How quickly things can change! Here's the updated frontier with Opus 5.5, GPT-6 Sol & Luna incorporated. Opus 5.5 has kicked Astra off the frontier, giving Anthropic more ownership of peak intelligence than before, while OpenAI still dominates the mid to low end of the frontier with GPT-6 Sol & Luna coming in smarter and 50% cheaper than their prior 5.6 generations. Xiaomi's MiMo takes the rotating open source spot for today, but otherwise the entire AA Pareto frontier is dominated by just Anthropic & OpenAI.

There is something clearly different in how Anthropic & OpenAI scale effort levels. Doesn't show up on every benchmark, but these results on FrontierCode make it really clear. Anthropic models peak at lower effort levels, where as OpenAI models start low and climb up fairly consistently. The result is a better score at a lower cost for Anthropic models, but an unintuitive experience where increasing effort does not increase scores and might actually degrade performance (on this benchmark at least).

GPT-6 Sol and Luna push the cost efficiency frontier by halving cost relative to GPT-5.6 Sol and Luna. Intelligence Index and Coding Agent Index scores remain level with GPT-5.6, with progress in some evaluations and regressions in others Pricing is approximately half that of GPT-5.6: Sol drops from $4/$20 to $2/$10 per million input/output tokens, and Luna from $0.20/$1.20 to $0.10/$0.50, with the same 90% discount for cache reads and 25% premium for cache writes. Key takeaways: ➤ Halves Cost per Task: GPT-6 Sol (max) costs $1.06 per task to run the Artificial Analysis Intelligence Index, ~50% less than GPT-5.6 Sol (max) at $1.99. GPT-6 Luna (max) costs $0.07 per task, ~60% less than GPT-5.6 Luna (max) at $0.18. This is driven by the price cut, as both models use slightly more output tokens per task (31k vs 29k for Sol, and 51k vs 41k for Luna). These two releases allow OpenAI to capture a significant portion of the cost efficiency Pareto frontier. ➤ In the Coding Agent Index, Sol improves but Luna regresses: In OpenAI's Codex harness, GPT-6 Sol (max) scores 57 in the Artificial Analysis Coding Agent Index, up 2 points from GPT-5.6 Sol (max), with gains in Terminal-Bench 4.0 (43% vs 37%) and SWE-Atlas-QnA (58% vs 54%). At $2.99 per task it costs ~50% less than GPT-5.6 Sol (max) and sits on the Pareto frontier of Coding Agent Index vs Cost per Task. GPT-6 Luna (max) scores 41, down 2 points from GPT-5.6 Luna (max), with lower scores in SWE-Atlas-QnA (44% vs 49%) and DeepSWE v1.1 (64% vs 66%), at ~60% lower cost per task. ➤ Significant reduction in hallucination: Both models hallucinate less in AA-Omniscience, our knowledge and hallucination benchmark. GPT-6 Sol (max) cuts its hallucination rate from 92% to 60% and GPT-6 Luna (max) from 93% to 77%. Sol achieves this by declining to answer more often: it attempts 83% of questions vs 99% for GPT-5.6 Sol (max), which cuts wrong answers by about a quarter but also lowers accuracy 5 points from 59% to 54%. Luna's accuracy is broadly unchanged at 44% vs 43% while it answers fewer questions. On the AA-Omniscience Index, Sol improves from 22 to 27 and Luna from -10 to 1. ➤ Mix of improvement and regression across evals: Beyond AA-Omniscience, both models improve in AutomationBench-AA (Sol 62% vs 60%, Luna 53% vs 50%) and Terminal-Bench 4.0 (Sol 44% vs 40%, Luna 13% vs 12%). However, we observe regressions in two key knowledge work evaluations. In GDPval-AA v2.1, our benchmark adapted from OpenAI's dataset of economically valuable tasks across 44 occupations, Sol drops ~100 Elo points and Luna ~75. Luna also drops ~45 Elo points in AA-Briefcase v1.1, while Sol is level. AA-Briefcase v1.1 is a private evaluation across multi-week knowledge work projects, with thousands of input files. Our team has manually inspected hundreds of model outputs: the regressions tend to be driven by reduced presentation quality and deliverables that omit rubric elements. Congratulations @OpenAI and @sama on the launch!

