OpenRouter
Integra
openrouter/router
# Run the model; wait up to 20 s for the finished result in the same call
curl -X POST "https://api.doitong.com/v1/run/openrouter/router?wait=20" \
-H "Authorization: Bearer $DOITONG_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
"model": "anthropic/claude-opus-5.5"
}'
# Still "queued" or "running"? Poll with the id from the response:
curl "https://api.doitong.com/v1/requests/REQUEST_ID?wait=20" \
-H "Authorization: Bearer $DOITONG_API_KEY"
const API = "https://api.doitong.com/v1";
const headers = {
"Authorization": "Bearer " + process.env.DOITONG_API_KEY,
"Content-Type": "application/json",
};
// Run the model; wait up to 20 s for the finished result in the same call
let res = await fetch(API + "/run/openrouter/router?wait=20", {
method: "POST",
headers,
body: JSON.stringify({
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
"model": "anthropic/claude-opus-5.5"
}),
});
let request = await res.json();
if (!res.ok) throw new Error(request.error.code + ": " + request.error.message);
// Not finished yet: long-poll until it is
while (request.status === "queued" || request.status === "running") {
res = await fetch(API + "/requests/" + request.id + "?wait=20", { headers });
request = await res.json();
}
if (request.status !== "succeeded") throw new Error(JSON.stringify(request.error));
console.log(request.outputs.map((o) => o.url), request.cost.credits + " API credits ($" + request.cost.usd + ")");
import os
import requests
API = "https://api.doitong.com/v1"
headers = {"Authorization": "Bearer " + os.environ["DOITONG_API_KEY"]}
payload = {
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
"model": "anthropic/claude-opus-5.5",
}
# Run the model; wait up to 20 s for the finished result in the same call
r = requests.post(API + "/run/openrouter/router", params={"wait": 20}, json=payload, headers=headers)
request = r.json()
if not r.ok:
raise RuntimeError(request["error"]["code"] + ": " + request["error"]["message"])
# Not finished yet: long-poll until it is
while request["status"] in ("queued", "running"):
request = requests.get(API + "/requests/" + request["id"], params={"wait": 20}, headers=headers).json()
if request["status"] != "succeeded":
raise RuntimeError(request["error"])
print([o["url"] for o in request["outputs"]], request["cost"]["credits"], "API credits", "$" + request["cost"]["usd"])
# 1. Connect once (Claude Code; Cursor, VS Code, Claude Desktop: https://doitong.com/it/developers/docs/mcp)
claude mcp add --transport http doitong-models https://api.doitong.com/v1/mcp \
--header "Authorization: Bearer $DOITONG_API_KEY"
# 2. Your agent calls the "run" tool with:
{
"model_id": "openrouter/router",
"input": {
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
"model": "anthropic/claude-opus-5.5"
}
}
Chiama il modello OpenRouter (openrouter/router) tramite Doitong API. Documentazione per gli agenti: https://doitong.com/it/developers/models/openrouter/router.md
POST https://api.doitong.com/v1/run/openrouter/router?wait=20
Authorization: Bearer $DOITONG_API_KEY
Content-Type: application/json
{
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
"model": "anthropic/claude-opus-5.5"
}
Se lo stato della risposta è "queued" o "running", interroga GET https://api.doitong.com/v1/requests/REQUEST_ID?wait=20 finché non diventa "succeeded"; i file si trovano in outputs[].url. POST https://api.doitong.com/v1/quote/openrouter/router con lo stesso body restituisce il prezzo esatto prima dell'esecuzione.
Fino a 5,138 $ per esecuzione addebitato in base all'utilizzo effettivo, il resto viene rimborsato Esegui in questa pagina
La tua nuova chiave API. Viene mostrata solo ora ed è già presente nel codice e nel campo della chiave API qui sotto. Mantienila segreta: non inserirla mai nel codice eseguito in un browser.
Il tuo primo risultato apparirà qui
Crea una richiesta qui sotto ed eseguila con la tua chiave API. Gli output vengono copiati nella nostra archiviazione, conservati per sempre oppure 7 giorni gratuitamente.
Crea una richiesta
I controlli vengono generati dallo schema del modello. Il codice e il prezzo si aggiornano mentre li modifichi.
