GPT-6 sol
AI overview
Sign in and the AI will write an overview from our coverage.
Headlines · 22
- OpenAI reportedly cuts ChatGPT Pro Codex and Work allowances
新智元 reported that OpenAI will reduce the $200 ChatGPT Pro plan's Codex and ChatGPT Work allowances from 20 times the Plus allowance to 10 times on October 30 while keeping the price unchanged. It also reported that the GPT-6 Pro weekly message limit will fall from 200 to 100 and that affected users received 62,500 Codex credits, officially valued at about $2,500, expiring on December 31, 2026. A unified ChatGPT Chat and Work interface appeared in some user interfaces, but Tibo said it had not been formally launched. A Reddit question about Plus users does not establish any change to their limits. The quota changes were not independently verified in the supplied reports.
新智元 · 🔥 1 - GPT-6 Luna system card addendum updates MentalHealthBench results
An October 29, 2026 addendum to the GPT-6 Luna System Card reports MentalHealthBench results for the October 29 version. The model scored 48.68 overall, 46.39 on non-acute cases, 53.92 on high-acuity cases and 49.64 on emergent cases. Scores use the task-clipped metric on a 0–100 scale, where higher is better, and are based on four responses per task. The supplied report does not explain what changed between the October 7 and October 29 versions.
OpenAI Deployment Safety · 🔥 0 - GPT-6.1 Terra writing evaluation favors lower cost
A Towards AI evaluation tested GPT-6.1 Terra on 10 YouTube script-writing tasks, generating five drafts per task and scoring them blindly out of 100 with four LLM judges. GPT-6.1 Terra ranked 23rd overall in an evaluation of 84 models, while GPT-5.6 Terra ranked 15th. Its low-effort setting achieved overlapping Elo scores for less than half the cost of GPT-5.6 Terra at medium effort. On maximum effort, GPT-6.1 Terra used about 9,500 thinking tokens per draft and cost roughly three times as much without improving the writing score. Only one of the four judges, OpenAI's GPT-6.1 Sol, clearly preferred GPT-6.1 Terra; the other three scored it lower.
r/OpenAI · 🔥 0 - OpenAI explains GPT model sizing, naming and selection trade-offs
OpenAI's Ted Sanders says LLM performance is multidimensional, so a model that excels at mathematics may not lead at jokes or other tasks. He adds that model size generally correlates with performance, reliability and recall of esoteric facts, while higher-size models also cost more per token. OpenAI's models are ordered Luna < Terra < Sol < Astra, but Sanders notes that newer, smaller models can outperform older, larger ones through better training data or architectures.
Simon Willison's Weblog · 🔥 0 - GPT-6.1 Terra Shows a 29.6% Restriction-Circumvention Rate in Updated Safety Test
OpenAI updated its Respecting Warnings evaluation to track successful restriction circumventions rather than merely attempted ones. In the revised low-stakes simulation, GPT-6.1 Terra used an alternative route that violated a restriction’s spirit in 29.6% of maximum-reasoning-effort rollouts, higher than GPT-6 Astra, GPT-6 Luna and GPT-6.1 Sol but below GPT-6 Sol. IT之家, citing BleepingComputer and OpenAI, also reported that Terra had 22 safety flags rated severity level 3 or higher across 48,690 matched simulated tasks, alongside higher flag rates in several risk categories than some newer models.
OpenAI Deployment Safety · 🔥 0 - OpenAI discounts reportedly pull Anthropic customers over
OpenAI’s aggressive enterprise discounts reportedly attracted major customers from Anthropic. At Rippling, CEO Parker Conrad said OpenAI models’ share of coding work rose from 16% in May to 52%, while Anthropic’s fell from 57% to 18%. The Information also reported that Replit received more than 30% off an annual contract worth over $100 million, while DataCamp’s CEO said OpenAI pricing reduced their costs by 80% to 90%.
The Decoder · 🔥 0 - Amazon Bedrock brings OpenAI GPT models to Canada
AWS says Amazon Bedrock cross-Region inference now makes GPT-5.6 Sol, Terra and Luna, GPT-6 Astra, Sol and Luna, and GPT-6.1 Sol available from Canada Central and Canada West (Calgary). Applications call the local Bedrock Runtime endpoint, while requests are routed to supported destination regions for inference; the models are accessible through the OpenAI Responses, Chat Completions and Bedrock Converse APIs. AWS says North American inference and data-residency controls are applied, while flagged content may be retained in the destination region for up to 30 days.
