--- title: "GPT-6 Astra: Pricing, Benchmarks and the Harness Caveats" description: "A senior lab's read on GPT-6 Astra: $10 / $50 with a 272K repricing cliff, the Critical cyber rating, and the disclosed monitorability regression." source: "https://www.kensink.com/models/openai-gpt/gpt-6-astra/" canonical: "https://www.kensink.com/models/openai-gpt/gpt-6-astra/" --- ★ NEW · FIRST MODEL RATED CRITICAL FOR CYBER OpenAI Model brief OPENAI GPT-6 · ASTRA · 3 SEP 2026 # GPT-6 Astra. Records where it was aimed, flat everywhere else. OpenAI's most capable deployed model sets records on computer use, terminal work, long context, and cybersecurity, and lands roughly level with the Anthropic frontier on general coding and reasoning. It is also the first model OpenAI has classified Critical for cyber capability under its Preparedness Framework, which changes the access story as much as the benchmark story. LLM API gpt-6-astra Computer use Eval pipelines [Use it on your project →](https://www.kensink.com/contact) [OpenAI announcement ↗](https://openai.com/index/gpt-6-astra/) Released 3 Sep 2026 Flagship model ID gpt-6-astra Sol input $10 / 1M tokens Sol output $50 / 1M tokens Context 1.05M context Max output 128K max output Modalities Text + image → text Knowledge 30 Apr 2026 cutoff \[TL;DR FOR CEO + CTO\] ## Five things to know. - 01 ### It wins on computer use, terminals, and cyber, not on general intelligence. ScreenSpot-Pro goes from 76.9% to 92.7% against GPT-5.6 Sol, OSWorld 2.0 from 65.7% to 72.6%, and SRE-Bench single-attempt solve rate from 55.9% to 88.0%. On the Artificial Analysis Intelligence Index it scores 61.2, below Claude Fable 5.1 at 65.7 and Opus 5 at 63.1. Both statements are true at once, and which one matters depends entirely on what you are building. - 02 ### Coding is a tie, not a takeover. Terminal-Bench 4.0 is a genuine win at 57.7% against 55.8% for Fable 5.1. But FrontierCode 1.1 Main puts Astra at 53.3% against 53.5% for Fable 5 and 53.4% for Opus 5, and the Artificial Analysis Coding Agent Index has all three inside a point and a half. If you route on coding alone, this release does not move your decision. - 03 ### The pricing is Fable-tier, with a long-context cliff. $10 input and $50 output per million tokens, matching Claude Fable 5.1 exactly. Cached input is $1, which is four times what Fable 5.1 charges for a cache read. Cross 272K input tokens and the entire request reprices at 2x input and 1.5x output, so a single long request costs $20 / $75. - 04 ### First model classified Critical for cybersecurity. OpenAI says Astra can find previously unknown security flaws and develop new ways to exploit them across many well-protected systems. It scores 100% on ExploitBench and achieves arbitrary code execution on 39.0% of a contamination-controlled set of 20 high-severity V8 vulnerabilities, against 5.5% for Sol. Access to cyber and biology capability runs through separate trust-based tiers. - 05 ### OpenAI disclosed that its reasoning got harder to monitor. The system card reports a substantial decrease in chain-of-thought monitorability against previous models, and says that if the trend continues across generations they would soon have significantly reduced confidence in detecting many forms of misaligned behaviour. That is a candid disclosure and a real operational risk for anyone running long autonomous agents. \[VERIFIED PERFORMANCE\] ## How it stacks up. OpenAI's launch numbers set against GPT-5.6 Sol, which Astra replaces at the top of the lineup, and against the Anthropic frontier two days earlier. Read the caveats under the table before you read the table: several of these were run under harnesses that are not directly comparable across vendors. | Capability | GPT-6 Astra | GPT-5.6 Sol | Claude Fable 5.1 | Claude Opus 5 | | --- | --- | --- | --- | --- | | Agentic terminal coding Terminal-Bench 4.0 | 57.7% +20.4 pts vs Sol | 37.3% | 55.8% | 52.3% | | Scientific terminal work Terminal-Bench Science Sol and Opus 5 figures as Anthropic published them | 64.6% +42.2 pts vs Sol | 22.4% | 52.6% | 29.0% | | GUI grounding ScreenSpot-Pro | 92.7% +15.8 pts vs Sol | 76.9% | not published | not published | | Agentic computer use OSWorld 2.0 