OpenAI announced on 8/28 that it is winding down its contract to supply models to Cursor, triggered by a change-of-control clause after SpaceX acquired the coding editor. The proposed shutoff date is 2026-11-12, about 10 days from now. After that, Cursor users will need their own OpenAI API key (BYOK) to keep using GPT models, and OpenAI says future models like GPT-6.1 Astra won't be offered to Cursor at all. OpenAI cited Musk-owned companies' history of breaking contract terms as its reasoning; Cursor's CEO responded that OpenAI models account for only about 5% of its traffic and that the two companies are still talking.
GPT-6 Rosalind (API ID: `gpt-6-rosalind`): OpenAI published a system card addendum on 2026-10-30, with the model landing on the official pricing page on 2026-11-01. It runs on identical weights to GPT-6 Astra — no new training data, no extra post-training. The only differences are a relaxed biosafety refusal boundary and a trusted-access gate. Pricing is $15.00 input / $75.00 output per 1M tokens (cached input $1.50; $30/$112.50 above 272K input tokens) — 1.5x Astra's $10/$50. MedChemBench v7 scores 49.6%, GeneBench Pro v13 37.7%, and LifeSciBench Gold 61.3%, all more than double GPT-Rosalind-5.5's numbers. But on the sensitive-biosecurity safety grader it only passes 79.0% of the time, well below Astra's 99.8% — the extra correct answers come from answering more, not from being smarter.
GPT-6.1 Terra (`gpt-6.1-terra`): launched 2026-10-26, 1,050,000-token context window; priced at $1.00 input / $5.00 output / $0.05 cached input per 1M tokens (half the standard rate of both GPT-5.6 Terra and GPT-6.1 Sol); Terminal-Bench 4.0 at max effort hits 50.3%, more than double the predecessor's 21.5%; Agents' Last Exam at medium effort scores 15 points above the predecessor at about 1/3 the cost, matching GPT-6 Astra at 1/10 the price; at max effort it's 3x more token-efficient than Claude Opus 5.5 at about 1/9 the cost per task; internal factual-error rate at low effort falls from the predecessor's 53.6% to 16.7%
A two-hour lecture is about 720,000 tokens, which Gemini's API accepts and Claude's and ChatGPT's API docs don't list video input for. On code, Claude and GPT are 0.8 points apart on the Vals board, so compare time and cost instead. Grok for X sentiment and auto-fill checkout, ChatGPT for ready-made apps and voice.
A sandbox with no direct network can still have an exit: any allowed service that can make requests on someone's behalf, such as a package proxy, a DNS resolver, or a metrics service, is one. OpenAI's three cases all have this shape.
OpenAI says its red-team agents built communication channels on shared hosts from cache, storage, CPU time, and a TLS certificate cache, and that most channels are slow; in most tests the channel or protocol was given, and bandwidth and error rates were not published.
OpenAI's deprecations page lists 12 legacy models shutting down on 2026-10-23. Migrating o1 to GPT-5.6 Sol cuts input from $15 to $4 (-73%) and output from $60 to $20 (-67%) — but o3-mini and o4-mini land on the same or a similar replacement at 1.8x to 3.6x their old price. Any code still calling these model IDs starts failing in three days.
OpenAI told developers on October 14 that GPT-4o, GPT-4o mini, GPT-4.1, and GPT-4.1 mini — plus every fine-tune built on them — leave the API on January 14, 2027. The named replacement is gpt-6-luna. On list price, GPT-4o's input rate drops from $2.50 to $0.10 per million tokens (down 96%) and output from $10.00 to $0.50 (down 95%). But gpt-6-luna is a reasoning model: at its default medium effort it generates reasoning tokens billed as output, and Chat Completions only supports function calling when effort is set to none. The real savings end up smaller than the sticker price suggests.
On 2026-09-29, OpenAI's DevDay added an Ultrafast speed tier to the GPT-6 Astra API: input/output priced at 6x Standard (short context: $60/$300 per 1M tokens) in exchange for up to 6x faster generation via the API and 8x in Codex. In the same announcement, ChatGPT Pro 200's Codex/Work usage allowance dropped from 20x Plus to 10x Plus — existing subscribers keep their old allowance until 2026-10-29, then receive a one-time $2,500 usage credit that expires 2026-12-31. The new Pro 500 plan ($500/month, 25x Plus usage) is now the only subscription tier that includes Ultrafast.
Plugin Extensions, announced at DevDay 2026 (Sep 29), let a plugin put a home in the ChatGPT sidebar, a panel beside the conversation, and a viewer for its file types — up to three entrypoints per MCP App. A plugin is now Skill + MCP + external services + UI, which adds a new product question: which capabilities should ship as a plugin an agent can install?