Please welcome GPT-6 Sol and GPT-6 Luna to the GPT-6 universe. GPT-6 Sol and Luna build on the advances behind GPT-6 Astra, bringing much of its strengths into faster and more affordable models to support work at scale. We’ve also made caching and inference more efficient, and we’re passing the savings directly to you: 50% lower API prices for Sol and Luna compared with GPT‑5.6 promotional pricing.

If you're on Pro, Max, or Team, your reset is available today in Settings → Usage. Apply it any time until Oct 22. Opus 5.5 is the default for paid plans. It's priced lower than Opus 5, so your 5-hour and weekly limits go 25% further.

Grok 4.7 scores 46 on the Artificial Analysis Intelligence Index to bring SpaceXAI into the top 4 AI labs. Coding Agent Index performance has also improved, overtaking GPT-5.6 Sol Grok 4.7 scores +2 points over Grok 4.6 on the Intelligence Index, with strong performance on agentic knowledge work tasks. We evaluated the new model at xhigh reasoning effort. Congratulations to @SpaceXAI and @ElonMusk on the release! Key takeaways: ➤ Grok 4.7 joins the frontier of agentic knowledge work: Grok 4.7 gains +111 Elo over Grok 4.6 (high) on AA-Briefcase, our private benchmark for long-horizon agentic knowledge work, scoring 1657 Elo and placing it alongside Claude Opus 5 and Claude Fable 5.1 at the frontier. On GDPval-AA, it scores 1695 Elo, +90 ahead of Grok 4.6 (high). ➤ A leap in coding agent performance: Grok 4.7 (xhigh) with Grok Build scores 56 on the Artificial Analysis Coding Agent Index, up +9 points from Grok 4.6 (xhigh). Among models in their native harnesses, Grok 4.7 + Grok Build now ranks 4th, behind only Claude Fable 5.1, GPT-6 Astra, and Claude Opus 5. ➤ Incremental performance changes elsewhere: Outside of agentic knowledge work, Grok 4.7 broadly matches Grok 4.6 (high) on the other Intelligence Index tasks. It improves on Terminal-Bench 4.0 (+4.5 percentage points) and GDP.pdf (+3.0 p.p.), with regressions on AA-LCR (-3.7 p.p.) and AutomationBench-AA (-1.1 p.p.). ➤ High token use across tasks: Grok 4.7's gains come with higher token usage. Grok 4.7 (xhigh) uses approximately 81k output tokens per Intelligence Index task, compared with 36k for Grok 4.6 (high) and 27k for GPT-6 Astra (max) - 125% and 196% more, respectively. Other model details: ➤ Context window of 500k tokens, unchanged from Grok 4.6 ➤ Pricing of $2/$6 per 1M input/output tokens with cache hits discounted to $0.50 per 1M tokens, matching Grok 4.6 ➤ Configurable reasoning effort spans low to xhigh. Our evaluation uses xhigh.

With the combination of Astra & 5.6 Luna, OpenAI dominates the pareto frontier on the Artificial Analysis Intelligence Index, owning 11 out of 15 positions. This is why they are gaining share.

Anthropic and OpenAI are now chasing 20 to 30 MW compute deals alongside gigawatt projects as “speed to usable capacity” becomes increasingly important. That puts already energized sites in a way stronger position as labs look to take whatever capacity can come online fastest.

NEA recently led @positron_ai's $875M Series C — now @NeehaarGandhi @psikryan @lilatretikov @ScottDSandell & Forest Baskett are sharing why we think this is the next architectural reset for AI. ⚠️ AI just hit a trillion-dollar traffic jam — and it's not compute that's stuck, it's memory and networking. We think we found the company built to fix it. ⚡ Meet Asimov: a chip that ditches the switch entirely, links 16,000+ chips directly, and crams in more memory than anything else on the market. No HBM. No CoWoS. No bottleneck. 🚀 5x the tokens per dollar. A fraction of the power. And the its first-gen system already live.

Three key open-weight labs have seen monthly dollars spent on their models grow by 10x or more so far in 2026. The closed models have seen comparatively slower growth in spending, though from much higher starting points.