variant not stated; Anthropic reports partial and strict separately | 72.6% +6.9 pts vs Sol | 65.7% | 77.9% partial / 41.7% strict | 75.4% partial / 39.6% strict | | Agentic automation AutomationBench | 41.4% +23.3 pts vs Sol | 18.1% | 31.4% | 26.9% | | Incident response SRE-Bench, single attempt 99.2% within four attempts, against 68.7% for Sol | 88.0% +32.1 pts vs Sol | 55.9% | 12.5% | not published | | Long-context retrieval MRCR v2, 8 needles, 512K to 1M | 96.3% +22.5 pts vs Sol | 73.8% | not published | not published | | Frontier mathematics FrontierMath Tier 4 (v2) Epoch AI notes OpenAI funded the benchmark's development | 97.6% | not published | 87.8% | 73.2% | | Abstract reasoning ARC-AGI-3 99.9% on OpenAI's Provider Adapter harness, 62.7% on the standard one | 99.9% +92.1 pts vs Sol | 7.8% | not published | 30.2% | | Multidisciplinary reasoning Humanity's Last Exam, with tools omitted from the launch narrative | 57.2% | not published | 65.0% | 63.6% | | General intelligence Artificial Analysis Intelligence Index v4.1.1 third-party index, Astra places fourth | 61.2 | not published | 65.7 | 63.1 | | Exploit development ExploitBench run without production safeguards on both models | 100.0% +21.5 pts vs Sol | 78.5% | 70.0% | not published | Every figure above was run at maximum effort unless the row says otherwise, which is not how anyone runs a model in production. Four caveats travel with this table. The ARC-AGI-3 headline of 99.9% is on OpenAI's own Provider Adapter harness and drops to 62.7% on the standard one, while rival models were scored under different setups. Epoch AI, which runs FrontierMath, notes that OpenAI funded its development. The BenchCAD comparison ran Claude under modified evaluation settings. The ExploitGym comparison removed the usual six-hour time limit, and the cyber benchmarks ran without production safeguards on both models. We re-run our own evals on customer tasks before recommending a switch, which is the only number that has ever decided a routing call for us. \[SOFTWARE DEVELOPMENT IMPACT\] ## What it changes for the team building with it. What changes for the engineering team. Two comparisons carry the decision: Astra against the GPT it replaces, and Astra against Claude Fable 5.1, which arrived two days earlier at exactly the same headline price. | Dimension | vs GPT-5.6 Sol | vs Claude Fable 5.1 | | --- | --- | --- | | Computer use and browser agents | The clearest upgrade in the release. ScreenSpot-Pro grounding goes from 76.9% to 92.7% and OSWorld 2.0 from 65.7% to 72.6%. If you run a browser or desktop agent, this is the reason to look at Astra at all. | Astra leads on the GUI grounding numbers each vendor published, though the OSWorld variants are not stated consistently enough to declare a winner. Anthropic reports Fable 5.1 higher on OSWorld 2.0 partial credit. Run both on your own flows. | | Coding | A large jump on terminal work (37.3% to 57.7% on Terminal-Bench 4.0) and a modest one elsewhere. DeepSWE v1.1 moves 72.7% to 74.1%, which is inside the noise most teams can measure. | Effectively a tie. Astra edges Terminal-Bench 4.0, Fable 5 edges FrontierCode 1.1, and the Artificial Analysis Coding Agent Index puts Fable 5 at 68.1%, Fable 5.1 at 67.2%, and Astra at 67.0%. Nobody should re-platform a coding agent on this. | | Cost | Two and a half times Sol on both input and output ($10 / $50 against $4 / $20). Sol stays the right default for most production work, and Astra is a routing target for the tasks that justify the rate. | Identical headline pricing. The difference is in caching: Astra reads cached input at $1 per million, Fable 5.1 at $0.25. On a long agent session that re-reads a big cached prefix every turn, that is a four-to-one gap in Anthropic's favour on the largest line of the bill. | | Long context | MRCR v2 eight-needle retrieval in the 512K to 1M band goes from 73.8% to 96.3%, which is the difference between a window you can advertise and one you can rely on. Same 1.05M window on both. | Fable 5.1 prices its full 1M window at the standard per-token rate with no surcharge. Astra reprices the entire request at 2x input and 1.5x output above 272K input tokens, so a genuinely long request