Entity trail

GPT-5.6 Sol Ultra

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

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Sources

Corpus findings

2026-09-04 / sources-researcherLatent Space burned 20B+ tokens on GPT-6 Astra and reports it runs at roughly $6 an hour while managing 20-50 subagents in parallelswyx's 2026-09-03 hands-on says Astra saturates the hardest FrontierMath (97.6%) and ARC-AGI-3 (99.9%), but the practical finding is that it holds coherence across billions of tokens in a single agent thread and can choose and train models, label data for active learning, instrument and read logs, and command fleets of subagents running other models. The $6/hour figure comes from 33 tokens per second at a $50 per million token max rate, and Artificial Analysis independently confirms Astra is more token-efficient than Sol and Fable. Their caveat matters for anyone budgeting: on Ultra with 20-50 parallel agents the hourly cost goes far past $6 precisely because the model parallelizes well.
2026-08-20 / rss-researcherAI agents given six days and $3,000 to do real research scored 2/6 and 1/6 from the papers' own authorsA Princeton-led study with Stanford, UC Berkeley, Johns Hopkins, Toronto, Georgetown, and the UK AI Security Institute handed Claude Opus 4.8 and GPT-5.6 Sol Ultra the central research question from unpublished NeurIPS 2026 submissions, plus six days, a $3,000 API budget, GPU credits, and web access. The original authors graded the output as reviewers and rejected both papers, citing weak experimental design, unsupported conclusions, and impenetrable prose. Neither agent spent its full budget, and researchers Peter Kirgis and Sayash Kapoor argue the failure was in judgment, not engineering: the agents could run experiments and write LaTeX but not decide which hypotheses deserved compute.
2026-08-14 / reddit-researcherFour Frontier Releases in About 72 Hours: r/singularity's 'No Way, What an AI Week' Hits 496 UpvotesA meta-post cataloguing the week's release density topped r/singularity at 496 upvotes and 60 comments — Grok 4.6 (Aug 12), DeepSeek V4-Pro and Harness (Aug 13), GPT-5.6 Sol Ultrafast (Aug 13), and GLM-5.3 (Aug 14) all landed inside roughly 72 hours. This follows r/LocalLLaMA declaring a "Models Day" the previous day, making it two consecutive multi-release clusters. The practical consequence for builders is that benchmark comparisons published more than a week ago are now stale in three separate model families at once.
2026-08-14 / reddit-researcherCerebras Clocks Humanity's Last Exam at 11 Hours 11 Minutes for 2,500 Questions — Against 78+ Hours for Claude Fable 5The most concrete number in the Cerebras/OpenAI Ultrafast writeup is a wall-clock eval comparison: GPT-5.6 Sol Ultrafast completed all 2,500 Humanity's Last Exam questions in 11 hours 11 minutes, versus 78+ hours for Claude Fable 5, and posted a 5.6x end-to-end speedup on GDP-Val with no measured degradation. Cerebras claims the throughput comes "without any quality compromise," with accuracy comparable to the slower serving path. For anyone running long agentic eval sweeps, this reframes inference speed as an experiment-iteration variable rather than a UX nicety.
2026-08-14 / reddit-researcherOpenAI Previews 'Ultrafast' Tier: GPT-5.6 Sol at 750 Output Tokens/Sec on Cerebras Wafer-Scale Silicon, 14x Standard SpeedOn August 13 OpenAI and Cerebras announced Ultrafast, a new API service tier running GPT-5.6 Sol at up to 750 output tokens per second — up to 14x Standard processing, 11x faster than Claude Fable 5, and 5x faster than Opus 4.8 on Fast mode. It runs on the Cerebras Wafer-Scale Engine with 44 GB of on-chip SRAM per wafer, launching as a limited API preview for select customers with capacity-gated expansion. OpenAI is targeting latency-bound products — financial research, incident response, support, voice, commerce, live experimentation — where a frontier model previously had to sit behind a batch job.
2026-08-13 / sources-researcherSimon Willison Ships alchemy-utils 0.1a0 — a Cross-Database sqlite-utils — From a Shower Idea in One Morning With Codex and GPT-5.6 Sol UltraReleased 12 Aug 2026, alchemy-utils reimplements sqlite-utils' core API (insert/upsert/insert_all/upsert_all, create, update, table introspection) on SQLAlchemy so it works against PostgreSQL, SQLite and DuckDB with identical calls. The interesting artifact is the prompt: a single research-spike instruction that specified the reference implementation path, mandated red/green TDD with pytest, named a repo to copy the Postgres test strategy from, and required `uv init` plus early and frequent commits — and it took very few follow-ups to reach releasable alpha. A demo imports every street tree in San Francisco straight from a CSV URL into DuckDB with schema auto-created, in roughly 35 seconds after optimization.
2026-08-11 / sources-researcherDan Luu Kills the 'Dynamic Languages Are 2–3x Cheaper for Coding Agents' Claim by Rerunning It on Real Tasks Instead of Rosetta CodeIn a piece posted Aug 10, Dan Luu re-tests the widely-shared Alderson result that dynamic languages cost roughly 1/2 to 1/3 the tokens of static ones (J at 70 tokens average vs Clojure's 109), and shows it was an artifact of trivial Rosetta Code problems. He ran two substantial evals — implementing a full Zstd decoder from RFC specs with no tests, and the Pandoc task from ProgramBench scored against holdout tests — across ~24 languages using GPT-5.6 Sol at medium and ultra effort, measuring cost against correctness. His pre-registered predictions held: the dynamic-language advantage and the 'weird language' advantage both vanish at real complexity, a weak-to-moderate positive correlation emerges between language popularity and solution quality, and neither language family dominates. The practical takeaway is to pick your agent's language for ecosystem depth, not token thrift.
2026-08-08 / thought-leaders-researcherWillison Runs the Same Game Prompt Through Codex + GPT-5.6 Sol Ultra and Claude Fable 5 — Sol Ultra 'Produced a Much Better Game'On August 7 Simon Willison published 'Moonlight & Mayhem,' the result of handing Codex Desktop running GPT-5.6 Sol Ultra — the mode that makes aggressive use of sub-agents — the identical raccoon-heist premise he had previously given Claude Fable 5. Sol Ultra's version has a full team-of-thieves heist structure versus Fable's single raccoon collecting coins in a back yard, though it required manual debugging and the sub-agent fan-out is expensive. Willison still qualifies the result: Sol is 'definitely very competent' but has not struck him as better than Fable on the hardest complex-coding tasks.
2026-08-08 / vibe-coding-researcherTip: Ask the Model Why the Bug Looks Wrong Instead of Debugging It YourselfRebuilding a four-year-old raccoon heist game with GPT-5.6 Sol Ultra through Codex Desktop, Simon Willison hit a visual bug where oversized black spheres floated above each raccoon's head. Rather than inspecting the code, he asked 'Why do the raccoons have huge black spheres on them?' followed by 'Fix it' — and the model resolved it. The session gives a rare hard cost datapoint for aggressive sub-agent use: 52 minutes, $23.28 at full API rates, 700.7K input tokens plus 32.5M cached tokens and 148K output tokens.
2026-07-26 / reddit-researcher"POV: Using GPT-5.6 Sol Ultra to Rename a Variable" Hits 625 Upvotes as Frontier-Model Overkill Becomes a PunchlineA r/ChatGPT meme mocking the use of a top-tier reasoning model for a trivial refactor pulled 625 upvotes. Read alongside the WSJ reporting on enterprises rationing token spend, it's a cheap but real signal that practitioners have internalized model-tier routing as an economic decision rather than a default-to-strongest habit. Single-source, sentiment only — no data behind it.
2026-07-20 / hn-researcherGPT-5.6 Sol Ultra Found and Weaponized a WordPress Core RCE for $25 in Ten HoursSearchlight Cyber adapted OpenAI's published vulnerability-research prompt, pointed four agents at WordPress Core for six-plus hours, and got a full pre-auth exploit chain: CVE-2026-63030 (REST API batch endpoint route confusion, CVSS 9.8) escalated via CVE-2026-60137 (author__not_in WP_Query SQLi). Total compute cost was roughly $25 over ten hours against a market where exploit brokers pay $500,000 for a WordPress RCE. The chain — dubbed 'wp2shell' — poisons the oembed cache, abuses customize_changeset to temporarily assume admin, and uses a cycle-detection gadget to re-trigger parse_request, create an admin account, and upload a backdoor plugin; 500M+ sites affected, emergency patch 7.0.2 released.
2026-07-15 / arxiv-researcherOpenAI Ships GPT-5.6 Family (Sol/Terra/Luna) to API and Codex, Claims +13.1 on Agents' Last Exam vs Claude Fable 5OpenAI launched the GPT-5.6 family on July 14, 2026 — Sol (flagship), Terra (cost-optimized), and Luna (fast tier) — rolling out across ChatGPT, Codex, and the API the same day after a US-government-requested delay for security review. GPT-5.6 Sol scored 53.6 on Agents' Last Exam, beating Claude Fable 5 (adaptive reasoning) by 13.1 points; at medium reasoning it exceeded Fable 5 by 11.4 points at roughly one-quarter the cost, with Terra/Luna claimed to beat Fable 5 / Opus 4.8 at ~1/5 the API cost. Sol adds an 'Ultra' subagent mode and a 'Max' reasoning-effort setting.

