Who is NextModel for?

NextModel is for developers and small teams with real API traffic. It helps them keep spend visible through one OpenAI-compatible hosted API without rewriting their integration.

What problem does it solve?

It turns Fresh calls, upstream cache pricing passed through 1:1, and receipts into one visible layer above the SDK, so developers can understand cost before traffic and spend scale.

Where should I start?

Start with the pricing page, the quickstart docs, and receipts. Those pages show the unit economics, the smallest code change needed to get live, and how each request stays explainable.

NextModel UK · production gateway · OpenAI-compatible

All models.One API.

Control AI API spend with one OpenAI-compatible hosted API for UK teams. Misses call the real upstream, upstream prompt-cache hits are billed 1:1 at official cached rates, and receipts keep the commercial picture visible without rewriting your SDK integration.

Get API Key View pricing Quickstart

›prompt: "Pick a model for this workload in your control plan."

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

Model sources140 / growing

Gateway statusOK

Latest

New model · 2026-09-05

GPT-6 Astra is live on NextModel — 1M context, tiered pricing

Pricing · 2026-09-05

The 272K cliff, explained: GPT-6 Astra pricing with worked examples

Migration · 2026-09-05

Migrating from GPT-5.x to GPT-6 Astra: ids, Responses API, rollback

Community · 2026-09-06

What early testers are actually doing with GPT-6 Astra

Who it's for

Built for UK developers and small teams with live API traffic.

If you're watching token spend, repeat traffic, governance pressure, and deployment speed, this is the hosted API layer above your existing SDK.

NextModel turns Fresh calls, upstream cache pricing passed through 1:1, 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.

OpenAI migrationsKeep the SDK

Change base_url and compare providers without reworking the call shape.

Growing spendSee cost early

See the difference between Fresh and cached pricing before traffic multiplies.

ReceiptsVisible facts

Every request's receipt is hashed, batch-anchored on-chain, and independently verifiable.

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, upstream cache pricing passed through 1:1, 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, upstream cache pass-through, and receipts.

supported model sources · not official partnerships

anAnthropicopOpenAIgoGooglevoVolcenginealAlibaba ClouddeDeepSeekopOpenRoutermoMoonshotanAnthropicopOpenAIgoGooglevoVolcenginealAlibaba ClouddeDeepSeekopOpenRoutermoMoonshot

why nextmodel

One gateway.
Keep spend, policies, and sources in view.

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 UK product, platform, and compliance reviews.

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.

GPT-4o mini$0.15

Doubao Mini$0.20

Gemini Flash$0.30

DeepSeek R1$0.70

Gemini Pro$1.25

Claude Sonnet$3.00

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

140 models,
one shortlist.

One endpoint for model comparison. Review price, latency estimates, provider source, and workload fit before you route production traffic.

Quickstart

Three steps from an existing SDK to visible spend control.

StepCreate an API key

Issue a key for the project, environment, or workload you want to track.

Stepbase_url

Set the OpenAI SDK base URL to https://api.nextmodel.app/v1.

StepStart calling models

Use a model ID from the catalog, then compare cost and output quality.

Cost governance

Keep Fresh, cache, and receipts visible before spend scales.

This is the layer developers and small teams need once request volume and spend start to grow.

Usage analyticsProject + key

Understand which applications and environments are driving model spend.

Billing semanticsFresh + Exact

See which requests hit the real upstream and which were served as idempotent replays.

Transparent workflows

Send requests through one OpenAI-compatible interface.
Misses call the real upstream model.
Upstream prompt-cache hits are billed 1:1 at official cached rates.
Use receipts and usage exports to reconcile what happened.

Docs CTA

Copy a working request in Python, Node, or curl.

curl

curl https://api.nextmodel.app/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $NEXTMODEL_API_KEY" \
  -d '{"model":"qwen/qwen3.7-max","messages":[{"role":"user","content":"Reply with OK."}]}'

Python

# pip install openai
import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["NEXTMODEL_API_KEY"],
    base_url="https://api.nextmodel.app/v1",
)

resp = client.chat.completions.create(
    model="qwen/qwen3.7-max",
    messages=[{"role": "user", "content":"Reply with OK."}],
)
print(resp.choices[0].message.content)

Node.js

// npm install openai
import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.NEXTMODEL_API_KEY,
  baseURL: "https://api.nextmodel.app/v1",
});

const resp = await client.chat.completions.create({
  model: "qwen/qwen3.7-max",
  messages: [{ role: "user", "content":"Reply with OK." }],
});
console.log(resp.choices[0].message.content);

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 benchmark

Compare

Choosing an LLM API?

Before you commit traffic, compare gateways, providers, and per-token prices side by side. Every list shows its data source and last-updated date.

OpenRouter alternatives LLM API providers compared Cheap LLM API options

Start in the UK

Pick the model, then govern the spend.

Open quickstart, copy a request, and compare your real workload against Fresh and cached pricing.

Get your API key Read the quickstart