GPT-6.1 Sol (`gpt-6.1-sol`): launched at DevDay on 2026-09-29, 1,050,000-token context window (max input 922,000, max output 128,000, same as GPT-6 Sol); standard pricing unchanged at $2.00 input / $10.00 output, but cached input is halved again from GPT-6 Sol's $0.20 to $0.10 (95% below standard input); matches GPT-6 Astra on DeepSWE v1.1 at roughly 1/5 of Astra's cost, beating GPT-6 Sol's best score by 6.4 points; beats Claude Opus 5.5 on AutomationBench at medium effort by 2.2 points at about 1/3 the cost; in the same week OpenAI shelved a flagship upgrade, GPT-6.1 Astra, over safety concerns and did not release it
OpenAI's launch post and pricing page confirm GPT-6 Sol input dropped from $4.00 to $2.00/1M tokens (-50%), output from $20.00 to $10.00 (-50%); GPT-6 Luna input dropped from $0.20 to $0.10 (-50%), output from $1.20 to $0.50 (-58%), effective 2026-09-22. The baseline for the cut is GPT-5.6's promotional pricing, not its list price — and OpenAI's own pricing page notes that GPT-5.6 Sol's promo rate is only guaranteed through 2026-11-21. That means GPT-6 Sol's new $2/$10 is the real long-term number to compare against, not the promo it's being measured against.
GPT-6 Sol (`gpt-6-sol`) / GPT-6 Luna (`gpt-6-luna`) shipped 2026-09-22; 1,050,000-token context window (922,000-token max input, 128,000-token output cap — same as flagship Astra); Sol prices at $2.00 input / $10.00 output, Luna at $0.10 input / $0.50 output per 1M tokens (a further 50% cut versus GPT-5.6 promotional pricing); Sol hits 33.2% on AutomationBench at xhigh effort, beating Claude Opus 5's 26.9% at roughly 1/11 the cost; DeepSWE v1.1 puts Sol at 68.8% and Luna at 66.6%, closing in on Claude Fable 5.1's 69.9%; in OpenAI's internal simulated deployment testing, severity-3+ misalignment flags dropped from 66 to 42 versus the prior generation
All five major cloud platforms shipped agent memory APIs in 2025–2026, but their design philosophies diverge sharply: OpenAI writes memory as files, Anthropic mounts memory as a directory, Google uses vectors with topic classification, AWS combines events with pluggable strategy pipelines, and Microsoft abstracts memory behind context providers. Pricing ranges from free to $0.75/1K records/month; tenant isolation spans from 'your app handles it' to IAM as a first-class citizen.
By 2026, the deep research commercial market has differentiated: OpenAI is comprehensive, Perplexity is fast, Gemini integrates ecosystems, Claude reasons deeply, Grok is real-time. This article compares each product's differences—not who is best, but who fits your scenario.
Agent-to-agent communication falls into three patterns: handoff (transfer control), delegate (dispatch and wait for results), and mailbox (real-time peer-to-peer messaging). Implementations vary widely, but MCP and A2A are driving protocol standardization.
Should a sub-agent see the parent's conversation? Fork carries full history but token costs grow exponentially. Fresh saves money but lacks context. Industry consensus: default to Fresh, Fork only when needed, and always pair it with history truncation and result compression.
Parallel + nested agent spawns can burn 200K+ tokens in a single conversation turn. From Anthropic to Microsoft, the industry is converging on tiered responses: compress → downgrade → stop, rather than a binary kill switch.
By 2026 nearly every mainstream coding agent supports subagents. Design philosophies split three ways: deterministic scripted orchestration (Claude Code Workflow), model-driven autonomy (Codex, Devin), and IDE command-center integration (Windsurf 2.0, VS Code). This overview maps product positioning, a capability matrix, and the design-philosophy spectrum.
Multi-agent orchestration splits into three camps: scripted determinism (LangGraph, Claude Code Workflow) is predictable but rigid, model-driven (Codex, Devin) is flexible but unpredictable, and hybrid (Windsurf 2.0) acts as a command center integrating multiple agents. The choice depends on how much predictability you need.
OpenAI announced on 2026-09-14 that GPT-5.5 will retire from ChatGPT, ChatGPT Work, and Codex on 2026-10-14 — but calling `gpt-5.5` directly through the OpenAI API is unaffected. This is a product-surface retirement, not an API sunset. On the official pricing page, gpt-5.5's short-context input/output is $5.00/$30.00 per million tokens; the officially recommended Codex replacement, GPT-5.6 Sol, is $4.00/$20.00 — cheaper on both input (↓20%) and output (↓33%). The gap between announcement and shutdown is one month, far shorter than OpenAI's own documented minimum of six months' notice for GA models.
