Cost Estimation

POST /api/v1/estimate

{
  "model": "claude-sonnet-4.6",
  "count": 1000
}

Response

{
  "model": "claude-sonnet-4.6",
  "cost_per_request": "$0.0150",
  "for_count": 1000,
  "total_cost": "$15.0000",
  "cheaper_alternatives": [
    {"id": "gpt-5",       "cost_cents": 0.6, "savings_percent": 60},
    {"id": "deepseek-v3", "cost_cents": 0.3, "savings_percent": 80}
  ],
  "monthly_estimate": {"at_100": "$1.50", "at_1000": "$15.00", "at_10000": "$150.00"}
}

No API key needed — and the endpoint doesn’t read one either. Estimates are pure catalog math (model cost × count), not balance-aware; pass Authorization if you like for consistency with your other requests, but it has no effect here.

Full response shape

Also included: model_name, provider, tier, cost_per_request_cents, estimated_tokens (approximate, from your messages if provided), full capabilities on each alternative, ensemble_pricing, and a note reminding you billing is per-request, not per-token.

Parameters

Parameter	Default	Description
`model`	required	Any concrete model ID (not `auto*`).
`count`	`1`	Requests to price (1–1,000,000).
`models`	—	Also return a side-by-side `model_comparison` across these IDs.
`messages`	—	Improves the token estimate; doesn’t change the price.

Compare prices across models

{
  "model": "claude-sonnet-4.6",
  "count": 50000,
  "models": ["gpt-5-mini", "deepseek-v3", "gpt-5", "claude-sonnet-4.6", "claude-opus-4.6"]
}

{
  "model_comparison": [
    {"model": "gpt-5-mini",        "cost_cents": 0.3, "total_cost_cents": 15000},
    {"model": "deepseek-v3",       "cost_cents": 0.3, "total_cost_cents": 15000},
    {"model": "gpt-5",             "cost_cents": 0.6, "total_cost_cents": 30000},
    {"model": "claude-sonnet-4.6", "cost_cents": 1.5, "total_cost_cents": 75000},
    {"model": "claude-opus-4.6",   "cost_cents": 3.0, "total_cost_cents": 150000}
  ]
}

Guard a batch before running it

import requests

def estimated_cents(model: str, count: int) -> float:
    r = requests.post("https://www.ninjachat.ai/api/v1/estimate",
        json={"model": model, "count": count})
    return r.json()["total_cost_cents"]

cost = estimated_cents("claude-sonnet-4.6", 10000)
if cost > 5000:  # $50 budget
    print(f"Too expensive (${cost/100:.2f}) — try gpt-5 or deepseek-v3")
else:
    pass  # run the batch

Agents get the same guarantee over MCP — estimate_cost is one of the free MCP tools.

Get Started

Chat

Superpowers

Media & Search

Reference

Parameters

Compare prices across models

Guard a batch before running it

​Parameters

​Compare prices across models

​Guard a batch before running it

Parameters

Compare prices across models

Guard a batch before running it