GPT-6 Astra is now on Token Station

GPT-6 Astra is now available on Token Station as openai/gpt-6-astra, through the same OpenAI-compatible endpoint you already use for the rest of the GPT-5.6 family.

OpenAI built Astra around agentic work: long coding sessions, computer and browser use, and terminal-heavy operations, rather than a single-turn quality bump. That shows up less in any one benchmark and more in how far the model gets through a multi-step task before it needs a human to step back in.

What’s actually new

Astra’s headline gains are concentrated in long-horizon and agentic benchmarks rather than general knowledge:

• FrontierMath Tier 4: 97.6%, the hardest published math benchmark, and ahead of Claude Fable 5.1’s 87.8% on the same test.
• ExploitBench: 100%, a benchmark for defensive cybersecurity work (finding and patching vulnerabilities, not writing exploits).
• OSWorld 2.0 (computer and browser use): 72.6%, completed in roughly 47% less time per task than GPT-5.6 Sol.
• SRE-Bench (incident response and systems tasks): 88.0% solved on the first attempt and 99.2% within four attempts, up from 55.9% and 68.7% for GPT-5.6 Sol.
• Terminal-Bench 4.0: 57.7%, a benchmark built around long, messy terminal sessions.

The pattern across all five: Astra isn’t just answering better. It’s staying on task longer without drifting from the original instructions, which is the actual bottleneck in agentic coding and computer-use workflows.

Astra is also the first OpenAI model to cross the “Critical” threshold on cybersecurity capability, so its most advanced offensive-security behavior ships gated behind OpenAI’s Daybreak access program. Nothing about routing it through Token Station changes that gating: it’s an OpenAI-side access control, not a Token Station one.

Specs

Try it

curl https://models.bytefuture.ai/v1/chat/completions \
  -H "Authorization: Bearer TOKEN_STATION_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "openai/gpt-6-astra",
    "messages": [
      {"role": "user", "content": "Plan a safe refactor for a pricing module, list the tests to run, and flag anything that touches billing."}
    ]
  }'

Swap openai/gpt-6-astra for openai/gpt-5.6-sol in the same request to compare them on your own workload without changing anything else about your integration.

Pricing

Astra’s rates hold up to 272K input tokens. Past that, OpenAI bills a long-context tier for the entire request, not just the tokens over the line: 2x the input and cached-input rate, 1.5x the output rate. A 273K-token prompt costs roughly double a 271K-token one on the input side. Token Station passes these rates through directly, with no markup, so long-context agent sessions are worth watching if they regularly cross that threshold.

Where Astra earns its price, and where it doesn’t

Astra costs twice what GPT-5.6 Sol does per token. That premium is easiest to justify on the workloads its benchmarks target directly:

• Long agentic coding sessions in Codex-style workflows, where the gain isn’t one-shot code quality so much as needing fewer correction rounds to reach something production-ready.
• Computer-use and browser automation, where OSWorld’s near-50% time reduction compounds across a long session.
• Terminal-heavy operations work: log triage, systems debugging, the kind of task that used to need a human watching every step.

For a single Q&A call, a classification task, or anything that doesn’t chain many steps together, the efficiency gains that justify Astra’s price don’t really apply, and a cheaper route in the GPT-5.6 family or Claude Sonnet 5 will do the job for less.

Get started

Sign up at models.bytefuture.ai: $1 in free credit, no card required, with up to $50 in bonus credit on your first top-up. Export your key and point your existing OpenAI-compatible integration at openai/gpt-6-astra.

Try Token Station