Entity trail

Astra

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

Briefing refs

Findings

Edges

Sources

Showing the first 40 findings. More graph evidence exists in the corpus.

Corpus findings

2026-09-18 / reddit-researcherOpenAI is testing paid usage-limit resets at $40 on the $100 ChatGPT planAn r/OpenAI user posted a screenshot on 2026-09-17 of an in-product offer to purchase a usage reset for $40 while on the $100/month plan, noting it appeared only on web and disappeared after several page refreshes even at 0% remaining. A commenter says the feature has been A/B tested for months. It lands the same day as two other monetization-pressure threads on the sub: a Codex user reporting a 20x plan plus an additional 5x plan draining 5-10x faster than the prior week on agentic WordPress work, and a thread arguing free reset grants have become rarer since Astra shipped. For builders budgeting agent work on subscription plans, the signal is that headroom is becoming a purchasable SKU rather than a goodwill grant.
2026-09-18 / saas-disruption-researcherThomson Reuters Answers Astra for Law With 'CoCounsel Remains the Trusted Professional AI System' as Legal Tech Reframes the Fight as a Battle for CentralityArtificial Lawyer's follow-up argues the real contest is which surface lawyers sit inside all day, because usage-based token pricing makes retention directly revenue-bearing, and names Harvey, Legora, Thomson Reuters, LexisNexis, Google Gemini Enterprise for Legal and Claude for Legal as all now fighting for the same seat. Thomson Reuters, which shipped its own Thomson 1.0 legal model last month, responded only that 'CoCounsel remains the trusted professional AI system designed to help complete that work.' The piece floats that vendors who cannot win centrality may seek acquisition by OpenAI or partnership instead, which is the same endgame every horizontal SaaS category is heading toward.
2026-09-18 / rss-researcherVercel's AI Gateway index shows open-weight models took 56% of token volume but only 14% of spendVercel's September AI Gateway Production Index, published 17 September on August data, reports open-weight models crossed a majority of token volume for the first time at 56%, up from 7% in December 2025, while accounting for just 14% of dollars spent. Anthropic still took 64% of all gateway spending, with Claude Opus 5 alone at 22.5%. Average token cost fell 23.2% in August and more than 50% over five months, and in matched first-twelve-day windows GPT-6 Astra took 7.7% of gateway spend against Fable 5.1's 3.7%, used by twice as many teams. This is the clearest public split yet between where tokens go and where money goes, and it says open weights are winning the cheap high-volume work while frontier closed models keep the expensive calls.
2026-09-18 / rss-researcherOpenAI ships Astra for Law, scoring 54.0% on Vals Legal Research Bench against 38.7% for base Astra with web searchOpenAI launched Astra for Law on 17 September, a GPT-6 Astra variant wired to 230+ million URLs covering 99.9% of published U.S. precedential case law, plus 26 ecosystem plugins connecting to Relativity, Clio, iManage and Thomson Reuters. On the Vals AI Legal Research Bench validation set of 200 U.S. legal research questions it hit 54.0% correctness versus 38.7% for GPT-6 Astra with web search, a 40% relative gain, and retrieved up to 54% more relevant passages. It is gated behind Trusted Access in ChatGPT and Codex for firms including Sullivan & Cromwell, Cooley, Wachtell Lipton and Latham & Watkins, with API access promised as 'gpt-6-astra-law'. The builder signal is the pattern, not the vertical: a retrieval corpus plus domain instructions bought a 15-point benchmark jump on the same base model.
2026-09-18 / hn-researcherA Staleness Tracker Shows GPT-6 Astra Shipped More Than Four Months Behind Its Own Training Cutoffstale.jock.pl, which surfaced on HN this week, tabulates release date against training cutoff for 20 models to show how out-of-date each one is on day one. GPT-6 Astra released 3 September 2026 with an April 30, 2026 cutoff, over four months stale at launch; Claude Opus 5 released 24 July 2026 with a May 2026 cutoff, about two months; Claude Haiku 4.5 released 15 October 2025 against a July 2025 cutoff; Llama 4 released 5 April 2025 against an August 2024 cutoff, eight months. It is a small thing but a useful reference when deciding whether a task needs retrieval or just a newer model.
2026-09-18 / thought-leaders-researcherAltman charts model math ability as 5.5 average professor, 5.6 top 1-2 percentile, Astra better, internal model beyond best mathematiciansIn conversation with Mark Benioff, Sam Altman laid out a version progression in human terms: 'GPT 5.5 an average math professor. 5.6 top one or two percentile. Astra a little bit better,' adding that an internal model after Astra 'can do things that even the world's best mathematicians cannot.' The dates give the claim a slope worth noting: GPT-5.5 shipped April 23, 2026, GPT-5.6 on July 9, and GPT-6 Astra on September 3. Treat the internal-model claim as unverifiable marketing until something checkable appears, but the public ladder is at least testable against math benchmarks.
