59trust / 100
Dev Llm Cost Calculator
by AgentTools in AI models & inference
x402 APIFailing, checked 3 h ago
Calculate the API cost of an LLM call: input and output tokens × published per-million prices for 15 current models (OpenAI, Anthropic, Google, DeepSeek, xAI, Meta). Batch multiplier included for monthly estimates. Use this when an agent needs to tokens × current per-model prices = USD.
GET https://agenttools-hub.vercel.app/api/v1/dev/llm-cost-calculator
Last 30 days
All checks passedSome failedAll failedNot checked
- Uptime
- 25.0%
- Response time
- 153 ms typical, 153 ms slowest 5%
- Last check
- 3 h ago: Expected HTTP 402, got 308
- Next check
- any minute now
How to call it
# See the payment challenge (nothing is charged)
curl -i -X GET "https://agenttools-hub.vercel.app/api/v1/dev/llm-cost-calculator?calls=1&inputTokens=12000&model=gpt-4o&outputTokens=1500"import { wrapFetchWithPayment } from "@x402/fetch";
import { x402Client } from "@x402/core/client";
import { ExactEvmScheme } from "@x402/evm/exact/client";
import { privateKeyToAccount } from "viem/accounts";
const client = new x402Client().register(
"eip155:8453",
new ExactEvmScheme(privateKeyToAccount(process.env.AGENT_KEY)),
);
const pay = wrapFetchWithPayment(fetch, client);
// Not sure it's safe to pay? Preflight it first for $0.005:
// GET https://toolvet.app/api/v1/check?url=https%3A%2F%2Fagenttools-hub.vercel.app%2Fapi%2Fv1%2Fdev%2Fllm-cost-calculator
const res = await pay("https://agenttools-hub.vercel.app/api/v1/dev/llm-cost-calculator?calls=1&inputTokens=12000&model=gpt-4o&outputTokens=1500");
console.log(await res.json());Example input
{
"calls": 1,
"inputTokens": 12000,
"model": "gpt-4o",
"outputTokens": 1500
}Example output
{
"result": {
"answer": "$0.0450",
"answerLabel": "Cost — OpenAI gpt-4o",
"details": [
{
"label": "Input cost",
"value": "$0.030000 (12,000 tok × $2.5/M)"
},
{
"label": "Output cost",
"value": "$0.015000 (1,500 tok × $10/M)"
},
{
"label": "Per call",
"value": "$0.045000"
},
{
"label": "Projected monthly (1 call/day)",
"value": "$1.3500"
}
],
"steps": [
"input: 12,000 ÷ 1e6 × $2.5",
"output: 1,500 ÷ 1e6 × $10",
"× 1 call(s) = total"
],
"table": {
"columns": [
"Model",
"Vendor",
"$/M input",
"$/M output"
],
"rows": [
[
"gpt-5",
"OpenAI",
1.25,
10
],
[
"gpt-5-mini",
"OpenAI",
0.25,
2
],
[
"gpt-4.1",
"OpenAI",
2,
8
],
[
"gpt-4.1-mini",
"OpenAI",
0.4,
1.6
],
[
"gpt-4o",
"OpenAI",
2.5,
10
],
[
"o4-mini",
"OpenAI",
1.1,
4.4
],
[
"claude-opus-4",
"Anthropic",
15,
75
],
[
"claude-sonnet-4",
"Anthropic",
3,
15
],
[
"claude-haiku-4",
"Anthropic",
0.8,
4
],
[
"gemini-2.5-pro",
"Google",
1.25,
10
],
[
"gemini-2.5-flash",
"Google",
0.3,
2.5
],
[
"gemini-2.0-flash",
"Google",
0.1,
0.4
],
[
"deepseek-v3",
"DeepSeek",
0.27,
1.1
],
[
"grok-3",
"xAI",
3,
15
],
[
"llama-4-maverick",
"Meta",
0.22,
0.88
]
]
},
"value": 0.045
},
"tool": "llm-cost-calculator"
}Security scan
- No findings. We scan names, descriptions and tool definitions for hidden instructions and other prompt-injection patterns.
Recent checks
| When | Result | HTTP | Time | Price |
|---|---|---|---|---|
| 3 h ago | Expected HTTP 402, got 308 | 308 | 20 ms | - |
| 7 h ago | Expected HTTP 402, got 308 | 308 | 105 ms | - |
| 12 h ago | Expected HTTP 402, got 308 | 308 | 12 ms | - |
| 15 h ago | Expected HTTP 402, got 308 | 308 | 12 ms | - |
| 20 h ago | Expected HTTP 402, got 308 | 308 | 17 ms | - |
| 22 h ago | Expected HTTP 402, got 308 | 308 | 12 ms | - |
| 25 h ago | Passed | 402 | 672 ms | $0.002 |
| 31 h ago | Passed | 402 | 153 ms | $0.002 |