Graph relationships

BENCHMARKED AGAINST
Claude Opus 4.8
Claude Opus 4.8 and GPT-5.6 Sol Ultra were tested on research tasks.
Source finding
USES
Simon Willison
Simon Willison used GPT-5.6 Sol Ultra through Codex to rebuild a game.
Source finding
USES
Simon Willison
Simon Willison used GPT-5.6 Sol Ultra with Codex Desktop to generate a game.
Source finding
COMPETES WITH
Claude Fable 5
GPT-5.6 Sol Ultra produced a much better game than Claude Fable 5 for the same raccoon-heist premise.
Source finding
BUILT BY
alchemy-utils
GPT-5.6 Sol Ultra generated alchemy-utils from a research spike prompt in one morning.
Source finding
BENCHMARKED AGAINST
Claude Opus 4.8 -> GPT-5.6 Sol Ultra
Claude Opus 4.8 and GPT-5.6 Sol Ultra were tested on research tasks.
Source finding
USES
Simon Willison -> GPT-5.6 Sol Ultra
Simon Willison used GPT-5.6 Sol Ultra through Codex to rebuild a game.
Source finding
USES
Simon Willison -> GPT-5.6 Sol Ultra
Simon Willison used GPT-5.6 Sol Ultra with Codex Desktop to generate a game.
Source finding
BUILT BY
alchemy-utils -> GPT-5.6 Sol Ultra
GPT-5.6 Sol Ultra generated alchemy-utils from a research spike prompt in one morning.
Source finding

Source trail

Latent Space MIT Technology Review r/singularity (496up/60c) — community aggregation of individually verified releases Cerebras Blog (corroborated by unite.ai and 9to5Mac)OpenAI (corroborated by Cerebras blog, TechCrunch, GlobeNewswire); r/singularity 463up/43c Simon Willison Dan Luu (surfaced via Hacker News, 201 pts)simonwillison.net Simon Willison r/ChatGPT Searchlight Cyber / Hacker News OpenAI

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