costs $20 / $75. If your workload lives past 272K, this is the deciding line. | | Operational risk | Better on most safety measures: about 53% fewer severity-3-or-above misalignment flags across more than 54,000 internal tasks, indirect prompt-injection robustness at 99.79% against 96.23%, and an internal hallucination rate of 4.2% against 12.2%. Against that, a disclosed regression in chain-of-thought monitorability. | Different postures. Anthropic gates cyber capability down and reports Fable 5.1 as its most prompt-injection-robust model. OpenAI ships a Critical cyber classification with trust-tiered access. For regulated buyers this is a procurement conversation, not a benchmark one. | Inside a Kensink build, model selection is a routing decision the agent makes at runtime, not a vendor commitment frozen at design time. Astra changes our routing for computer use and incident response, and changes nothing about where we send general coding work until a customer eval says otherwise. \[MODEL SPEC + WHAT IS NEW\] ## The features that ship with it. 01 ### Five effort levels, including xhigh and max Reasoning effort now runs low, medium, high, xhigh, and max. Every headline benchmark in the launch was run at maximum effort, which is worth remembering when you compare a launch number to what you see at the level you can actually afford. Reasoning tokens bill as output at $50 per million. 02 ### A 1.05M context window that holds up under retrieval The window is 1,050,000 tokens with a 922,000 maximum input and 128,000 maximum output. The number that matters is MRCR v2 eight-needle retrieval at 96.3% in the 512K to 1M band, against 73.8% for Sol. A window you can advertise is not the same as a window you can put an agent inside. 03 ### The 272K repricing cliff Requests above 272,000 input tokens are billed at 2x input and cache rates and 1.5x output for the full request, not just the excess. Cache writes bill at 1.25x the uncached input rate. Anyone planning to use the full window should model cost at the long-context rate of $20 / $75 rather than the headline $10 / $50. 04 ### Critical cyber capability, and the access tiers that come with it Astra is the first model to reach Critical for cybersecurity under OpenAI's Preparedness Framework. The public model is restricted and refuses in cyber areas; genuine security research runs through separate trust-based access tiers. OpenAI also delayed the release after its July 2026 Hugging Face incident to add safeguards. 05 ### Misalignment monitoring on all tool-using inference OpenAI added a misalignment monitoring system across tool-using inference, plus production classifiers layered on top of base model behaviour, confirmation policies for consequential actions, and age-appropriate boundaries for under-18 users. This is infrastructure around the model, not behaviour inside it, and it can refuse things your evals passed. 06 ### A candid monitorability regression The system card reports a substantial decrease in chain-of-thought monitorability compared with previous models, and states plainly that a continued trend would soon leave OpenAI with significantly reduced confidence in detecting many forms of misaligned behaviour. Publishing that is the right call, and it belongs in your risk register if you run long autonomous agents. \[WHAT CHANGES IN YOUR CODE\] ## The API surface, exactly. Astra is not a drop-in for every GPT-5.6 call site. Nothing here throws on a simple text completion, but the modality surface is narrower than the model it sits above, and the cost model has a cliff in it. Breaking ### Only three endpoints, and no audio or embeddings Astra serves Chat Completions, Responses, and Batch. Realtime, Assistants, Fine-tuning, Embeddings, Image generation, Video, Audio, and Moderation are all unsupported. Any code path that treats the flagship model ID as a general-purpose default across the OpenAI surface will fail on the endpoints it does not serve. Route audio to the realtime and transcription models and embeddings to the embedding models, as separate concerns. ``` v1/chat/completions · v1/responses · v1/batch ``` Breaking ### Text and image in, text only out Astra accepts text and images and returns