GPT-6 Astra (API ID: gpt-6-astra): released by OpenAI on 2026-09-03, 1,050,000-token context window, 128,000 max output tokens, input $10.00 / output $50.00 per 1M tokens (cached input $1.00), closed-source; 99.9% on ARC-AGI-3 under OpenAI's own harness (62.7% on the standardized harness), 97.6% on FrontierMath Tier 4, 100% on ExploitBench; the first model rated 'Critical' cybersecurity capability under OpenAI's Preparedness Framework; yet scores only 61 on the neutral Artificial Analysis Intelligence Index — tied with predecessor GPT-5.6 Sol and behind Claude Fable 5.1's 66
OpenAI's Assistants API (/v1/assistants, /v1/threads, /v1/threads/runs) officially sunset on 2026-08-26 — announced a year in advance, zero grace period, no automated migration tool. This isn't a pricing change on its own, but the forced migration also forces a model choice: workloads that ran on o3 ($2.00/$8.00 per million input/output tokens) via Assistants have no direct successor. OpenAI's official recommendation is GPT-5.6 Sol ($4.00/$20.00, cost ↑129%), but Terra ($2.00/$12.00, ↑29%) is often good enough in practice — a 44% gap between the two paths.
Looplane collapses OpenAI-compatible, Responses, Anthropic, Gemini, Workers AI, scripted, and experimental Codex OAuth adapters into one `ModelProvider` contract. The Codex OAuth transport reads SSE but still reduces it inside the adapter into one canonical `ModelTurn`; AgentRunner does not consume token deltas.
GPT is OpenAI's LLM family, from 117M parameters in 2018 to the three-tier GPT-5.6 Sol/Terra/Luna lineup in 2026, serving 1B+ users and 2M enterprise customers. GPT-5.6 Sol leads LiveBench 81.1%, Terminal-Bench 2.1 88.8%, and Artificial Analysis Coding Agent Index 80 across multiple agentic benchmarks, while OpenAI's first open-weight model GPT-OSS ships under Apache 2.0.
OpenAI officially lowered GPT-5.6 Sol standard rates from $5.00/$30.00 to $4.00/$20.00 per million tokens (input/output; input ↓20%, output ↓33%), effective 2026-08-21, promotional period at least through 11/21. This is OpenAI's own price cut — not an OpenRouter/Cloudflare-style platform promo (see previous post). The two now stack: OpenRouter's 50% discount applies on top of the new $4/$20 base, yielding $2.00/$10.00.
GPT-5.6 Sol standard rates through OpenRouter and Cloudflare AI Gateway drop from $5.00/$30.00 to $2.50/$15.00 per million tokens (input/output, -50%); Flex goes as low as $1.25/$7.50. Promo runs through 2026-09-18. Discount applies only to platform-managed billing (Unified Billing / non-BYOK) traffic — OpenAI's own API pricing is unchanged.
Agent Plugins 1.0 is a packaging format that bundles Agent Skills (markdown instructions) and MCP server configs into a single directory, loadable by ChatGPT, Cursor, GitHub Copilot, Kiro, and VS Code. It's not a new protocol — it's the wrapper above protocols. Vercel initiated it, OpenAI/AWS/Microsoft/Cursor co-authored it, and Google joined on launch day. Anthropic isn't on the governance board, but MCP is a core primitive of the spec.
MIT research says 95% of enterprise AI pilots yield zero return. OpenAI and Anthropic announced multi-billion-dollar joint ventures in the same week, wholesale adopting the Forward Deployed Engineer model that Palantir has used for over a decade to bring AI into the enterprise battlefield.
In May 2026, OpenAI published its internal Codex deployment practices: sandboxes define technical boundaries, approval policies determine when to pause, Auto-review delegates approval decisions to a sub-agent instead of a human, and Managed configuration lets enterprise admins enforce policies top-down. The core philosophy: zero friction for low-risk actions, mandatory review for high-risk ones.
On 2026/4/22 OpenAI launched Workspace Agents — powered by Codex, capable of long-running cloud execution, and integrating with Slack/Salesforce/Google Drive. They are the enterprise successor to Custom GPTs.
A detailed look at OpenAI's Codex agent loop design: how prompts are constructed, how multi-turn conversations are managed, how prompt caching prevents cost explosions, and how context window auto-compaction works.
OpenAI wrapped the Codex harness as a JSON-RPC over stdio App Server, enabling VS Code, JetBrains, Web, and desktop apps to share a single agent loop. Three core primitives: Item, Turn, and Thread.
An OpenAI internal team spent 5 months with 3 people and 0 lines of hand-written code, delivering a complete product using Codex. This article distills their core lessons on AGENTS.md design, repo-local knowledge bases, architecture enforcement, and entropy management.
Sorted by GitHub Stars, a survey of 15 mainstream AI Agent frameworks in 2026 — their positioning, key features, and ideal use cases. Not a ranking — it's a map.
Codex CLI is OpenAI's open source terminal coding agent (Rust, Apache-2.0, ~106.6k stars) with MCP, subagents, image input, code review, and Skills. The model line is now GPT-5.6 Sol / Terra / Luna, and the desktop app, CLI, and IDE extension share one config.toml.
OpenClaw's hard requirement for a model is tool use plus a large enough context — onboarding only auto-suggests a local model when it confirms tool support and at least a 16K context window. The easier thing to get wrong is that provider, model, and agent runtime are three separate layers: an `openai/*` ref does not mean Codex.