Change base_url and compare providers without reworking the call shape.
NextModel Россия · production gateway · совместимый с OpenAI
All models.One API.
Контролируйте расходы на AI API через один hosted API, совместимый с OpenAI, для команд в России. Misses обращаются к реальному upstream, проверенные replay Exact cache тарифицируются со скидкой, а чеки сохраняют видимость расходов без переписывания SDK-интеграции.
›prompt: "Vyberite model dlya etogo workload."
anclaude-sonnet-4-51.2s
stoimost: $0.00321
opgpt-4o-mini0.6s
stoimost: $0.00012
gogemini-2-5-flash0.5s
stoimost: $0.00008
dedeepseek-v30.9s
stoimost: $0.00037
Requests / sec42,891
Lowest input$0.112
Model sources42 / growing
Gateway statusOK
Для кого
Создано для разработчиков и небольших команд с реальным API-трафиком.
Если вы следите за расходом token, повторяющимися запросами и скоростью интеграции, это hosted API-слой над вашим текущим SDK.
NextModel turns Fresh calls, exact-cache discounts, and receipts into one visible control layer above the SDK. That gives developers a place to keep unit economics honest and adopt a hosted API without reworking the app.
See the difference between Fresh and Exact cache before traffic multiplies.
Each request can expose served mode, usage source, and receipt links.
Прямой ответ
Что такое NextModel?
NextModel — это hosted API, совместимый с OpenAI, для разработчиков и небольших команд, которым нужно в одном месте управлять Fresh fallback, скидками Exact cache и прозрачными чеками до того, как расходы на модели вырастут.
Teams use NextModel when they want a compatible hosted API without losing visibility into billing facts. The gateway keeps the familiar OpenAI SDK shape while adding transparent pricing context, exact-cache reuse, and receipts.
podderzhivaemye istochniki modelei · eto ne oficialnye partnerstva
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pochemu nextmodel
Odni vorota.
Derzhite rashody, politiki i istochniki na vidu.
Vynesite vybor modeli, pravila byudzheta, sravnenie istochnikov i reporting po ispolzovaniyu iz koda prilozheniya. API ostayotsya znakomym, a sloi prinyatiya reshenii stanovitsya vidimym dlya komand produkta i platformy.
01 · one sdk
OpenAI SDK, many model sources.
Already using OpenAI? Change base_url, keep chat completions, streaming, tools, and JSON-oriented workflows.
pythonnodecurl
client = OpenAI(
base_url="https://api.nextmodel.app/v1",
api_key=os.environ["NM_KEY"],
)
client.chat.completions.create(
model="claude-sonnet-4-5",
messages=[...],
)02 · routing
Policies before production traffic.
Route by workload, source, budget, latency, or capability instead of scattering rules across services.
03 · billing
Spend by key, project, and team.
See which application paths drive token cost and turn model selection into an operational decision.
api.web$353 · 42%agent.eval$235 · 28%rag.ingest$151 · 18%dev$101 · 12%
04 · price
Compare the gap before calling.
05 · governance
Budget-aware model operations.
Bring your own keys, assign project limits, and keep a clear audit trail for model API spend.
42 models
tracked dimensionsproject · key · source
policy layerbudgets · providers
SDK modeOpenAI-compatible
06 · regions
Domestic + global, one endpoint.
Compare Chinese and global model sources from one interface without implying official provider partnership.
zhivoi graf modelei
42 modeli,
odin shortlist.
Odin endpoint dlya sravneniya modelei. Proverte cenu, ocenku zaderzhki, istochnik providera i sootvetstvie workloadu do togo, kak routing poydet v produkcionnyi trafik.
Quickstart
Three steps from an existing SDK to visible spend control.
Issue a key for the project, environment, or workload you want to track.
Set the OpenAI SDK base URL to https://api.nextmodel.app/v1.
Use a model ID from the catalog, then compare cost and output quality.
Контроль расходов
Сохраняйте Fresh, cache и чеки видимыми до роста расходов.
This is the layer developers and small teams need once request volume and spend start to grow.
Understand which applications and environments are driving model spend.
See which requests hit the real upstream and which were safely replayed.
Transparent workflows
- Send requests through one OpenAI-compatible interface.
- Misses call the real upstream model.
- Exact cache hits are replayed with discounted billing.
- Use receipts and usage exports to reconcile what happened.
Docs CTA
Copy a working request in Python, Node, or curl.
Python
from openai import OpenAI
client = OpenAI(
api_key="YOUR_API_KEY",
base_url="https://api.nextmodel.app/v1"
)
resp = client.chat.completions.create(
model="doubao-seed-2-0-mini",
messages=[{"role": "user", "content": "Hello from NextModel"}]
)
print(resp.choices[0].message.content)Node
import OpenAI from "openai";
const client = new OpenAI({
apiKey: process.env.NEXTMODEL_API_KEY,
baseURL: "https://api.nextmodel.app/v1",
});
const response = await client.chat.completions.create({
model: "doubao-seed-2-0-mini",
messages: [{ role: "user", content: "Hello from NextModel" }],
});
console.log(response.choices[0].message.content);curl
curl https://api.nextmodel.app/v1/chat/completions \
-H "Authorization: Bearer $NEXTMODEL_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "doubao-seed-2-0-mini",
"messages": [{"role": "user", "content": "Hello from NextModel"}]
}'New benchmark
Before you enable caching, measure whether reuse is safe.
CacheSafety Bench checks safe hit rate, bad hit rate, semantic trap failures, and cost savings before teams trust a cache layer.
CacheSafety Bench helps teams compare safe hit rate, bad hit rate, semantic trap failures, and cost savings before they trust a cache layer in production.
Explore benchmarkNachnite seychas
Pick the model, then govern the spend.
Open quickstart, copy a request, and compare your real workload against Fresh and Exact cache pricing.