AnthropicFrontier
Claude Fable 5
Anthropic's Mythos-class flagship — the sharpest reasoner in the fleet
200K context
Ask once — KernelFold routes your request across 15 frontier models from labs like Anthropic, OpenAI, DeepSeek and Moonshot, runs specialists in parallel, verifies the work, and streams back one answer. It learns per task and routes around outages automatically.
Free to start · no credit card · OpenAI-compatible API
Orchestrating models from
You never pick a model — the router does, per task, using live capability, cost, latency, and learned accuracy. These are the flagships it leads with — every plan gets all of them.
Anthropic's Mythos-class flagship — the sharpest reasoner in the fleet
200K context
OpenAI's frontier lane — elite reasoning & agentic coding
400K context
Deep reasoning & verification at million-token context
1M context
The Claude 5 workhorse — frontier-family quality, everyday speed
200K context
Deep technical reasoning at great value
128K context
The fleet's top dedicated coder
256K context
General work at million-token context, great value
1M context
Frontier reasoning with native tool use & vision
400K context
Availability is monitored continuously — if a model degrades mid-answer, KernelFold switches and keeps streaming. See live status
Model choice blends capability, cost, latency, and historical accuracy — read live from the database on every request. The more you use it, the smarter it routes.
KernelFold streams the single best model for latency. KernelFold Ultra runs the full ensemble — plan, route, parallel workers, verify, reflect, synthesize.
Mid-stream drops and gateway 502s are retried with automatic model fallback. The user still gets an answer.
Every pipeline stage emits an event. Watch the pipeline animate live; audit cost, latency, and routing rationale after.
A separate model verifies each answer before consensus — confidence you can see, not vibes.
The planner decomposes your request into tasks with dependencies.
Each task is scored against every model on capability, cost, latency, and learned accuracy.
Specialist workers run in parallel, each on the model best suited to its task.
An independent model checks the work — errors are caught before you see them.
Consensus resolves disagreements; one answer streams back, live.
Your plan is a monthly pool of tokens shared across every model and the API — a quick chat spends a few thousand, a deep multi-agent run more. Paid plans also have a rolling five-hour window and a fixed weekly quota: these add no tokens, they just keep one runaway agent loop from draining a month in an afternoon. Allowances reset monthly. When you run out, upgrade or wait for the reset; we never charge overages.
Try the whole fleet, every month.
1.5M tokens every month
The 1.5M-token monthly allowance is the only cap — no rolling windows, no weekly quota. When it runs out, upgrade or wait for the monthly reset.
For daily individual use.
120M tokens every month
Up to 4M tokens in any rolling five-hour window and 30M per week, subject to your 120M monthly allowance.
For heavy and agentic workloads.
360M tokens every month
Up to 7M tokens in any rolling five-hour window and 90M per week, subject to your 360M monthly allowance.
Every plan uses the same models and the same API — plans differ only in monthly tokens and how fast you may spend them. Upgrade, downgrade, or cancel any time from your billing page.
Free to start · no credit card required
import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://www.kernelfold.com/api/v1",
apiKey: process.env.KERNELFOLD_API_KEY,
});
const res = await client.chat.completions.create({
model: "kernelfold-ultra", // the whole fleet
messages: [{ role: "user", content: "..." }],
stream: true,
});