Change base_url and compare providers without reworking the call shape.
NextModel India · production gateway · OpenAI-compatible
All models.One API.
Control AI API spend with one OpenAI-compatible hosted API for Indian teams. Misses call the real upstream, verified exact-cache replays are billed at a discount, and receipts keep spend visible without rewriting your SDK integration.
›prompt: "Pick a model for this workload before you scale support or product flows."
anclaude-sonnet-4-51.2s
cost: $0.00321
opgpt-4o-mini0.6s
cost: $0.00012
gogemini-2-5-flash0.5s
cost: $0.00008
dedeepseek-v30.9s
cost: $0.00037
Requests / sec42,891
Lowest input$0.112
Model sources42 / growing
Gateway statusOK
Who it's for
Built for developers and small teams with real API traffic.
If you're watching token spend, repeated requests, and integration speed, this is the hosted API layer above your existing 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.
Direct answer
What is NextModel?
NextModel is an OpenAI-compatible hosted API for developers and small teams that need one place to handle Fresh fallback, Exact cache discounts, and transparent receipts before model spend scales.
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.
supported model sources · not official partnerships
anAnthropicopOpenAIgoGooglevoVolcenginealAlibaba ClouddeDeepSeekopOpenRoutermoMoonshotanAnthropicopOpenAIgoGooglevoVolcenginealAlibaba ClouddeDeepSeekopOpenRoutermoMoonshot
why nextmodel
One gateway.
Keep spend, policies, and sources visible.
Keep model choice, budget rules, source comparison, and usage reporting out of application code. The API stays familiar while the decision layer stays visible for India-based product, support, and platform teams.
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.
live model graph
42 models,
one shortlist.
One endpoint for model comparison. Inspect price, latency estimates, provider source, and workload fit before you route production traffic across high-volume workloads.
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.
Cost governance
Keep Fresh, cache, and receipts visible before scale.
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 benchmarkStart in India
Pick the model, then govern the spend.
Open quickstart, copy a request, and compare your real workload against Fresh and Exact cache pricing.