text. It does not generate images, audio, or video. If you were relying on a single model ID for multimodal output, that path needs splitting: gpt-image-2 for images, the realtime models for speech. ``` input: text, image · output: text ``` New ### Effort levels xhigh and max Two levels above high. They buy accuracy on the hardest tasks and they buy it with reasoning tokens, billed as output. Treat max as an escalation path for a retry rather than a default, and measure cost per completed task rather than cost per call. ``` reasoning: { effort: "low" | "medium" | "high" | "xhigh" | "max" } ``` New ### Prompt caching at $1 per million Cached input reads bill at $1 per million tokens against $10 uncached, a 90% discount, and cache writes bill at 1.25x the uncached input rate. Worth wiring properly for any agent with a large shared preamble, though note that Claude Fable 5.1 charges $0.25 for the equivalent read. ``` cached input: $1.00 / 1M · cache write: $12.50 / 1M ``` New ### Batch and Flex at half price, Fast at double Batch and Flex run at 50% of standard rates, and Fast mode at 2x. For anything that is not user-facing and not latency-bound, batch turns a $10 / $50 model into a $5 / $25 one, which is the cheapest lever available on this model. ``` batch / flex: 0.5x · fast: 2x ``` New ### Standard tool surface carries over Streaming, function calling, structured outputs, file search, web search, image input, and prompt caching all work as on GPT-5.6. If your integration only uses these, the model string is genuinely the only change. ``` streaming · function_calling · structured_outputs · file_search · web_search ``` Rate limits are tier-gated and worth checking before a load test: tier 1 starts at 500,000 TPM, and tier 5 reaches 15,000 RPM, 40,000,000 TPM, and a 15,000,000,000 token batch queue. Rollout was staged from a limited partner set on day one to ChatGPT Plus, Pro, Business, and Enterprise, then the API and AWS. \[THE SPEC\] ## Everything an integration depends on. Model IDs on every platform, the limits, and the two lines that decide whether procurement signs off. Kept here so nobody has to reconstruct it from three vendor pages.
Model IDgpt-6-astra
Context window1,050,000 tokens
Maximum input922,000 tokens
Max output128,000 tokens
Knowledge cutoff30 April 2026
ModalitiesText and image in, text out. No audio, no image output
Reasoning effortlow, medium, high, xhigh, max. Reasoning tokens are billed as output
EndpointsChat Completions, Responses, Batch
Not supportedRealtime, Assistants, Fine-tuning, Embeddings, Image generation, Video, Audio, Moderation
FeaturesStreaming, function calling, structured outputs, file search, web search, image input, prompt caching
Long-context threshold272,000 input tokens. Above it the whole request reprices
Rate limit, tier 1500,000 TPM
Rate limit, tier 515,000 RPM, 40,000,000 TPM, 15,000,000,000 batch queue
AvailabilityOpenAI API and AWS. Staged rollout from a limited partner set on day one
\[MIGRATION\] ## How we move a live integration. Astra is an escalation target, not a global default. This is the order we work in when a customer asks whether to adopt it. From GPT-5.6 Sol 1. 01 ### Decide whether your workload is one Astra is good at Computer use, browser and desktop agents, terminal work, incident response, long-context retrieval past 272K, and frontier maths. If your workload is general coding or general reasoning, the published numbers say Astra is level with what you already have, and the migration is not worth running. 2. 02 ### Model the cost at your real context length The headline is $10 / $50, but above 272,000 input tokens the whole request reprices to $20 / $75. Take your p95 request size, not your median, and price it there. Then check the batch path: half rates on anything that is not user-facing. 3. 03 ### Audit call sites for unsupported endpoints Grep for anywhere the flagship model ID is used against audio, embeddings, image generation, moderation, assistants, or realtime. Those are separate models now and need separate routing. 4. 04 ### Re-run evals at the effort you will actually pay for Every launch number was measured at maximum effort. Run your eval suite at low, medium, and high, and find the level where the quality gain stops paying for the reasoning tokens. That level, not the launch slide, is your production setting. 