Anthropic released Claude Opus 5.5, positioning it as matching Claude Fable 5.1 on most tasks. Running it on typical default workloads costs 40% less than Opus 5. The update is API-only (no weights), comes with changes like thinking no longer being switchable off, added output watermarking for the EU AI Act, and revised pricing plus higher usage limits for paid plans.
OpenAI released GPT-6 Sol and GPT-6 Luna to complement its flagship GPT-6 Astra, with no GPT-6 Terra in the lineup. GPT-6 Sol and GPT-6 Luna cut default prices to $2/$10 and $0.10/$0.50 per million input/output tokens, respectively (down from GPT-5.6 Sol’s $4/$20 and $0.20/$1.20). OpenAI’s changes mean lower token costs, improved prompt caching with up to 90% discounts for cached input, and availability in ChatGPT Work and Codex for paid tiers starting Tuesday (with a gradual rollout).
GPT-6 Sol and GPT-6 Luna were introduced as two AI models for everyday work with different capability-versus-cost tradeoffs. The article provides no concrete number, date, or price details. As a result, users have two model options tuned for different budgets and performance needs.
Anthropic released Claude Opus 5.5, its first model in the Claude 5.5 family, despite days earlier arguing for AI development to slow down. Artificial Analysis measured Opus 5.5 scoring 58 at maximum effort on its Intelligence Index, the highest result it had measured. The launch pairs that top ranking with lower token prices and added API restrictions, while leaving some benchmarks where competitors still lead.
Simon Willison posted a brief monthly digest focused on LLM updates related to Anthropic. The post’s subscription pitch asks for $10/month to get a curated email digest. No concrete LLM or model change is described—this functions mainly as a newsletter/briefing note.
Anthropic released Opus 5.5 on Tuesday, positioning it as stronger at coding and knowledge work than its larger Fable model. Opus 5.5 output tokens cost $20 per million tokens versus $25 for the previous Opus model. The update lowers pricing and compute, and changes communication behavior by using less jargon and placing key information earlier.
JS Denain of Epoch AI discusses how uncertainty around RSI and AI acceleration relates to AI risk, comparing public evidence from OpenAI and Anthropic with internal metrics they might track. He points to a reported 2X-a-month increase in Codex spending by researchers as the most striking observable sign of value, while arguing it doesn’t prove imminent self-sustaining acceleration or full automation of AI research. The conversation shifts focus from “imminent” RSI claims to slower, trackable indicators and constraints like compute and research understanding bottlenecks.
The author argues that Jev’s system avoids generating token-by-token confidence text and instead builds a shared transformer state that branches into multiple questions whose probabilities are read out directly as distributions. The key concrete detail is that Jev’s API claims to output probabilities in parallel rather than autoregressively generating by token. As a result, applications can route or decide using trained decision probabilities while shifting JSON formatting and other option handling to application code, with less repeated computation from re-processing the shared state for many questions.
The article describes the post-training self-improvement loop where a model generates candidate answers, a grader selects the good ones for training data, and the model retrains to produce better answers. It says this loop has been run at industrial scale for two years across frontier labs. As a result, the focus shifts from speculative self-modifying agents to the already-deployed post-training pipeline as the main driver of model improvements.
TestingCatalog AI News·3 weeks ago·
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SpaceXAI released Grok 4.7 as its most capable model for coding and knowledge work, adding access through Cursor, Grok Build, the Grok API, third-party coding tools, model routers, and cloud platforms. Pricing starts at $2 per million input tokens and $6 per million output tokens, with a fast variant that doubles both output speed and price. Compared with Grok 4.6, it adds longer performance on difficult tasks, more careful self-checking, improved longer-context handling, and a new safeguards stack that strengthens refusals and jailbreak resistance.
SpaceXAI released Grok 4.7, moving the company’s model into the top group of frontier labs on the Artificial Analysis Intelligence Index. Grok 4.7 scored 46 points, up 2 points from Grok 4.6, placing it fourth using the xhigh reasoning setting. The update improves agentic knowledge work and coding-agent benchmarks but increases compute to about 81,000 output tokens per task and rolls out a new safety stack and invite-only red-team access.
SpaceXAI released Grok 4.7 as its new flagship model for coding, agentic tasks, and knowledge work. Grok 4.7 keeps the same pricing as Grok 4.6 at $2 per million input tokens and $6 per million output tokens. The main changes are a larger base model, a longer reinforcement learning run, improved self-verification and long-context handling, and native Grok Bot harness support, with a 500,000-token context window and updated safety measures.
SpaceX introduced Grok 4.7, its latest large language model, and evaluated it against multiple benchmarks while adding long-horizon processing and safety safeguards. SpaceX reports Grok 4.7 completed CursorBench 4.0 tasks at an average cost of $4.69 per task. The rollout changes pricing and availability by offering Grok 4.7 starting at $2 per million input tokens and $6 per million output tokens, plus a twice-faster version that costs twice as much.
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