Change base_url and compare providers without reworking the call shape.
NextModel Polska · production gateway · zgodne z OpenAI
All models.One API.
Kontroluj koszt AI API przez jedno hosted API zgodne z OpenAI dla zespolow w Polsce. Misses wywoluja prawdziwy upstream, zweryfikowane replaye Exact cache sa rozliczane taniej, a potwierdzenia utrzymuja widocznosc kosztow bez przepisywania integracji SDK.
›prompt: "Wybierz model dla tego workloadu."
anclaude-sonnet-4-51.2s
koszt: $0.00321
opgpt-4o-mini0.6s
koszt: $0.00012
gogemini-2-5-flash0.5s
koszt: $0.00008
dedeepseek-v30.9s
koszt: $0.00037
Requests / sec42,891
Lowest input$0.112
Model sources42 / growing
Gateway statusOK
Dla kogo
Zbudowane dla deweloperow i malych zespolow z prawdziwym ruchem API.
Jesli obserwujesz koszt tokenow, powtarzalne requesty i szybkosc integracji, to jest warstwa hosted API nad Twoim obecnym 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.
Prosta odpowiedz
Czym jest NextModel?
NextModel to hosted API zgodne z OpenAI dla deweloperow i malych zespolow, ktore chca zarzadzac Fresh fallback, znizkami Exact cache i przejrzystymi potwierdzeniami zanim koszt modeli wzrosnie.
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.
obslugiwane zrodla modeli · to nie sa oficjalne partnerstwa
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dlaczego nextmodel
Jedna brama.
Utrzymuj wydatki, polityki i zrodla w zasiegu wzroku.
Wyjmij wybor modelu, zasady budzetowe, porownanie zrodel i raportowanie uzycia z kodu aplikacji. API pozostaje znajome, a warstwa decyzyjna staje sie widoczna dla zespolow produktu 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.
graf modeli na zywo
42 modele,
jedna shortlista.
Jeden endpoint do porownywania modeli. Sprawdz cene, szacowana latencje, zrodlo providera i dopasowanie do workloadu, zanim skierujesz ruch produkcyjny.
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.
Zarzadzanie kosztem
Utrzymuj widoczne Fresh, cache i potwierdzenia zanim wydatki wzrosna.
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 benchmarkZacznij teraz
Pick the model, then govern the spend.
Open quickstart, copy a request, and compare your real workload against Fresh and Exact cache pricing.