The fastest LLM on Earth.

Responses up to 15x faster. Same API shape, same tasks, a fraction of the latency.

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A fraction of the latency.

Same tasks, same scoring. 75.9 on MMLU-Pro at a 158 ms median response, more than 10x faster than the models it is measured against.

Read the full breakdown
response time (ms, log scale) → slower MMLU-Pro accuracy → 1003001,0003,000 806040200 Gemini 3.5 Flash Lite 83.0% · 1.2s GPT-5 81.9% · 2.0s GPT-5 mini 78.5% · 2.5s Gemini 2.5 Flash 73.0% · 2.6s Mercury 2 63.7% · 257 ms Celeris-1 158 ms · 75.9%
Figure 1. MMLU-Pro accuracy (5-shot, chain-of-thought) vs. response time (log scale). Up and to the left is better. The reasoning budget for Celeris, GPT-5, GPT-5 mini, and the Gemini models was set to 0, and Mercury 2 ran in instant mode - the fastest possible configuration for every model.
MMLU-Pro accuracy75.9
Response time · p50158 ms
Speedup vs GPT-513×
Output tok/s · p501,664
Speedup vs GPT-524×
Server time vs GPT 525×
06001,2001,800 tok/s Celeris 1,664 Mercury 2 324 Gemini 3.5 Flash Lite 224 Gemini 3.5 Flash 134 GPT-5 mini 99 GPT-5 69
Figure 2. Output speed (p50 tokens per second) on ~1,000-token long-form prompts, 50 requests per model, outputs capped at 1,024 tokens. Full methodology →
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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 responses in milliseconds.

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Three lines to swap. Then everything is faster.

  • ›The API is OpenAI-compatible. Point your SDK at inference.celeris.ai and keep the code you already wrote.
  • ›Streaming is on by default. Responses as low as 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.
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quickstart.pypython
from openai import OpenAI

client = OpenAI(
    base_url="https://inference.celeris.ai/celeris-1/v1",
    api_key="ck_…",
)

# same call shape you already wrote
stream = client.chat.completions.create(
    model="celeris-1",
    messages=[{"role": "user",
               "content": "Explain a Kalman filter"}],
    stream=True,
)

Start building with Celeris-1.

celeris-1 is available through the Celeris inference API. OpenAI-compatible, streaming on by default. Keys are live immediately.

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