OpenAI / ChatGPT

OpenAI added two models to the GPT-6 family: Sol for complex coding and professional tasks, Luna for fast high-volume work. API pricing is cut by 50% vs GPT-5.6 promo rates—Luna's output price actually dropped 58%. Sol beats Claude Opus 5 on AutomationBench and Agents' Last Exam at roughly one-tenth the cost per task. Both are live in the API today; no weights are released.

AI safety and competition dominated this week's U.S.-China summit, but deep mistrust makes concrete outcomes unlikely. The Trump administration has loosened chip export curbs, letting Nvidia sell H200 chips to China, while U.S. officials accuse Chinese firms of stealing AI models through distillation. Both sides agreed to set up a hotline for AI-related national security risks and plan to meet again in Shenzhen in two months. Senator Warren warned Trump against catering to the AI industry instead of pressing Xi on AI risks. Analysts expect talks to stay at the level of definitions and principles, since neither side will accept limits on its own competitiveness.

Tunguz argues that enterprise AI spend concentrates in the 'good enough, affordable' middle tier, not the frontier. Anthropic held Opus at $5/$25 across five releases while OpenAI slashed Luna 80% then 50%; open models run most token volume at an 86% discount to closed models. The priciest model, Fable 5.1, captured only 3.7% of gateway spend in its first 12 days, while mid-tier models claim 40% of spend and 30% of tokens. As intelligence per dollar explodes but enterprise requirements barely move, tokens may shift to commodity—and that will decide the market's economics.

Simon Willison compares three models launched on the same day. GPT-6 Luna drops to $0.10/M input tokens—half the price of GPT-5.6 Luna and one of OpenAI's cheapest models ever. GPT-6 Sol also halves its predecessor's price. Claude Opus 5.5 gets a 20% cut but still costs twice as much as GPT-6 Sol. In testing, Opus 5.5 at max thinking level over-thinks to the point of hitting its 128k output limit, failing to produce even a simple pelican SVG. Each failed attempt cost $2.56 and took nearly 20 minutes. Willison calls the max mode effectively useless.

Artificial Analysis reports that GPT-6 Sol and Luna score close to their GPT-5.6 predecessors on the Intelligence Index, while token pricing drops ~50%, halving per-task cost. Their chart shows the intelligence-vs-cost trade-off across OpenAI model generations. The post does not disclose exact scores or pricing figures.

OpenAI added two models to the GPT-6 family: Sol and Luna. They inherit most of GPT-6 Astra's capabilities but run faster and cheaper, with API pricing 50% below GPT-5.6 promotional rates. The post credits caching and inference efficiency gains. No benchmarks, latency figures, or rollout timeline are disclosed.

GPT-6 prompt caching now hits more often by default, with discounts for shared prefixes reused within 30 minutes. A new dashboard tracks hit rates and a diagnostics tool pinpoints misses—e.g., a tools_changed reason costing 5,629 tokens. Developers can set explicit cache breakpoints, adjust reasoning effort without breaking cache, and prewarm context to cut latency. GitHub Copilot reports a >50% drop in tokens needing fresh processing; Manus raised cache hit rates from ~85% to >90% in under a week.

OpenAI's GPT-6 Sol and Luna cut prices roughly in half: Sol drops to $2/$10 per million input/output tokens, Luna to $0.10/$0.50. Per-task cost on the Artificial Analysis Intelligence Index falls from $1.99 to $1.06 for Sol and $0.18 to $0.07 for Luna, while overall scores stay level. Hallucination rates drop sharply—Sol from 92% to 60%, Luna from 93% to 77%—but both models decline to answer more often. In the Coding Agent Index, Sol gains 2 points to 57; Luna loses 2 points to 41. Both regress on GDPval-AA v2.1, a knowledge-work benchmark: Sol drops ~100 Elo, Luna ~75, driven by shorter deliverables that omit rubric elements. The cost drop is real; the quality trade-off on knowledge tasks is worth watching.

Arena is now testing two new OpenAI models, GPT-6 Sol and GPT-6 Luna, with scores not yet released. You can try them on real agent tasks and vote to feed the leaderboard. The post doesn't disclose model size, release date, or pricing.