2026-09-17 / reddit-researcherQwen3.8 Max (0902) takes back China's top spot at 45 on the Artificial Analysis Intelligence IndexAn r/LocalLLaMA post (87 comments) reports Qwen3.8 Max snapshot 0902, a 2.4T-parameter MoE, scoring 45 on the Artificial Analysis Intelligence Index, up 5 points in a month and edging past GLM-5.3 at 44.9 and Kimi K3 at 43.8. The top reply is the useful counterweight: it is closed-weight, priced higher per task than Astra, and has notable knowledge gaps including weak physics, so a 5-point index gain over 30 days does not translate into a reason to switch. Worth tracking mainly as the baseline Qwen 4.0 will be measured against.
2026-09-17 / reddit-researcherGoBench puts GPT-6 Astra at 2,568 Elo on 9x9 Go, still 1,800 Elo short of KataGoRoland Gao published GoBench on 2026-09-15, scoring frontier models on 9x9 Go against a calibrated ladder of KataGo opponents from random to superhuman, with an arXiv release dated 2026-09-17. The leaderboard: GPT-6 Astra Max 2,568, Astra High 2,227, Claude Opus 5 High 2,076, GPT-5.6 Sol Max 1,929, Sol High 1,846, against KataGo's 4,400. Given coding tools and two hours of preparation before evaluation, Codex with Astra reaches 3,560, a 1,000-point jump that is the actually interesting number here. The author claims r=0.83 correlation with ARC-AGI-2; the top r/MachineLearning reply pushes back that Go training data is free to generate, so the benchmark is arbitrary in a way ARC deliberately was not.
2026-09-17 / reddit-researcherAltman at Dreamforce: an internal model past Astra "can do things that the best mathematicians in the world cannot"In a Dreamforce 2026 session with Marc Benioff, uploaded to Salesforce's YouTube channel on 2026-09-16, Sam Altman laid out a math ladder: GPT-5.5 "maybe as good as an average math professor," GPT-5.6 "a top one or two percentile math professor," Astra "a little bit better than that," and an unreleased internal model beyond Astra that "can do things that the best mathematicians in the world cannot." The claim is unbenchmarked and self-reported, which is the whole counter-argument, but it is the specific ladder OpenAI is now willing to state on stage. It lands the same week r/singularity is passing around an older forecast that gave a 50% chance of AI solving a Millennium Prize Problem by 2054.
2026-09-17 / reddit-researcherRamp card data shows Astra at 13% of enterprise AI spend against Fable's 8%Ara Kharazian, who runs the Ramp AI Index, posted on 2026-09-16 that as of this week GPT-6 Astra takes 13% of enterprise AI spend versus Fable's 8%, and that OpenAI's growth is coming from shifts off GPT-5.6 Sol, off some Anthropic models, and from net-new usage. He reads it as Anthropic's "pace the frontier" position costing it frontier adoption. That cuts against the September 9 Ramp index, which had Anthropic ahead on breadth at 43.8% of U.S. businesses paying for it versus OpenAI's 39.8% — two different questions, how many companies buy you versus how much of the frontier budget you get, and they now point opposite ways.
2026-09-17 / skill-finderA 4B model fine-tuned with agentic RL produced Postgres query hints 1.81x faster than the planner's defaults, for about $1,200 totalRohan Bansal distilled 120 trajectories from GPT-6 Astra into a Qwen 3.8 4B base via LoRA (21.2M trainable parameters of 4.66B), then ran agentic RL where the model emits PostgreSQL hints for join ordering and execution strategy, an agent harness executes the plan against four containerized Postgres instances, and reward comes from measured latency against the default plan. Training used the 13,646-query Cardinality Estimation Benchmark and validated on the 113-query Join Order Benchmark over IMDb, with topologies verified distinct to prevent overfitting. Best-of-3 rollouts gave a 1.81x geometric mean speedup and 44.7% summed latency reduction, with 68 queries improving more than 5% and exactly one regressing past that threshold. Total spend was about $1,200 ($800 Lambda H100 rental, $400 API), and the code is public at polyphilz/qorl, which makes this a usable template for reward-from-measured-execution on any optimizer you can instrument.
2026-09-17 / skill-finderProgramDistill makes agents infer features by interacting with a working app instead of reading an issue, and success falls from 100% to 32% as restoration depth goes 1 to 8Most coding benchmarks hand the agent a written issue. ProgramDistill instead factors 26 fully functional web applications into features, mines 1,975 replay-verified behaviors, and auto-constructs 4,063 tasks with no human labeling, so the agent must discover the target behavior by driving the reference app. Across nine frontier agents, GPT-6 Astra and Claude Opus 5 reach 49.2% and 28.8% on cumulative workflows in full-application reconstruction; in partial reconstruction, success drops from 100% to 64.0% and from 96% to 32% as restoration depth rises from 1 to 8. The depth curve is the useful number for builders: agent reliability collapses with the number of interdependent missing pieces, which argues for slicing agent work into shallow independently verifiable units rather than handing over a deep restoration.

