同。
在终端-Bench 4.0上，Terra以最大努力解决50.3%，比GPT-5.6 Terra的21.5%有所上升。 它仍然沿着GPT-6.1 Sol(55.9%),GPT-6 Astra(58.2%)和Claude Sonnet 5.5(70.6%). OpenAI本身为最复杂的编码任务推荐Sol或Astra.
在终端-Bench-Science 0.1上，Terra以每任务4.15美元得分58.3%. 这比GPT-6.1 Sol的57.0 % 高5.47美元。 Claude Opus 5.5以23.21的反比分63.3%。
GPT-6.1 Terra花费多少，哪里有？
标准API定价为1美元输入，0.05美元缓存输入，每百万令牌输出5美元。 批量和Flex便宜50%. 快模式成本2x标准。 在2x输入和1.5x输出时提示超过272K输入令牌。
GPT-6.1 Sol的2美元/10美元和GPT-6 Astra的10美元/50美元的1/10。 Claude Opus 5.5以4美元/20美元的价格列出。
在ChatGPT中，Terra为Plus,Pro,Entertainment和Edu用户在ChatGPT Work和Codex中直播。
GPT-6.1 Terra对竞争对手
基准测试的分数和每个任务的费用从OpenAI的发布图表中读取，并自报。 标本和价格来自每个供应商的模型页。 Claude价格：Anthropic定价。
关键外卖
- GPT-6.1 Terra每百万令牌花费1/5美元，占GPT-6.1 Sol的一半。
- 符合GPT-6 Astra在Agents最后考试中57.6%的成绩
- MMMU-Pro 达到 85.5%，在Astra的1.2点之内。
- 编码仍然落后：50.3%的终端-奔驰4.0对索内5.5的70.6%。
- 所有分数均为OpenAI-报告；参数和硬件未披露。
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米哈尔·萨特(Michal Sutter)是一名数据科学专业人员，拥有帕多瓦大学的数据科学硕士学位。 在统计分析、机器学习和数据工程方面有一个坚实的基础，Michal在将复杂的数据集转化为可操作的见解方面表现突出。
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OpenAI has released GPT-6.1 Terra, a fast model for visual reasoning, computer use and writing. It replaces GPT-5.6 Terra and is live in the API as gpt-6.1-terra. Standard pricing is $1 per million input tokens and $5 per million output tokens. For teams automating screens, forms and documents at volume, GPT-6.1 Terra changes the cost math.
TL;DR
- Size: 1.05M-token context, 128K max output.
- Runs on: OpenAI-hosted only (API, ChatGPT Work, Codex). Closed weights.
- Performance: Near GPT-6.1 Sol and GPT-6 Astra on vision and computer use, at lower cost.
- Best: 85.5% on MMMU-Pro (Vision) at max effort, 1.2 points behind GPT-6 Astra.
- Worst: 50.3% on Terminal-Bench 4.0, behind GPT-6.1 Sol (55.9%) and Claude Sonnet 5.5 (70.6%).
What is GPT-6.1 Terra?
GPT-6.1 Terra is an OpenAI reasoning model built for high-volume visual and computer-use work. It accepts text and image input and returns text. The context window is 1,050,000 tokens, with up to 128,000 output tokens. Its knowledge cutoff is June 1, 2026.
Reasoning effort runs from none to max, with medium as the default. Tool calling requires the Responses API. Supported tools include computer use, hosted shell, code interpreter, web search, file search and MCP. OpenAI’s listed use cases include matching product photos to catalog entries and interpreting technical drawings.
How does GPT-6.1 Terra perform on computer use?
OpenAI’s main influential test is Agents’ Last Exam v1, a set of professional computer tasks. One task turns architectural drawings into a 3D model.
At medium effort, Terra scores 57.6% at $0.29 per task. GPT-5.6 Terra scored 42.6% at $0.84 on the same setting. GPT-6 Astra also scores 57.6% at medium, but costs $4.82 per task.
At max effort, Terra reaches 60.0% at $1.01 per task. Claude Opus 5.5 scores 63.2% at $8.82 per task on the same chart. OpenAI says Terra completes tasks over 3 times faster than GPT-6 Astra and GPT-6.1 Sol.
On the OSWorld 2.1 offline set, Terra scores 66.7% at medium and 74.2% at max. GPT-5.6 Terra peaked at 57.1%. GPT-6.1 Sol reaches 78.0%, GPT-6 Astra 80.9% and Claude Sonnet 5.5 83.9%.
How strong is its visual reasoning?
On MMMU-Pro (Vision), Terra scores 85.5% at max effort. GPT-6.1 Sol scores 86.3% and GPT-6 Astra 86.7%. GPT-5.6 Terra topped out at 81.7%. Terra’s max-effort run costs $0.012 per task, against $0.065 for Astra.
Did factuality and coding improve?
On OpenAI’s internal set of error-inducing prompts, the low-effort error rate fell from 53.6% to 16.7%. At max effort, it fell 18 points to 4.3%. The set is adversarial by design, so production error rates will differ.
On Terminal-Bench 4.0, Terra solves 50.3% at max effort, up from 21.5% for GPT-5.6 Terra. It still trails GPT-6.1 Sol (55.9%), GPT-6 Astra (58.2%) and Claude Sonnet 5.5 (70.6%). OpenAI itself recommends Sol or Astra for the most complex coding tasks.
On Terminal-Bench-Science 0.1, Terra scores 58.3% at $4.15 per task. That beats GPT-6.1 Sol’s 57.0% at $5.47. Claude Opus 5.5 with fallbacks scores 63.3% at $23.21.
How much does GPT-6.1 Terra cost, and where is it available?
Standard API pricing is $1 input, $0.05 cached input and $5 output per million tokens. Batch and Flex are 50% cheaper. Fast mode costs 2x Standard. Prompts above 272K input tokens bill at 2x input and 1.5x output.
That is half of GPT-6.1 Sol’s $2/$10 and one-tenth of GPT-6 Astra’s $10/$50. Claude Opus 5.5 lists at $4/$20.
In ChatGPT, Terra is live for Plus, Pro, Enterprise and Edu users in ChatGPT Work and Codex.
GPT-6.1 Terra vs competitors
Benchmark scores and per-task costs are read from OpenAI’s launch charts and are self-reported. Specs and prices come from each vendor’s model page. Claude prices: Anthropic pricing.
Key Takeaways
- GPT-6.1 Terra costs $1/$5 per million tokens, half of GPT-6.1 Sol.
- It matches GPT-6 Astra’s 57.6% on Agents’ Last Exam at $0.29 per task.
- MMMU-Pro hits 85.5%, within 1.2 points of Astra.
- Coding still lags: 50.3% on Terminal-Bench 4.0 vs Sonnet 5.5’s 70.6%.
- All scores are OpenAI-reported; parameters and hardware are undisclosed.
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Michal Sutter is a data science professional with a Master of Science in Data Science from the University of Padova. With a solid foundation in statistical analysis, machine learning, and data engineering, Michal excels at transforming complex datasets into actionable insights.
Auto-fetched and translated by AI Atlas. All rights belong to the original author.