Skip to content

Designed to reduce unnecessary context without degrading the model response — and measured per tool-set, per request. OpenAI-compatible shapes, published rates, and the catalog in Studio — chat, voice, and code on one key.

The Free plan is $0. Notre Runtime is included on every plan.

One key, three products

Everything Caedral builds, on one key.

The laboratory where you try models, the compiler that keeps chat cheap, and the OpenAI-compatible surface you ship on.
  • The AI laboratory

    Caedral Studio

    Chat, voice, images, video, and code in the browser — metered in weekly plan credits as you use it.

  • Economy on chat

    Notre Runtime

    Up to 99.7% fewer tokens in validated Notre V4 MAX workloads — with required-set recall held at 100%.

  • OpenAI-compatible

    API

    One key, one request shape. POST /v1/chat/completions on prepaid at-cost rates with the Notre Fee — never a surprise.

The API, live

The exact requests your app sends — switch product and language in the demo. One base URL, OpenAI-compatible shapes, published rates.

Read the quickstart

POST/v1/chat/completions

Request

import { Caedral } from "caedral"

const client = new Caedral({
  apiKey: process.env.CAEDRAL_API_KEY,
})

const completion = await client.chat.completions.create({
  model: "anthropic/claude-haiku-4.5",
  messages: [{ role: "user", content: "Hello" }],
})

Result · illustrative Notre workload

Status
200 OK
Model
anthropic/claude-haiku-4.5
Input sent
5,103
Output
42
Notre
7,739 saved

Notre Runtime applies on chat completions only. Embed, Rerank, and Voice are different request shapes.

Built by Caedral

Our products and the frontier, classified.

Frontier labs are in the catalog. These four are developed or hosted by Caedral. Classification is explicit — we do not hide our own work behind someone else's name.
Caedral Embed

caedral-embed-e1-small-v1

384-dimensional embeddings. Developed and operated by Caedral. POST /v1/embeddings.

Plan credits

Caedral Rerank

BAAI/bge-reranker-v2-m3

Hosted by Caedral over an external foundation model. POST /v1/rerank.

Prepaid

Caedral Voice

caedral-voice-1

English WAV speech. Developed and operated by Caedral. Notre Runtime does not apply. POST /v1/audio/speech.

Plan credits

Notre Runtime

eligible chat

Native context compiler on POST /v1/chat/completions. Not a SKU. MCP is an integration, not the product name.

Included

Catalog

Live model IDs. Published rates.

682 models across 94 providers, plus the Caedral-operated products above.
ModelIDContext
Anthropic: Claude Fable Latest~anthropic/claude-fable-latest1,000,000
Anthropic: Claude Haiku Latest~anthropic/claude-haiku-latest1,000,000
Anthropic: Claude Opus Latest~anthropic/claude-opus-latest1,000,000
Anthropic: Claude Sonnet Latest~anthropic/claude-sonnet-latest1,000,000
DeepSeek: DeepSeek Flash Latest~deepseek/deepseek-flash-latest1,048,576
DeepSeek: DeepSeek Pro Latest~deepseek/deepseek-pro-latest1,048,576
DeepSeek: DeepSeek V4 Flash Latest~deepseek/deepseek-v4-flash-latest1,048,576

Notre Runtime

Measured. Not promised.

Designed to reduce unnecessary context without degrading the model response — and measured per tool-set, per request. Up to 99.7% fewer tokens in validated Notre V4 MAX workloads — with required-set recall held at 100%.

Fewer input tokens99.7%

M1.4 V4 MAX corpus — the honest bound, including a schema round-trip per required tool.

Blind-holdout recall99.47%

After the policy freeze (564/570) — the gate any stronger claim must pass.

Frozen safety regression192/192

Held — with required-set recall at 100% on the development corpus.

