The fastest LLM on Earth.

GPT-5 level intelligence just got 20× faster.

Get Started See the benchmarks
Shutterstock Kogan Haast Lightspeed Blackbird
Benchmarks

Higher quality. A fraction of the latency.

Same tasks, same scoring. Celeris matches or outperforms GPT-5 (reasoning off), GPT-5 mini, Gemini 2.5 Flash, and Nova Micro on accuracy — at a fraction of the latency.

Read the full breakdown
01 · Intelligence
response time (ms) → slower MMLU-Pro accuracy → 01,0002,0003,000 807060 GPT-5 · reasoning off 80.2% · 2.4s GPT-5 mini · reasoning off 73.8% · 2.5s Nova Micro 54.5% · 1.5s Gemini 2.5 Flash 75.2% · 2.8s Celeris 116 ms · 81.7%
Figure 1. MMLU-Pro accuracy vs. response time. Up and to the left is better. GPT-5, GPT-5 mini, and Gemini 2.5 Flash measured with reasoning / thinking off.
MMLU-Pro accuracy81.7
Response time · p50116 ms
Speedup vs baseline20×
Response time · p5023.5 ms
Speedup vs GPT-548×
Task accuracy100%
02 · Speed
04008001,200 ms Celeris 23.5 ms Nova Micro 503 ms GPT-5 mini 992 ms GPT-5 1,128 ms
Figure 2. Simple-workflow response time (p50) over 10 JSON tasks. All four models score 100% — this view is purely speed. GPT-5 and GPT-5 mini measured with reasoning off.
Read more
Architecture

A new architecture for language model inference.

Current autoregressive models generate one token at a time. Every token depends on the one before it, making latency fundamentally sequential. Celeris is a new inference architecture for language models, built on diffusion, that achieves latency and quality previous diffusion systems could not.

The result is an OpenAI-compatible API that delivers intelligent responses in milliseconds.

For builders

Three lines to swap. Then everything is faster.

  • ›The API is OpenAI-compatible. Point your SDK at api.celeris.ai and keep the code you already wrote.
  • ›Streaming is on by default. Responses begin within 24 ms, with no buffering or batch delay.
  • ›Pricing is per token. You are billed for the tokens you generate, so speed costs you nothing extra.
Get an API keyRead the docs
quickstart.pypython
from celeris import Celeris

client = Celeris(api_key="sk-…")

# same call shape you already know
stream = client.chat.completions.create(
    model="celeris-1",
    messages=[{"role": "user",
               "content": "Explain a Kalman filter"}],
    stream=True,
)
# → 24 ms response time · 1,180 tok/s
Access

Simple, usage-based pricing.

Pay as you go
$2/ M input tokens
$6/ M output tokens
You get full-speed celeris-1 and pay only for what you use.
  • +Streaming, OpenAI-compatible API
Get started
Enterprise
Custom
Dedicated capacity, security, and support.
  • +Volume pricing & dedicated clusters
  • +SSO · SOC 2 · VPC
  • +Solutions engineering
Contact sales

Start building with Celeris.

Experience the new paradigm of inference.

Get StartedSee the benchmarks