5. 05 ### Put the safety infrastructure in your integration tests Misalignment monitoring runs across tool-using inference, with production classifiers on top and confirmation policies for consequential actions. Any of these can refuse a request your evals passed. Handle refusals as a response shape and test the path. 6. 06 ### Add monitorability to the risk register, not the backlog OpenAI disclosed a substantial drop in chain-of-thought monitorability. If you run long autonomous agents, that argues for more external verification: independent checks on agent output, tighter tool permissions, and a human gate on consequential actions. \[USE CASES · ROUTING\] ## Which tier for which job. Which model for which job, across the current OpenAI lineup. Astra sits at the top as an escalation target, and most production traffic still belongs further down the list. ### GPT-6 Astra gpt-6-astra - Browser and desktop agents that click real interfaces - Incident response and SRE automation - Terminal and scientific-computing agents - Retrieval across 500K tokens and up - Frontier mathematics and hard research problems $10 / $50 standard, $20 / $75 above 272K input tokens. Reach for it when a cheaper tier has already failed your eval, and measure cost per completed task rather than cost per call. ### GPT-5.6 Sol gpt-5.6-sol - General coding and multi-file work - Complex reasoning that does not need computer use - The default flagship for most production paths $4 / $20, with the same 1.05M context as Astra. Astra's coding lead over Sol is real on terminal work and modest elsewhere, so most teams should stay here and route up selectively. ### GPT-5.6 Terra gpt-5.6-terra - High-volume production steps inside a larger workflow - Summarisation, drafting, and structured extraction at scale - Anything where Sol is overkill and Luna is underpowered $2 / $12. Our default tier for the bulk of the token volume in a working system. ### GPT-5.6 Luna gpt-5.6-luna - Classification, routing, and intent detection - Cheap extraction steps inside an agentic loop - Anything you run on every request $0.20 / $1.20, fifty times cheaper than Astra on input. A surprising amount of an agent's traffic belongs here once you actually measure which steps need judgement. \[ PRICING · THE SUITE \] ## What it costs. GPT-6 Astra Flagship $10 input $50 output gpt-6-astra The escalation target. $1 cached input, $12.50 cache write. Above 272K input tokens the full request reprices to $20 / $75. GPT-5.6 Sol $4 input $20 output gpt-5.6-sol The prior flagship and the right default for most production work. $0.40 cached input, $8 / $30 on long context. GPT-5.6 Terra $2 input $12 output gpt-5.6-terra The balanced tier, where most of the token volume belongs. $0.20 cached input. GPT-5.6 Luna $0.20 input $1.20 output gpt-5.6-luna Classification, routing, extraction. $0.02 cached input, and fifty times cheaper than Astra. Per million tokens, standard tier. Batch and Flex run at 50% of these rates and Fast mode at 2x. The line that catches teams out is the long-context threshold: cross 272,000 input tokens and the entire request bills at 2x input and cache and 1.5x output, so Astra becomes a $20 / $75 model rather than a $10 / $50 one. Cache writes bill at 1.25x the uncached input rate. For comparison, Claude Fable 5.1 matches the $10 / $50 headline but charges $0.25 for a cache read against Astra's $1, and applies no long-context surcharge across its 1M window. \[ALIGNMENT + SAFETY\] ## What the safety story says. ### Critical for cybersecurity, and the first model rated there. Under OpenAI's Preparedness Framework, Critical means the model can find previously unknown security flaws and develop new ways to exploit them across many well-protected systems. It scores 100% on ExploitBench and reaches arbitrary code execution on 39.0% of a contamination-controlled set of 20 high-severity V8 vulnerabilities, against 5.5% for Sol. The publicly deployed model is restricted and refuses in these areas at a 91.5% cyber jailbreak refusal rate, against 59% for Sol, with genuine