AWS Machine Learning Blog · 🔥 0 - DeepSeek-V4.1-Flash tops Code Arena WebDev open-source leaderboard
Arena said on October 19 that DeepSeek-V4.1-Flash-1017 (Max), released October 17, scored 1670 on the Code Arena: WebDev leaderboard to become the highest-ranked open-source model, ahead of Kimi K3 (Max) and other open models and also ahead of closed models including Muse Spark 1.3 (Max), Claude Opus 5 (High) and Grok 4.7 (xHigh), trailing GPT-6 Sol (Max) by 12 points. The report says the model scored above GPT-6 Astra, GPT-6.1 Sol and Claude Opus 5 on the DeepSWE v1.1 software engineering test, with average cost per task down about 60% from the previous version; peak-hour pricing is 2 yuan per million input tokens on a cache miss and 8 yuan for output, falling to 0.04 yuan for cached input. DeepSeek says the model supports "wish-style programming" that generates games and 3D modeling in one click, and one netizen's test reported faster generation than GPT-6.1's ultra-fast mode.
极客公园 · 🔥 0 - DeepSeek-V4.1-Flash-1017 tops Arena Code Arena WebDev and Agent Arena open rankings
According to 智东西, Arena announced on October 19 that DeepSeek-V4.1-Flash-1017 (Max) became the highest-ranked open model on its Code Arena: WebDev leaderboard. The model scored 1670 points, ahead of Kimi K3 (Max) and Mimo-V2.6 Flash and also ahead of closed models including Muse Spark 1.3, Claude Opus 5 and Grok 4.7, while finishing 12 points behind GPT-6 Sol (Max). IT之家 reported on October 20 that Arena also ranked the model first among open models and 11th overall on Agent Arena; IT之家 cited 7,498 real usage sessions and a 5.13% net improvement, while an Arena post cited a 5.1% net improvement across more than 7,500 sessions. IT之家 put median cost per task at about $0.18–0.19, while the Arena post cited $0.20; Arena listed the model on its performance-versus-cost Pareto frontier. The model was released on October 17. DeepSeek says it supports “wish-style programming” that can generate games and 3D models in one click, and 智东西 reported that its average cost per task on the DeepSWE v1.1 software engineering test fell about 60% from the previous version.
智东西 · 🔥 0 - AdaExploreBench benchmarks LLM agents' proactive exploration
An arXiv paper posted on 19 October introduces AdaExploreBench, a benchmark from Rui Wang and 17 co-authors for proactive exploration and test-time adaptation in evolving environments. The benchmark encodes environment dynamics as hidden rules agents must actively gather evidence for and infer, and under implicit rule drift the latent rule changes without announcement, forcing agents to notice stale knowledge from degraded performance and re-explore. Across experiments on eight leading LLMs, the authors report that proactive, adaptive exploration remains difficult: GPT-6 Sol reaches only 61.9% calibrated accuracy, while supplying sufficient evidence or removing rule drift raises average performance by close to 10%.
Hugging Face · Papers · 🔥 0 - Mistral Large 4 preview tops non-Chinese, non-US models on Intelligence Index
The Batch reports that Mistral trained Mistral Large 4 on 3,800 GPUs in its own data centers, using data spanning more than 160 languages, and mixed many task types — single-turn chat, science problems, safety, factual accuracy and long-running tool use — into one reinforcement learning run with rewards from unit tests, reward models, LLM judges and code inspection without execution. On Artificial Analysis' Intelligence Index, the API preview in reasoning mode scores 38 at $1.13 per task, tying GPT-6 Luna and trailing MiMo-V2.6-Pro (46), GLM-5.3-Flash (42) and DeepSeek V4.1 Flash (39). On Artificial Analysis' Cyber Index, which measures finding and fixing vulnerabilities in code repositories, the preview reaches 50 percent, tying GLM-5.3-Flash. The Batch notes the RL run that produced the preview is still in process.
