The new GPT-5.6 family: Luna, Terra, Sol

OpenAI released the GPT-5.6 family of models in three sizes—Luna, Terra, and Sol—claiming superior long-running agentic performance over Anthropic's Claude Fable 5 on the Agents' Last Exam benchmark, though Fable 5 outperformed on SWE-Bench Pro. OpenAI also published an audit questioning the reliability of SWE-Bench Pro, estimating ~30% of its tasks are broken.

OpenAI's latest flagship model hit general availability this morning https://openai.com/index/gpt-5-6/ , and comes in three sizes: Luna, Terra, and Sol from smallest to largest . The new models are priced per 1M input/output tokens as Luna $1/$6, Terra $2.50/$15, Sol $5/$30. For comparison, the Claude Opus series are $5/$25 and the Claude Fable 5 is $10/$50, but price-per-million tokens doesn't tell us much now that the number of reasoning tokens can differ so much between models for the same task. OpenAI's biggest benchmark claim concerns long-running agentic performance, with one benchmark showing all three models outperforming Claude Fable 5: We trained GPT-5.6 to get more useful work from every token. On Agents’ Last Exam , an evaluation of long-running professional workflows across 55 fields, GPT-5.6 Sol sets a new high of 53.6, eclipsing Claude Fable 5 adaptive reasoning by 13.1 points. Even at medium reasoning, it beats Fable 5 by 11.4 points at roughly one-quarter the estimated cost. That efficiency extends to smaller models, which are essential to making intelligence more abundant and affordable: GPT-5.6 Terra and GPT-5.6 Luna outperform Fable 5 at around one-sixteenth the cost. Amusingly, one self-reported benchmark that Fable 5 crushed the GPT-5.6 family on was SWE-Bench Pro, where Fable 5 got 80% compared to GUT-5.6 Sol getting 64.6%. This may help explain why OpenAI chose to publish this article yesterday https://openai.com/index/separating-signal-from-noise-coding-evaluations/ specifically calling out SWE-Bench Pro for problems they found while auditing that benchmark: In light of these results, we estimate that ~30% of SWE-bench Pro tasks are broken, and advise that model developers carefully examine results I've had some early access to GPT-5.6 Sol - it's definitely very competent, though so far it hasn't struck me as better than Fable at the kind of complex coding tasks I've been using with Anthropic's model. As usual, the model guidance for using GPT-5.6 https://developers.openai.com/api/docs/guides/latest-model?model=gpt-5.6 has the most interesting details. There are a bunch of new API features that I need to explore and probably add support for in LLM https://llm.datasette.io/ , including: Here's a full page with 18 different pelicans https://static.simonwillison.net/static/2026/gpt-5.6-pelicans.html - for reasoning efforts none, low, medium, high, xhigh, and max across the three different models. It also lists their token and calculated costs - the least expensive was gpt-5.6-luna at effort none for 0.71 cents, the most expensive was gpt-5.6-sol at max reasoning level for 48.55 cents. In further pelican news, if you jump to 17:50 in their livestream from this morning https://www.youtube.com/live/Wq45rvPGNHs?t=1070s you'll see OpenAI's own demo of 3D pelicans riding a tricycle, a bicycle, a pony, and another pelican Tags: ai https://simonwillison.net/tags/ai , openai https://simonwillison.net/tags/openai , generative-ai https://simonwillison.net/tags/generative-ai , llms https://simonwillison.net/tags/llms , llm-tool-use https://simonwillison.net/tags/llm-tool-use , llm-pricing https://simonwillison.net/tags/llm-pricing , pelican-riding-a-bicycle https://simonwillison.net/tags/pelican-riding-a-bicycle , llm-release https://simonwillison.net/tags/llm-release , gpt-5 https://simonwillison.net/tags/gpt-5