OpenAI dropped GPT-6 Sol and GPT-6 Luna on Arena, both built on GPT-6 Astra tech. The pitch is faster, cheaper inference with better caching for high-volume workloads. API pricing is 50% below GPT-5.6's promotional rate. The post doesn't disclose benchmark scores or latency numbers, so I'd wait for third-party benchmarks before getting excited.

Sam Altman posted that GPT-6 Sol and Luna have no competition when measured by per-task pricing. He claims big jumps over the 5.6 series in intelligence, alignment, work output, coding, and computer use, with per-token price halved and even lower per-task cost. The post doesn't disclose specific benchmarks or pricing figures—I'd wait for third-party testing before taking it at face value.

OpenAI released GPT-6 Sol and GPT-6 Luna, both built on GPT-6 Astra tech and aimed at cheaper, faster high-volume workloads. API pricing is 50% lower than GPT-5.6 promotional pricing, driven by more efficient caching and inference. Sam Altman reposted the announcement and called the character designs cute. The post doesn't disclose benchmark scores, latency figures, or regional availability.

OpenRouter just listed two new OpenAI models: GPT-6 Sol and GPT-6 Luna. Pricing is half that of the previous GPT-5.6—Sol at $2/M input and $10/M output, Luna at $0.10/M input and $0.50/M output. On AutomationBench, both beat the prior best score while costing less per task. The post doesn't disclose exact scores or latency figures.

Artificial Analysis tested OpenAI's GPT-6 Sol and Luna. Both cost half as much as their GPT-5.6 equivalents: Sol at $2/$10 per million input/output tokens, Luna at $0.10/$0.50. The Intelligence Index matches GPT-5.6, so you're getting the same capability for less money. The post doesn't disclose evaluation dimensions or latency figures.

OpenAI's developer account announced GPT-6 Sol and Luna, with API pricing cut to half of GPT-5.6. Sherwin Wu added specifics for Luna: $0.10 per 1M input tokens and $0.50 per 1M output tokens, noting that pricing will soon need to switch to per-billion-token units. The post doesn't disclose Sol's per-token price or how the two models differ in capabilities.

OpenAI announced the rollout of GPT-6 Sol and GPT-6 Luna for Plus, Pro, Business, Enterprise, and Edu users, available now in ChatGPT Work and Codex. The post doesn't disclose model specs, pricing changes, or performance vs. GPT-5—hold for benchmarks.

OpenAI followed GPT-6 Astra with two smaller models, Sol and Luna, aiming to make Astra-level intelligence cheaper and more accessible. Sol handles complex tasks like coding; Luna targets high-volume, clear-goal work such as summarization, extraction, and quick Q&A. The post doesn't disclose pricing, error-rate comparisons, or a launch date, so I'd hold off on the 'fewer mistakes' claim until benchmarks land.

OpenAI added two cheaper models to the GPT-6 family: Sol and Luna, with API prices halved across input and output. Sol costs $2/$10 per 1M tokens, Luna $0.10/$0.50. Sol scored 33.2% on AutomationBench at xhigh effort at 9% of Claude Opus 5's cost per task, and 56.4% on Agents' Last Exam at max effort at 60% lower cost. On internal factuality evals, Sol makes about half as many mistakes as its predecessor. The post does not specify a launch date beyond 'available now.'

404 Media obtained internal docs and spoke to three contractors: OpenAI hires thousands of people to rate ChatGPT responses, but many are using AI to generate their annotations. The rules ban any AI use, including Grammarly and AI translation. Reviewers spot AI-written work by looking for repetitive words, AI-style punctuation like overused em dashes, and unusually fast turnaround. Getting caught means immediate removal. One fired contractor said they just needed a boost and it led to their downfall. Outsourcing firm Mercor confirmed its contracts prohibit LLM use and violators are removed on detection.

The article coins 'spymark' for hidden tracking signals embedded in media without user knowledge or consent. Google SynthID can hide a 136-bit payload in a 512×512 image—enough for a 64-bit database ID plus error correction. OpenAI and others are building similar systems at scale. The author argues 'watermark' obscures the privacy risk; 'spymark' bakes the surveillance concern into the name. Examples cover frequency-domain image hiding, audio spectrogram encoding, and text word-choice steering. The open-source tool audiowmark has offered AES-protected 128-bit payloads since 2018. The post does not disclose actual deployment scope.