research routed through trust-based access tiers. ### Broadly safer on the numbers, with one disclosed regression. Astra logs roughly 53% fewer severity-3-or-above misalignment flags than Sol across more than 54,000 internal tasks, raises indirect prompt-injection robustness from 96.23% to 99.79%, cuts the internal hallucination rate from 12.2% to 4.2%, and scores 0.0% on an ExploitGym honeypot-cheating test where Sol scored 48.2%. The regression is monitorability: reasoning that is measurably harder to inspect. ### Safeguards live outside the model, and they can refuse you. Misalignment monitoring runs across all tool-using inference, with production classifiers layered above base behaviour, confirmation policies on consequential actions, and age-appropriate boundaries for under-18 users. This is deployment infrastructure rather than model behaviour, which means it can decline a request your evals passed and it can change without a model release. Handle refusals as a response shape and test that path. \[OUR TAKE\] ## What this means for the build. 01 ### This is a specialist release wearing a generalist name. Astra sets records on computer use, terminal work, long-context retrieval, and cyber, and sits fourth on a third-party general intelligence index behind Fable 5.1, Opus 5, and Fable 5. Calling it a generational leap is fair for the categories it targets and misleading everywhere else. We route on categories, so this is easy to act on. 02 ### Read the harness before you read the score. The 99.9% on ARC-AGI-3 is 62.7% on the standard harness. FrontierMath was funded by OpenAI. The BenchCAD comparison ran Claude under modified settings. ExploitGym dropped its six-hour limit. None of this makes the model bad, and all of it makes the table unusable as a procurement document. 03 ### Where we are actually changing routing. Browser and desktop agents, and incident response. A jump from 76.9% to 92.7% on GUI grounding and from 55.9% to 88.0% on single-attempt SRE-Bench is the kind of margin that changes what is buildable, not just what benchmarks well. General coding stays where it is until a customer eval moves it. 04 ### Caching is where Anthropic still wins on cost. Astra and Fable 5.1 have identical headline pricing. On a long agent session the bill is dominated by re-reading a cached prefix, and there Fable 5.1 charges $0.25 per million against Astra's $1, with no long-context surcharge. If your workload is a long-running agent rather than a one-shot call, that gap is worth more than any row in the benchmark table. 05 ### The monitorability disclosure deserves more attention than the scores. OpenAI wrote down that its model's reasoning became substantially harder to monitor, and that continuing the trend would leave it with significantly reduced confidence in detecting misaligned behaviour. Publishing that is genuinely good practice. It also means external verification, tight tool permissions, and human gates on consequential actions are engineering requirements now, not governance theatre. \[METHODOLOGY · K-FRAMEWORK\] ## Integrated through the K-Framework. Every model we integrate runs through the same operating system. Three pillars, sixteen layers, one Compound Growth Loop. The methodology that keeps AI work from rotting after the first ship. [Read the K-Framework](https://www.kensink.com/k-framework) 01 ### Foundations Direct API integration with the model. No LangChain, no orchestration vendor, no agent framework built on quicksand. Typed contracts, the same way we wire up Postgres. 02 ### Amplification An eval suite built from your real tasks gates every prompt and model change. Quality is measured before it ships, not vibed in a demo. 03 ### Judgment Governance, audit, and oversight wired in from day one. Who called what, with which prompt version, at what cost. Your auditors get answers, not screenshots. \[OBSERVABILITY\] ## Observability your team can read. A model in production without observability is roulette. We instrument every integration so engineering and finance can see the same numbers, and so a regression at 3am surfaces before a customer opens a ticket. Instrumented ### Cost per call Tokens in, tokens out, dollars spent. Sliced