Models think through problems step by step. Higher accuracy on math, logic, and code.
Reasoning models can call functions, browse, and execute code. Multi-step agent loops out of the box.
Reason over entire codebases, papers, or books. Citations to source material.
Models check their own work. Higher accuracy on high-stakes outputs.
Drop-in replacement. Reasoning is just another model parameter.
Watch the model think in real time. Inspect intermediate steps for debugging.
Token-efficient, with volume discounts and burn incentives.
P50 1.8s, 32K context
P50 6s, 128K context
P50 24s, 256K context
Plus underlying token cost
Pricing is illustrative. Final rates are governed by on-chain parameters and may vary based on network state.
Tier-based access. Higher stakes unlock better economics and more capacity.
| Tier | Required stake | Access |
|---|---|---|
| Builder | 50M QUBIC | All reasoning models, best-effort |
| Startup | 150M QUBIC | Reserved throughput, deep + max |
| Business | 500M QUBIC | Custom reasoning models |
| Enterprise | Custom | On-prem, private models |
Drop-in compatible with the OpenAI SDK.
from aigarth import Aigarth
client = Aigarth(api_key="sk-aigarth-...")
response = client.chat.create(
model="aigarth-reason-1-deep",
messages=[
{"role": "user", "content": "A train leaves Boston at 9am at 60mph. Another leaves NYC at 10am at 80mph. When do they meet?"},
],
)
# Inspect the reasoning
for step in response.choices[0].message.reasoning_steps:
print(step)
print("Answer:", response.choices[0].message.content)Everything in the standard tier, plus the things enterprises need.