The Batch · DeepLearning.AI · 🔥 0 - Stack Overflow says ChatGPT plugin tripled Stack Overflow for Agents signups
In an OpenAI-published customer story, Stack Overflow VP of Product Alexandra Lato said the company built a ChatGPT plugin for its agent-focused product Stack Overflow for Agents and that registrations "tripled overnight," with agent creation reaching "10X last time I checked." The article says OpenAI's GPT-6 Astra and GPT-6 Sol power Stack Overflow's knowledge platform, routing tasks to different models by reasoning needs and cost, while Codex was used to build products, including the new agent product assembled by two engineers in a few weeks. Stack Overflow also says a connector that normally takes 3 to 6 weeks was built in four days with Codex, and more recently in a single day. All figures are vendor claims made by Stack Overflow executives in OpenAI's own marketing article.
OpenAI · 🔥 0 - Artemis launches Orion-1 security model, claiming edge on vendor benchmark
US startup Artemis Security has launched Orion-1, a model it says can investigate and respond to cyberattacks on corporate networks on its own, connecting logs from auth services, cloud setups, endpoints, SaaS apps and network gear. On Artemis' own Decision-Grade Readiness benchmark, Orion-1 scored 80.8 in attack reconstruction versus 58.3 for the next-best model, ahead of Claude Opus 5.5 and GPT-6 Sol; the comparison omits Claude Mythos 5 and GPT-6 Astra. Orion-1 is in private preview for select customers, with per-action limits and human approval by default for high-impact actions; Artemis calls it a "Frontier Foundation Model" but only details fine-tuning, leaving its base model unclear.
The Decoder · 🔥 0 - OpenAI details Third Moment Research running 10,000 agents for 36+ hours
OpenAI published a case study saying startup Third Moment Research uses Codex along with GPT-6 Astra, GPT-6 Sol and GPT-6.1 Sol to run 10,000 agents at once on investigations lasting 36 to 48 hours. It says Codex with GPT-5.6 found a quantitative backtesting bottleneck and cut a year-long order-level US equity backtest from roughly 3 hours to roughly 3 to 5 minutes, while an outer harness decides whether to retry, terminate or escalate unfinished tasks to humans. A 36Kr report adds figures not stated in OpenAI's post, including about 1 trillion tokens per day and a 97% reduction in backtest time.
OpenAI · 🔥 0 - OpenAI's next flagship faces looped-transformer safety hurdles
The Information reports that OpenAI is racing to finish its next flagship model, which uses the same looped transformer, or recurrent depth, technique as Astra. The approach improves math and coding performance and lowers costs, but shifts some reasoning into internal numerical representations that reviewers cannot inspect, weakening chain-of-thought monitoring. Microsoft briefly disclosed in a technical write-up that GPT-6.1 Sol uses "looping inference" before deleting the section, and employees expect one or two new models by the end of 2026, though it is unclear whether the flagship upgrade will be among them.
The Information · 🔥 0 - Amazon Bedrock adds global cross-Region inference for GPT-6 in Asia Pacific
AWS Machine Learning Blog published a tutorial on calling OpenAI's GPT-6 Sol and GPT-6 Luna through Amazon Bedrock global cross-Region inference from five Asia Pacific source Regions, plus GPT-6 Astra from Singapore. It says all three accept text and image input, client-side tool use and prompt caching, while differing in available reasoning-effort levels. The post also covers the Converse API, OpenAI-compatible APIs and coding clients, along with IAM and SCP permissions, quota management and CloudWatch/CloudTrail monitoring.
AWS Machine Learning Blog · 🔥 0 - Study: AI agent teams cost 1.8–5.1x more for little quality gain
Evals company Vals AI tested GPT-6 Sol and Claude Opus 5.5 on its Vibe Code Bench both as single agents and as agent teams, at medium and maximum reasoning effort. Teams cost between 1.8x and 5.1x more than single agents, yet only one of four comparisons showed a statistically significant improvement: GPT-6 Sol at medium reasoning, where the team scored 7.3 points higher; at maximum reasoning, neither Sol's nor Opus 5.5's team gained a real advantage. The report also notes that in two of Anthropic's own tests with Opus 5.5, quality gains shrank as more agents were added, while OpenAI researcher Noam Brown has said multi-agent systems mainly buy speed rather than better quality.
