GPT-6 Astra scores 61 on Artificial Analysis, five behind Anthropic
Thursday's launch rhetoric met its first independent scoreboard, and the scoreboard is more interested in price tags. Also: Tesla quietly opened a door for businesses that want to buy and run Cybercabs themselves.
Artificial Analysis put GPT-6 Astra at 61 points on its Intelligence Index — five behind Anthropic's Claude Fable 5.1, which tops the table at 66 — in the first full third-party evaluation of OpenAI's new flagship, published the same day as the launch. The gap shrinks on agentic coding, where Astra hits 67 in the Codex harness, roughly level with Claude Opus 5, Fable 5, and Meta's Muse Spark 1.3, with Fable 5.1 still ahead at 70. The evaluator's verdict on OpenAI's AGI framing is blunt: the benchmark tables "offer no confirmation of it so far."
The more interesting number is the price. OpenAI raised rates 2.5x over GPT-5.6 Sol — $10 per million input tokens and $50 per million output, up from $4 and $20 — and Astra's token-efficiency gains only partially cover that. In Codex the model needs roughly a third of the tokens GPT-5.6 Sol (max) used, which is enough to put it on the cost-per-task Pareto frontier for agentic coding: same price as the old model at max effort, two index points higher. On the Intelligence Index it cuts output tokens by only about 10%, leaving it 75% more expensive per task than its predecessor. The clearest real gain is on knowledge and hallucination — the error rate on AA-Omniscience falls from 92% to 51% at max effort while accuracy rises four points, which is the opposite of the usual accuracy-versus-hallucination trade.
What that adds up to is a model that is genuinely better at the two things enterprises buy — writing code through an agent and not inventing facts — and worse at the thing OpenAI's launch briefing sold, which is a general intelligence claim. Agentic knowledge work splits down the middle: Astra gains about 80 Elo points on AA-Briefcase's multi-week, many-file projects while losing about 80 on GDPval-AA v2, an eval of economically valuable tasks across 44 occupations derived from OpenAI's own data, and it slips two to three points on long-document reasoning, banking support, and scientific Python. We covered the launch itself in OpenAI ships GPT-6 Astra and says the AGI era has started — this is the number that decides whether the pricing holds.
Tesla published an interest form Thursday inviting businesses to buy Cybercab fleets or supply infrastructure for its robotaxi network — the strongest signal yet that it no longer plans to keep the business entirely in-house. The form asks respondents to pick from Cybercab fleet purchasing, mobility hubs and infrastructure, event collaboration, or "other," and offers no detail on pricing, terms, or what a third-party operator would actually get. Until now Tesla had tested and operated its own robotaxis, first with Model Y vehicles and now with the purpose-built Cybercab.
The framing matters more than the form. Tesla's robotaxi pitch has always been vertically integrated — build the car, own the network, keep the economics. Widening the circle to fleet operators is the asset-light path Uber and Waymo already run, and it would let Tesla saturate markets far faster than it could by buying and operating every vehicle itself. That also puts it directly against the autonomous fleet management companies now raising serious money to be the middle layer of the robotaxi industry, and it lands the same week Tesla put wheel-less Cybercabs in public view in Austin.
A developer released sanoTTS, a text-to-speech stack whose smallest model is 294,000 parameters and 337 KB when quantized to 8-bit integers — small enough to run on a $3 ESP32-S3 microcontroller with no neural accelerator. The family spans 11 voices and 6 languages at sizes from 294k to 2.2M parameters; the creator reports a real-time factor of 0.225 on the ESP32, meaning four seconds of audio generate in one second. On the SCOREQ quality metric, the 1.51M-parameter voice scores 4.13 against 3.81 for a 4.63M-parameter commercial alternative and 3.02 for a 15M-parameter one, and the project claims roughly 2% word error rate when its output is transcribed back by Whisper. Models, code, a browser demo, and a recipe for adding languages are all public.
What to watch: whether Astra's Intelligence Index gap closes as OpenAI tunes effort levels, and whether Tesla's fleet form turns into actual fleet orders.
If a model is measurably better at coding and hallucinating less but still trails on general intelligence, is a 2.5x price hike defensible — or is OpenAI charging for a claim the benchmarks don't support? Tell us in the comments.
Sources: Artificial Analysis — Benchmarking GPT-6 Astra · Trending Topics · Tesla — Help Us Build Our Robotaxi Network · TechCrunch · sanoTTS on Hugging Face · sanoTTS on r/LocalLLaMA