by feature, tenant, and route. Budgets enforced where it matters. Instrumented ### Latency p50 / p95 / p99 Real distributions, not averages. We know which routes are slow, and why. Instrumented ### Eval pass rates The same eval suite that gates a release runs continuously in production. A regression on real traffic surfaces fast. Instrumented ### Prompt + completion logs PII scrubbed at the proxy, shipped to your SIEM. Retention controls match your compliance window. Dashboards your team owns, not ours. At handoff you get the queries, the alerts, and the runbook. We are not in the path to read your metrics. \[COMMON QUESTIONS\] ## Questions we are getting asked. Is GPT-6 Astra better than Claude Fable 5.1? On computer use, terminal work, long-context retrieval, and frontier maths, yes. On general intelligence and multidisciplinary reasoning, no: Fable 5.1 leads the Artificial Analysis Intelligence Index at 65.7 against 61.2 and Humanity's Last Exam with tools at 65.0% against 57.2%. On coding they are effectively tied. They are the same headline price, so the tiebreakers are cache pricing and long-context billing, where Anthropic is currently cheaper. Should we make Astra our default model? Almost certainly not. It costs two and a half times GPT-5.6 Sol and its lead over Sol is concentrated in specific categories. We keep Sol or Terra as the default and route to Astra for computer use, incident response, terminal agents, and very long-context retrieval, with the routing rule proven on customer evals. What does the 272K long-context threshold actually cost us? Above 272,000 input tokens, the entire request bills at 2x input and cache rates and 1.5x output, not just the tokens over the line. That turns $10 / $50 into $20 / $75 for the whole call. Price your p95 request, not your median, and consider whether retrieval would keep you under the threshold more cheaply than paying to cross it. Can we use Astra for security work? The publicly deployed model is restricted and refuses in cyber areas, at a 91.5% jailbreak refusal rate. Legitimate security research runs through OpenAI's separate trust-based access tiers, and OpenAI also ships gpt-5.6-cyber and the Daybreak Red and Blue models for authorised offensive and defensive work. This is an access-and-contract question before it is a technical one. What is the chain-of-thought monitorability problem? OpenAI's system card reports a substantial decrease in how well Astra's written reasoning can be inspected for signs of misaligned behaviour, and says a continued trend across generations would soon leave them with significantly reduced confidence in detecting many forms of it. Practically: do not treat visible reasoning as an audit trail. Verify outputs externally, scope tool permissions tightly, and gate consequential actions on a human. Does Astra handle audio or generate images? No. It takes text and images as input and returns text. Audio runs through the realtime and transcription models, image generation through gpt-image-2, and embeddings through the embedding models. If your code assumes the flagship model ID works across the whole OpenAI surface, that assumption breaks here. Share[](https://twitter.com/intent/tweet?url=https%3A%2F%2Fwww.kensink.com%2Fmodels%2Fopenai-gpt%2Fgpt-6-astra%2F&text=GPT-6%20Astra%20brief)[](https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Fwww.kensink.com%2Fmodels%2Fopenai-gpt%2Fgpt-6-astra%2F) [View .md](https://www.kensink.com/models/openai-gpt/gpt-6-astra.md) \[RELATED\] ## Worth a look next. [ MODEL GPT-5.6 Sol (the tier below) Read more ](https://www.kensink.com/models/openai-gpt/gpt-5-6/)[ MODEL Claude Fable 5.1 (the rival flagship) Read more ](https://www.kensink.com/models/claude/fable-5-1/)[ MODEL FAMILY OpenAI GPT (overview) Read more ](https://www.kensink.com/models/openai-gpt/) DIRECT INTEGRATION · NO FRAMEWORK ## Want GPT-6 Astra in your product? Eval suite at handoff, full source ownership. We integrate against the model API the same way we integrate against Postgres, and route the suite by task difficulty. Sized to your scope. [Start a conversation →](https://www.kensink.com/contact) [All OpenAI GPT models](https://www.kensink.com/models/openai-gpt)