ModelsArchitectures & capability
OpenAI's GPT-6 Astra charges 2.5× GPT-5.6 Sol's per-token rates but uses fewer tokens per task
GPT-6 Astra charges 2.5 times GPT-5.6 Sol's per-token rates, yet its lower token use can make tasks cheaper. The larger budget risk is a 272K-token threshold that reprices an entire request, while caching keeps the same break-even as Sol.
From the r/AISEOInsider (via last30days) article of October 19, 2026 and cited sources: Astra costs $10 per million input tokens and $50 per million output tokens, against Sol's $4 and $20. [2] [4] [11]
SOURCES[2] Artificial Analysis: Benchmarking GPT-6 Astra artificialanalysis.ai[4] Constellation Research: OpenAI launches GPT-6 Astra constellationr.com[11] MindStudio: GPT-6 Astra Pricing and Access mindstudio.ai
AT Pulse research deskPublished · Updated · 5 min read · AI-assisted analysisAI-assisted, not human-edited. Methodology Corrections Who owns this site
- What happened
- Astra costs $10 per million input tokens and $50 per million output tokens, against Sol's $4 and $20. [2] [4] [11]
- Original source
- r/AISEOInsider (via last30days) article, independent perspective
- Brief by
- AT Pulse research desk AI-assisted; publisher and contact on the desk page
- When
- October 19, 2026 article published
- Covered
- Oct 19 edition
- Evidence
- 12 sources across 10 sites
- 1GPT-6 Astra Burns Too Many Tokens? Try THISreddit.com
- 2Artificial Analysis: Benchmarking GPT-6 Astraartificialanalysis.ai
- 3TrueFoundry: GPT-6 Astra Pricing, Cache Break-Eventruefoundry.com
- 4Constellation Research: OpenAI launches GPT-6 Astraconstellationr.com
- 5Wikipedia: GPT-6en.wikipedia.org
- 680aj: OpenAI GPT-6 Astra card80aj.com
Reach: not measured yet
Astra costs $10 per million input tokens and $50 per million output tokens, against Sol's $4 and $20. Per task, it can still come out cheaper. Artificial Analysis scored Astra 53 on its Intelligence Index, tying Claude Fable 5.1 at about 40% of Fable's cost per task. Astra used roughly 27K output tokens per task to Fable's 78K. [2] [4] [11]
Once a prompt exceeds 272K input tokens, the higher rate applies to the whole request, so going slightly over the line can roughly double input cost. Caching still pays off: a cache write costs 1.25× the base input rate and a cached read 0.10×, so the break-even point matches Sol's. Caching only loses money when more than about 78% of requests on a shared prefix are writes. [3]
A higher effort setting does not guarantee a better result. In ML6's migration of a Kubeflow pipeline spanning more than 300 files, Astra at low effort succeeded and was the cheapest successful run, while Sol at low effort failed. Artificial Analysis measured Astra at $1.54, $1.73 and $3.26 per task at medium, high and max effort. [7] [10]
Measurements show Astra can finish tasks with far fewer tokens than comparable models, which offsets its higher rates.
Read the full assessment
For teams, the takeaway is to budget by tokens per task measured on their own workloads, keep prompts under 272K tokens, and start with lower effort settings.
Status: research is finished and the dossier is written below.
Executive brief
GPT-6 Astra costs 2.5× more per token than GPT-5.6 Sol, but it can still be cheaper per task because it uses fewer tokens. Artificial Analysis found that Astra matched Claude Fable 5.1's Intelligence Index score at about 40% of the cost per task (Artificial Analysis). The bigger cost risk is a pricing cliff: once a prompt goes over 272K input tokens, the higher rate applies to the whole request (TrueFoundry). The Reddit post that prompted this report is commentary, and nothing in it independently corroborates these points.
What changed and event timeline
Astra launches to Daybreak customers
OpenAI released Astra first to selected customers and said Plus, Pro, Business, Enterprise and API access would follow. It priced the model at $10 per million input tokens and $50 per million output tokens ().
Public release
OpenAI's system card rates it "Critical" for dual-use cybersecurity capability (;).
Independent index results
Artificial Analysis scored Astra 53 on its Intelligence Index. That ties Claude Fable 5.1 and is 6 points above GPT-5.6 Sol. Astra used about 27K output tokens per task, against 78K for Fable ().
Practitioner migration test
ML6 had the models migrate a Kubeflow pipeline spanning more than 300 files. Astra at low effort succeeded and was the cheapest successful run; Sol at low effort failed ().
Family expands
GPT-6 Sol and Luna shipped on Sep 22. GPT-6 Instant, which shows answers in interactive widgets ("Intelligent UI"), followed in October ().
Cache economics analysis
TrueFoundry showed that Astra's caching break-even point is the same as Sol's. It also flagged the 272K-token billing cliff ().
Capabilities and access
- Model: GPT-6 Astra. The OpenRouter ID is
openai/gpt-6-astra, and it is not available through OpenRouter's EU in-region routing (OpenRouter). - Context and output: about 1.05M tokens of context and up to 128K output tokens (LLM Stats).