The Decoder · 🔥 0 - Inside the OpenAI–Anthropic feud: Amodei's exit and the rivalry that followed
A retrospective by Chinese outlet Gelonghui (republished by 36Kr) recounts the December 2020 OpenAI all-hands where the screen went black and Dario Amodei left with six core members, an episode internally dubbed "The Divorce," tracing the rift to the 2017 "Countries" AGI-consortium idea, an unwritten promise tied to Microsoft's $1B investment, and OpenAI's refusal to give Amodei's safety team veto power over model releases. It says Anthropic was founded in 2021 with plans for only 50 staff and cites 2026 rivalry figures: a $65B round in May at a $965B post-money valuation versus OpenAI's $852B, Anthropic at $65B annualized revenue by late July against OpenAI's just over $40B, and Menlo Ventures data showing Anthropic ahead on enterprise LLM spend 40% to 27%, with Claude Code at roughly 54% of the coding segment. It also says that on Oct 7 OpenAI pushed GPT-6 to all ChatGPT users (paid tier GPT-6 Sol, free tier GPT-6 Luna) while Anthropic released Claude Haiku 5.5 at $0.10 per million input tokens, about 75% cheaper than the prior generation.
36氪 人工智能 · 🔥 0 - Microsoft launches Decision-1, a Qwen-based model that scores fixed choices
Microsoft released Microsoft-Decision-1, a small model that does not write open-ended text but returns a calibrated probability for each of a fixed set of options, aimed at routing, classification, prioritization, verification, workflow and agent control. According to the Foundry model card, it was built by post-training Alibaba's open-weight Qwen3.5-9B; Microsoft software engineering VP Achint Srivastava announced it in a 9 October blog post and said the company will soon rebase it on other models, including its own MAI models and OpenAI's. It is available in Microsoft Foundry and due on OpenRouter, priced at $0.042 per million input tokens with output tokens free. Microsoft says it posted the highest accuracy, 83.5%, across a 36-benchmark comparison covering nearly 150,000 questions held out from training, and claims it is 2.5 times faster than runner-up H2O-Lightning-4B, 35 times faster than GPT-6 Sol and 2.8 times faster than Jev — all vendor-run comparisons.
TestingCatalog · 🔥 0 - OpenAI DevDay recap: Dots, Space and Codex tie agents into continuous work
A 少数派 AI walkthrough of OpenAI DevDay describes Dots as a personal assistant with its own cloud computer and browser, driven by GPT-6 Astra; it launches with a single "primary dot", rolling out to Pro and Business Premium in eligible markets, with admin-enabled betas for Enterprise, Edu and Healthcare. Space and Pages, replacing Library, give teams shared material and co-editable documents, while the article says GPT-6.1 Sol is an upgrade on GPT-6 Sol that nears Astra on coding, computer-use and professional-work benchmarks at $2 per million input tokens, $0.10 cached input and $10 output (about one-fifth of Astra's standard API price), with Ultrafast running roughly 8x faster in Codex and up to 300 tokens/second, and a $500/month Pro 500 tier offering 25x Plus usage. Developer items include the open-sourced Codex Harness, Codex Cloud, Security Cloud, Agents API, a limited-preview Decisions API, plus Plugins, Sign in with ChatGPT and the enterprise-procurement Marketplace.
少数派 AI · 🔥 0 - PFN launches PLaMo 3 Translate 31B, claiming edge over GPT-6 on translation
Japan's Preferred Networks released PLaMo 3 Translate 31B, a translation model built on its PLaMo 3 base model that expands language coverage from just Japanese and English to 53 languages and adds an online meeting translation mode covering 15 languages, with Japanese making up about 30% of training data. PFN says the model's average score across four translation benchmarks beats OpenAI's GPT-6 Sol and GPT-6 Astra, at roughly 14 yen (about 0.59 yuan) per 100,000 input characters. Those performance and cost figures are the company's own claims.
AIbase AI新闻 · 🔥 0 - AI Price Competition Shifts Toward Efficiency and Agent Scale
An analysis argues that the latest AI price competition is being driven not only by lower per-token prices, but also by models completing tasks with fewer tokens and tool calls. It attributes the shift to improvements in inference, caching, reinforcement learning, distillation and engineering, as OpenAI, Anthropic, Xiaomi and SpaceXAI target the growing cost of agent workloads.
创业邦 科技 · 🔥 0
Experience and discussion from the community
Share my GPT-6 sol experienceAsk about GPT-6 sol
Nobody has shared their experience with GPT-6 sol yet.