Cost Estimation

Chat bills per token — /estimate runs the exact same pricing engine and token estimator that bills real traffic, so it can never disagree with what you’re actually charged.

POST /api/v1/estimate

{
  "model": "claude-sonnet-4.6",
  "messages": [{"role": "user", "content": "Summarize this document..."}],
  "count": 1000
}

Response

{
  "model": "claude-sonnet-4.6",
  "model_name": "Claude Sonnet 4.6",
  "provider": "Anthropic",
  "billing": "metered",
  "rates": {
    "input_per_mtok": 3.0,
    "output_per_mtok": 15.0,
    "cached_input_per_mtok": 0.3
  },
  "estimated_tokens": { "prompt": 62, "completion": 64 },
  "estimated_cents": 0.11,
  "estimated_cost": "$0.0011",
  "estimated_max_cents": 3.15,
  "estimated_max": "$0.0315",
  "for_count": 1000,
  "total_estimated_cents": 110.0,
  "total_estimated": "$1.1000",
  "cheaper_alternatives": [
    {"id": "gpt-5", "name": "GPT-5", "estimated_cents": 0.05, "savings_percent": 55, "capabilities": ["text", "code"]}
  ],
  "monthly_estimate": {"at_100": "$0.11", "at_1000": "$1.10", "at_10000": "$11.00", "at_100000": "$110.00"},
  "note": "Billing is per token ($/MTok input + output). Requests preauthorize estimated_max and settle to actual usage; estimates here use the same engine and estimator that bill real traffic."
}

estimated_cents/estimated_cost is a realistic mid-point for this exact payload — pass messages for a real token count, or omit them and it falls back to a 5K-token reference input. estimated_max/estimated_max_cents is the ceiling: what a real request with these messages and max_tokens would preauthorize against your balance before settling down to actual usage. Nothing is ever charged more than what you actually used — estimated_max exists so you can budget for the worst case, not because it’s what you’ll typically pay. No API key needed — and the endpoint doesn’t read one either. rates are the live $/MTok numbers this model bills at; pass Authorization if you like for consistency with your other requests, but it has no effect here.

Full response shape

Also included: pricing_version, tier, for_count, total_estimated_cents/total_estimated (estimated_cents × count), and — when you pass models — a model_comparison array. Every cents field is a float; round for display, don’t truncate.

Parameters

Parameter	Default	Description
`model`	required	Any concrete model ID (not `auto*`) — `ensemble`/`ensemble-quality` are supported and sum their member calls.
`messages`	—	Improves the token estimate — the real byte-aware estimator this API bills with. Omit and it falls back to a 5K-token reference input.
`max_tokens`	`2048`	Bounds `estimated_max` — the output ceiling a real request would hold against your balance.
`count`	`1`	Requests to price (1–10,000).
`models`	—	Also return a side-by-side `model_comparison` across these IDs.

Compare prices across models

{
  "model": "claude-sonnet-4.6",
  "messages": [{"role": "user", "content": "Summarize this document..."}],
  "count": 50000,
  "models": ["gpt-5-mini", "deepseek-v3", "gpt-5", "claude-sonnet-4.6", "claude-opus-4.6"]
}

{
  "model_comparison": [
    {"model": "gpt-5-mini",        "estimated_cents": 0.03, "total_estimated_cents": 1500},
    {"model": "deepseek-v3",       "estimated_cents": 0.04, "total_estimated_cents": 2000},
    {"model": "gpt-5",             "estimated_cents": 0.05, "total_estimated_cents": 2500},
    {"model": "claude-sonnet-4.6", "estimated_cents": 0.11, "total_estimated_cents": 5500},
    {"model": "claude-opus-4.6",   "estimated_cents": 0.18, "total_estimated_cents": 9000}
  ]
}

Numbers above are illustrative for this example payload — real figures scale with your actual token counts. Always read live numbers from the endpoint.

Guard a batch before running it

import requests

def estimated_max_cents(model: str, messages: list, max_tokens: int = 2048) -> float:
    r = requests.post("https://www.ninjachat.ai/api/v1/estimate",
        json={"model": model, "messages": messages, "max_tokens": max_tokens})
    return r.json()["estimated_max_cents"]

cost = estimated_max_cents("claude-sonnet-4.6", my_messages) * 10_000  # 10k-item batch
if cost > 5000:  # $50 budget
    print(f"Too expensive (worst case ${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.

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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