Verified benchmark — M1.4 development corpus · V4 MAX — Notre Runtime
  1. 01Large request context
  2. 02Notre Runtime
  3. 03Reduced model input
  4. 04Model inference
  5. 05Result
Input before
567,755,348
Input sent
1,894,662
Saved
565,860,686

99.7%reduction

TRUE AGGREGATE on the recorded corpus (n=4,962). Output tokens are generated by the model and are not claimed as Notre savings.

Notre Runtime is built into Caedral. No add-on. No separate bill. It is applied on eligible POST /v1/chat/completions when the platform runs Notre — never on embeddings, rerank, voice, or images. Different request shapes do not share this compiler, and we do not pretend they do.

Notre economics

Scale the verified ratio yourself.

Inspect the recorded compiler benchmarks, then project the same ratio onto a realistic input budget at Caedral prices. The model control sets the current tariff — Notre was not A/B tested on every catalog model.

Verified Notre benchmark

Same plan. Same price. More useful AI work.

Notre Runtime is built into Caedral. No add-on. No separate bill. Reduction is from the compiler corpus — not a live A/B of the catalog model you pick. Dollars use that model's current Caedral input tariff.

Up to
99.7%

fewer input tokens, strongest verified

Median
81.4%

across 4 verified workloads

Notre Runtime
Included

No add-on fee. No separate Notre bill.

Benchmark

Model (Caedral input tariff)

Economic scale

Projected from verified benchmark reduction. Same ratio, normalized workload.

Same budget

299.66×

equivalent input workload

  1. 01Input before Notre
  2. 02Notre Runtime compiles
  3. 03Input sent to the model
  • Input before10,000,000
  • Input sent33,372
  • Input saved9,966,628
Reduction
99.7%
Quality
Required-set recall 100%

Projected from verified benchmark

Input tokens avoided

9,966,628

of 10,000,000 baseline input

Usage value preserved · Anthropic: Claude Sonnet 4.6

$29.90

Current Caedral input tariff only. Output is not counted as savings.

More use from the same plan

100→29,966

equivalent requests on the same input budget

Compiler time (not model round-trip)

p50 10.5 ms · p95 50.7 ms

$10 prepaid budget

Without Notre-equivalent: 3,333,333 input tokens.

With this reduction: 998,868,208 equivalent input tokens.

Same budget, projected from the verified ratio and this model's current input price.

At this benchmark's 99.7% reduction rate, a 10,000,000-token input allowance can process roughly 299.66× the equivalent workload before exhausting the same input budget.

Benchmark details
Workload
M1.4 development corpus · V4 MAX
Date
2026-10-03
Runs
4,962
Policy
V4 MAX
Input before (recorded)
567,755,348
Input after (recorded)
1,894,662
Quality methodology
4,962 of 4,962 required tool-sets preserved (0 failures; Wilson 95% CI [99.92%, 100%]). Reduction is the honest primary metric: level-1 disclosure payload plus oracle round-trips for every required tool (1.08 round-trips/case). Pessimistic bound (round-trip for every selected tool): 98.51%. Deterministic lexical router — no LLM in the routing path.
Source
caedral-notre-mcp/benchmarks/results/M1.4-GUARDED-V3.json#v4_max_candidate.m14_development
Confidence
TRUE AGGREGATE SUM/SUM. Blind holdout pending single post-freeze evaluation. Development corpus tuned the lexical triggers; production shadow validation required before default-on.
Fallback rate
0.0%

Full verified table

Live on the platform

Models in the catalog
682

Live model IDs with published rates.

Providers behind one key
94

Frontier labs plus Caedral-operated models.

House voices in the Arsenal
4

Caedral F1, F2, M1, and M2 — English WAV.

Caedral-operated products
4

Embed, Rerank, Voice, Notre Runtime.

Verified Notre input reduction
99.7%

Up to, on the M1.4 V4 MAX corpus — honest bound.

Get started

Start with one API key

Create an account, pick a plan, and route chat, embeddings, rerank, and Notre-enabled completions through Caedral. Notre Runtime is built into Caedral. No add-on. No separate bill.