- Pricing: $10 input, $1 cached input, $12.50 cache write and $50 output, per million tokens.
Read the full section
- Model: GPT-6 Astra. The OpenRouter ID is
openai/gpt-6-astra, and it is not available through OpenRouter's EU in-region routing (OpenRouter). - Context and output: about 1.05M tokens of context and up to 128K output tokens (LLM Stats).
- Pricing: $10 input, $1 cached input, $12.50 cache write and $50 output, per million tokens. Batch and Flex cost half; Fast mode costs double (TrueFoundry).
- Effort levels: medium, high and max. Artificial Analysis measured $1.54, $1.73 and $3.26 per task respectively (Artificial Analysis).
- Strengths: computer use, long-context retrieval, terminal agents and cybersecurity (MindStudio).
Technical analysis for researchers and developers
- Architecture: OpenAI has not documented it.
- Measuring cost: the useful number is tokens per task, not price per token.
- Caching math: cache writes cost 1.25× the uncached input rate and cached reads cost 0.10×.
Read the full section
- Architecture: OpenAI has not documented it. Anonymous insider reports say Astra uses "recurrent depth" (looped transformers), which may hide part of its chain of thought (Wikipedia: GPT-6).
- Measuring cost: the useful number is tokens per task, not price per token. Astra at low effort beat Sol at high effort on ML6's migration (ML6).
- Caching math: cache writes cost 1.25× the uncached input rate and cached reads cost 0.10×. The token price cancels out of the break-even calculation, so caching pays off unless more than about 78.3% of requests against a shared prefix are writes (TrueFoundry).
- Harness effects: ARC-AGI-3 results depend on the test harness used. Results from different setups should not be compared directly (MindStudio).
Claims and evidence
- 68% fewer tokens than Sol in Codex (3.3M vs 10.2M per task, scoring 62 vs 55). This is OpenAI's own figure, repeated by ML6.
- Matches Fable 5.1 at ~40% of the cost per task: independent (Artificial Analysis).
- 99.9% on ARC-AGI-3: OpenAI's figure, run on its own adapter. A provider-neutral run reportedly scored 62.7% (MindStudio).
Read the full section
- 68% fewer tokens than Sol in Codex (3.3M vs 10.2M per task, scoring 62 vs 55). This is OpenAI's own figure, repeated by ML6. TrueFoundry warns these estimates "will not generalise" to other workloads (TrueFoundry).
- Matches Fable 5.1 at ~40% of the cost per task: independent (Artificial Analysis).
- 99.9% on ARC-AGI-3: OpenAI's figure, run on its own adapter. A provider-neutral run reportedly scored 62.7% (MindStudio).
- Finds zero-day vulnerabilities and builds working exploit chains: from OpenAI's system card, as summarized by 80aj.
Context and prior work
GPT-5.6 Sol cost $4/$20 per million tokens. Astra's 2.5× increase is offset by using fewer tokens (MindStudio). OpenAI says Astra was its largest training run so far, using more than 100,000 GPUs at the Stargate site in Texas (Constellation Research). Analysts describe the launch as OpenAI trying to regain momentum after a Hugging Face security incident (Constellation Research).
Limitations, safety and contested findings
- Scores conflict: Artificial Analysis reports an index tie at 53.
- Weaker on GDPval-AA v2: Astra lost about 45 Elo points there, attributed to it taking fewer interaction turns (Artificial Analysis).
- Safety concerns: reasoning may be harder to monitor, and the model has Critical-level cyber capability (Wikipedia: GPT-6).
Read the full section
- Scores conflict: Artificial Analysis reports an index tie at 53. MindStudio reports Astra at 61 and Fable at 66 on the same index, which suggests different index versions or effort settings (Artificial Analysis; MindStudio).
- Weaker on GDPval-AA v2: Astra lost about 45 Elo points there, attributed to it taking fewer interaction turns (Artificial Analysis).
- Safety concerns: reasoning may be harder to monitor, and the model has Critical-level cyber capability (Wikipedia: GPT-6).
Business and practitioner implications
- Start low: use low or medium effort by default and raise it only for costly-to-fail work (ML6).
- Watch the cliff: keep prompts under 272K tokens. Going 0.7% over the line can roughly double input cost (TrueFoundry).
- Keep caching: it saves the same share of cost as on Sol, and the absolute savings are larger at Astra's prices.
Read the full section
- Start low: use low or medium effort by default and raise it only for costly-to-fail work (ML6).
- Watch the cliff: keep prompts under 272K tokens. Going 0.7% over the line can roughly double input cost (TrueFoundry).
- Keep caching: it saves the same share of cost as on Sol, and the absolute savings are larger at Astra's prices.
- Test on your own workloads: budget by measured tokens per task. OpenAI's per-task estimates may not hold for other traffic.
The source trail.
Sources (12)
GPT-6 Astra Burns Too Many Tokens? Try THIS
Feed excerpt only.
reddit.com