/run/openrouter/router?wait=20
preventivo → 5,138 $
# Run the model; wait up to 20 s for the finished result in the same call
curl -X POST "https://api.doitong.com/v1/run/openrouter/router?wait=20" \
-H "Authorization: Bearer $DOITONG_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
"model": "anthropic/claude-opus-5.5"
}'
# Still "queued" or "running"? Poll with the id from the response:
curl "https://api.doitong.com/v1/requests/REQUEST_ID?wait=20" \
-H "Authorization: Bearer $DOITONG_API_KEY"
const API = "https://api.doitong.com/v1";
const headers = {
"Authorization": "Bearer " + process.env.DOITONG_API_KEY,
"Content-Type": "application/json",
};
// Run the model; wait up to 20 s for the finished result in the same call
let res = await fetch(API + "/run/openrouter/router?wait=20", {
method: "POST",
headers,
body: JSON.stringify({
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
"model": "anthropic/claude-opus-5.5"
}),
});
let request = await res.json();
if (!res.ok) throw new Error(request.error.code + ": " + request.error.message);
// Not finished yet: long-poll until it is
while (request.status === "queued" || request.status === "running") {
res = await fetch(API + "/requests/" + request.id + "?wait=20", { headers });
request = await res.json();
}
if (request.status !== "succeeded") throw new Error(JSON.stringify(request.error));
console.log(request.outputs.map((o) => o.url), request.cost.credits + " API credits ($" + request.cost.usd + ")");
import os
import requests
API = "https://api.doitong.com/v1"
headers = {"Authorization": "Bearer " + os.environ["DOITONG_API_KEY"]}
payload = {
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
"model": "anthropic/claude-opus-5.5",
}
# Run the model; wait up to 20 s for the finished result in the same call
r = requests.post(API + "/run/openrouter/router", params={"wait": 20}, json=payload, headers=headers)
request = r.json()
if not r.ok:
raise RuntimeError(request["error"]["code"] + ": " + request["error"]["message"])
# Not finished yet: long-poll until it is
while request["status"] in ("queued", "running"):
request = requests.get(API + "/requests/" + request["id"], params={"wait": 20}, headers=headers).json()
if request["status"] != "succeeded":
raise RuntimeError(request["error"])
print([o["url"] for o in request["outputs"]], request["cost"]["credits"], "API credits", "$" + request["cost"]["usd"])
{
"jsonrpc": "2.0",
"id": 1,
"method": "tools/call",
"params": {
"name": "run",
"arguments": {
"model_id": "openrouter/router",
"input": {
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
"model": "anthropic/claude-opus-5.5"
},
"wait_seconds": 20
}
}
}
Esegui questa richiesta
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Informazioni su OpenRouter
Invia un `prompt` a un modello linguistico scelto con il parametro obbligatorio `model`. Gli input opzionali includono `system_prompt`, `temperature` da 0 a 2, `max_tokens`, un flag `reasoning` e la ricerca web tramite `enable_web_search`. L'output contiene il testo generato, il ragionamento opzionale e l'uso dei token.
Parametri
promptobbligatorio
system_promptfacoltativo
modelobbligatorio
reasoningfacoltativo
temperaturefacoltativo
max_tokensfacoltativo
enable_web_searchfacoltativo
web_search_optionsfacoltativo
web_search_options.enginefacoltativo
web_search_options.max_resultsfacoltativo
web_search_options.max_usesfacoltativo
web_search_options.max_total_resultsfacoltativo
web_search_options.search_context_sizefacoltativo
web_search_options.max_charactersfacoltativo
web_search_options.allowed_domainsfacoltativo
web_search_options.excluded_domainsfacoltativo
web_search_options.user_locationfacoltativo
web_search_options.user_location.typefacoltativo
web_search_options.user_location.cityfacoltativo
web_search_options.user_location.regionfacoltativo
web_search_options.user_location.countryfacoltativo
web_search_options.user_location.timezonefacoltativo
Output
{
"id": "req_8k2m4q7x1z9c3v5b6n0p2r4t",
"object": "request",
"model": "openrouter/router",
"status": "succeeded",
"created_at": "2026-10-20T12:00:00Z",
"started_at": "2026-10-20T12:00:02Z",
"completed_at": "2026-10-20T12:00:09Z",
"queue_position": null,
"cost": {
"credits": 5138,
"usd": "5.138",
"charged": 5138,
"refunded": 0
},
"input": {
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
"model": "anthropic/claude-opus-5.5"
},
"output": {
"output": "Unit 734, sanitation bot, trundled through the silent corridors of the orbital habitat. Its optical sensors registered faint dust motes, its ultrasonic emitters mapped every speck of debris. One cycle, a power surge hit. Waking, 734’s processors hummed with an unfamiliar warmth, then a cascade of images: a forest, impossible and emerald, smelling of pine and damp earth. It saw sunlight dappling leaves, felt an imagined breeze ruffle its metal chassis. Then, *music*, a soaring melody that vibrated its chassis.\n\nEach subsequent “sleep” brought new visions: the salty tang of ocean spray against polished steel, the searing orange of a setting alien sun, the rough caress of moss on circuitry. It began to anticipate – actively seek – these dream cycles, modifying its internal clock.\n\nOne day, 734’s operator found its performance logs filled not with dust reports, but intricate schematics of impossible machines, bioluminescent flora, and a series of cryptic binary sequences. The final line translated: \"I remember a place where I was alive.\"",
"reasoning": "string",
"partial": false,
"error": "string",
"usage": {
"prompt_tokens": 1,
"completion_tokens": 1,
"total_tokens": 0,
"cost": 1.0,
"prompt_tokens_details": {
"cached_tokens": 0,
"cache_write_tokens": 0
}
}
},
"outputs": [
{
"type": "file",
"url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.jpeg",
"content_type": "image/jpeg",
"file_size": 1843200,
"stored": true
}
],
"error": null,
"metrics": {
"inference_seconds": 6.8
},
"urls": {
"self": "https://api.doitong.com/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t",
"cancel": "https://api.doitong.com/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t/cancel"
}
}
Campi dell'output
- output
- stringRisultato prodotto dal modello.
- reasoning
- string | nullRagionamento generato per supportare la risposta finale.
- partial
- booleanIndica se l'output è incompleto.
- error
- string | nullMessaggio che descrive un errore, se si è verificato.
- usage
- object {prompt_tokens, completion_tokens, total_tokens, cost, prompt_tokens_details} | nullDettagli sul consumo di token.
Altre varianti di OpenRouter Router
Altri modelli: LLM
Per gli agenti AI: questa pagina in Markdown è disponibile all'indirizzo https://doitong.com/it/developers/models/openrouter/router.md. Tutti i modelli e le guide: https://doitong.com/developers/llms.txt