Quick Answer
The circulating number is ~10 trillion parameters with a Mixture-of-Experts (MoE) architecture — but this is rumor, not an official figure. OpenAI hasn't confirmed Astra's size. What's more reliable than the exact number is the direction: a larger MoE model than GPT-5.6, designed for deeper multi-agent reasoning, at a cost that reflects its scale.
What "MoE" Actually Means
A Mixture-of-Experts model doesn't run every parameter on every token. It routes each input to a subset of "experts" (specialized sub-networks):
- Total parameters can be huge (10T) — the full pool of experts.
- Active parameters per token are much smaller — only the experts chosen for that input fire.
So "10T parameters" ≠ "10T active per token." MoE gets you big-model capacity without big-model per-token cost — which is why frontier models keep scaling this way.
Why the 10T Figure Is Uncertain
- OpenAI hasn't published architecture details for Astra.
- The number comes from leaks/industry reporting, not a model card.
- Even if directionally right, "10T" could be total vs. active, or off by a factor.
Treat it as a signal of scale, not a data-sheet spec. The confirmed facts remain: native multi-agent training and the 10 math proofs.
What Scale Implies (More Useful Than the Number)
- Capability — more total experts means more specialized capacity for the model to route into, which plausibly underpins the multi-agent and deep-reasoning claims.
- Cost — even with MoE's active-parameter efficiency, a 10T-scale flagship is expensive to serve; the $2,000-per-problem figure is consistent with that.
- Latency — bigger models plus multi-agent coordination mean longer reasoning chains; plan for minute-scale timeouts and streaming.
The Practical Takeaway
Don't obsess over the exact parameter count — it doesn't change how you integrate. What matters:
- Astra is bigger and more expensive, so it's a "critical reasoning" resource, not a daily driver.
- It's OpenAI-compatible, so integration is a
base_url+ model-name change. - Route by task: Astra for the hard 20%, cheap tiers for the rest.
from openai import OpenAI
client = OpenAI(api_key="sk-teamo-xxxxxx", base_url="https://api.teamorouter.com/v1")
# model="gpt-6" on launch; route everything else to cheaper tiers
FAQ
Q: Is 10 trillion parameters confirmed? No. It's rumor from industry reporting. OpenAI hasn't published Astra's architecture. Treat it as directional, not official.
Q: Does more parameters mean better? Not linearly. MoE active-parameter routing matters more than the total count, and capability is per-dimension — a 10T model can still underperform on tasks it wasn't optimized for.
Q: Will a 10T model be too slow to use interactively? For simple tasks, possibly overkill — but MoE keeps per-token cost manageable. The real latency concern is multi-agent coordination, not raw size; use streaming and longer timeouts.
Summary
The ~10T MoE figure is rumor, but the direction is real: Astra is bigger, more expensive, and multi-agent. Don't chase the exact number — prep the access layer and route by task. Sign up for TeamoRouter to add Astra as your top reasoning tier when it ships.
Get Started
TeamoRouter — one key, ready for Astra at whatever size it ships.