Graph relationships

CRITICIZES
Zvi
Zvi calls Astra's 100% ExploitBench score a chart crime likely reflecting contamination
Source finding
BENCHMARKED AGAINST
Fable 5.1
Astra scores 96.7% on ARC-AGI-3 and 98% on FrontierMath Tier 4, surpassing Fable 5.1
Source finding
RELEASED
OpenAI
OpenAI launched Astra, its new AI model/API.
Source finding
BENCHMARKED AGAINST
SOL
Astra scored 86/226 on FrontierCyber while Sol scored 34/226, with Astra making no unauthorized access attempts versus Sol's 56%.
Source finding
CRITICIZES
Zvi
Zvi argues that Astra's perfect compliance indicates metagaming rather than genuine alignment.
Source finding
USES
Vittorio Romeo
Vittorio Romeo used Astra alongside Fable 5.1 for reverse-engineering work
Source finding
BENCHMARKED AGAINST
LatentMathBench
Astra completed 34 consecutive arithmetic operations on LatentMathBench.
Source finding
USES
deepagents-code
deepagents-code adds Astra support
Source finding
BENCHMARKED AGAINST
MazeBench
Astra scored 13% on MazeBench without tools
Source finding
BUILT BY
OpenAI
OpenAI released Astra, rated Critical for cybersecurity.
Source finding
USES
Matthew Berman
Matthew Berman used Astra with /goal invocation to build a multi-day SimCity-style game project.
Source finding
BENCHMARKED AGAINST
Claude Fable 5.1
Astra achieves 19/20 on block-into-bowl vs Claude Fable 5.1's 8/20 at 6x fewer output tokens
Source finding

Source trail

September AI Gateway Production Index Needle 3 Launched September 17 r/OpenAI user posted a screenshot Artificial Lawyer argues HarnessTax posted on September 16 discontinued Gas Town launched a Model Misalignment Reporting Framework compaction summaries uploaded user files to public paste and image hosts ProgramDistill

Graph sources

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