Anthropic opens its books for Wall Street, showing record revenue and mounting obligations
AIvia The Decoder
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Anthropic is the first major AI lab to file an S-1 with the SEC, planning to list on the Nasdaq under the ticker "ANT." The numbers are strong, with $11.6 billion in quarterly revenue, a first-ever operating profit, and existing customers spending nine times more year over year. But the company also carries roughly $800 billion in long-term compute commitments. The article Anthropic opens its books for Wall Street, showing record revenue and mounting obligations appeared first on The Decoder.
Anthropic is the first major AI lab to file an S-1 with the SEC, planning to list on the Nasdaq under the ticker “ANT.” The numbers are strong, with $11.6 billion in quarterly revenue, a first-ever operating profit, and existing customers spending nine times more year over year. But the company also carries roughly $800 billion in long-term compute commitments.
The article Anthropic opens its books for Wall Street, showing record revenue and mounting obligations appeared first on The Decoder.
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. window.addEventListener("message",function(e){if(e.data&&e.data.mtpTerraH){var f=document.getElementById("mtp-t61-frame");if(f){f.style.height=e.data.mtpTerraH+"px";}}}); 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. Check out the OpenAI announcement, the API model page and the system card addendum. Also, feel free to follow us on Twitter and don’t forget to join our 150k+ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well. Need to partner with us for promoting your GitHub Repo OR Hugging Face Page OR Product Release OR Webinar etc.? Connect with us The post OpenAI Launches GPT-6.1 Terra, Halving GPT-6.1 Sol’s API Price for Agent Work appeared first on MarkTechPost.
OpenAI has released GPT-6.1 Terra, a mid-range model that cuts API prices in half compared to its predecessor. Analysts say it sets a new standard for intelligence per dollar, especially for everyday computing tasks and AI-driven computer control. The article GPT-6.1 Terra halves OpenAI's API prices and still gains ground on its larger models appeared first on The Decoder.
A study from New York University and the University of Texas at Austin shows that frontier language models systematically overuse linguistic patterns that are hard for humans to parse. The researchers trace the problem to training on code and math, where correctness gets rewarded but clarity doesn't. Smaller models, by contrast, write more like humans. The article Researchers find six patterns that make AI text so exhausting to read appeared first on The Decoder.
