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  4. Celeris-1
CE
Celeris
Released July 24, 2026

Celeris-1

Celeris-1 by Celeris is a diffusion-based LLM generating 1,664 tokens/sec with 158ms median latency. It is 24x faster than GPT 5.

Announcement
Inputs
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Outputs
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Model Overview

Capabilities, design details, and architectural traits

Celeris-1 - Diffusion-based language generation at real-time latency

Celeris-1 is a diffusion LLM that replaces sequential autoregressive decoding with parallel refinement. Instead of predicting one token at a time, it starts with a rough version of the entire response and improves it over a few rapid passes, similar to how a blurry image comes into focus. This architecture is designed to preserve frontier-level reasoning while operating within real-time latency constraints.

TraitDetail
Diffusion architectureGenerates and refines the entire output sequence simultaneously across multiple passes, rather than token-by-token autoregressive decoding
1,664 tokens/secOver 5x the output speed of Mercury 2 and 24x faster than GPT 5
158ms median latencyResponses land below the threshold at which humans perceive delay, enabling live conversational audio and real-time control loops
Second commercial diffusion LLM APIFollows Inception's Mercury as the second commercially available diffusion language model API

Targeted at latency-sensitive production workloads

Celeris-1 is engineered for agentic tool orchestration, classification, data extraction, and real-time voice pipelines. Its sub-perceptual latency compounds across multi-step agentic loops where dozens of internal reasoning calls for routing, validation, and tool selection each complete in milliseconds instead of seconds. The model is accessible through an OpenAI-compatible API, requiring only a base URL and key change to integrate into existing workflows.

Benchmark Performance

Independent evaluations · Artificial Analysis

12.4%
Intelligence
14.4%
Coding Index
2.4%
Agentic Index

Accuracy & Capability Details

GPQA - Graduate Science63.1%
Humanity's Last Exam6.8%
SciCode - Scientific Coding20.7%
Long Context Reasoning37.3%
Specs
Context window
-tokens
Input pricing
$0.20per 1M tokens
Output pricing
$0.70per 1M tokens

Prices in USD.

Compare Models Side-by-Side

Evaluate specifications, pricing, and independent benchmark indices

Model Details
General Info
ProviderCelerisAnthropicAnthropic
Release DateJuly 24, 2026July 24, 2026June 9, 2026
Knowledge Cutoff-May 2026-
Context & Limits
Context Window-1M1M
Pricing (per 1M tokens)
Input Pricing
$0.20
Best Input Pricing
$5$10
Output Pricing
$0.70
Best Output Pricing
$25$50
Modalities
Inputs
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Outputs
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Benchmarks (0-100)
Intelligence Index12.4
63.1
Best Intelligence Index
62.1
Coding Index14.4
78.0
Best Coding Index
76.5
Agentic Index2.4
59.2
Best Agentic Index
56.6
Celeris-1
Claude Opus 5
Claude Fable 5

GPQA Benchmark

Graduate-level reasoning and expert Q&A evaluation.

63%
Celeris-1
GPQA Benchmark
Score: 63%
CE
Celeris-1
93%
Claude Opus 5
GPQA Benchmark
Score: 93%
Claude Opus 5
93%
Claude Fable 5
GPQA Benchmark
Score: 93%
Claude Fable 5

Humanity's Last Exam

Extremely difficult logical reasoning and knowledge.

7%
Celeris-1
Humanity's Last Exam
Score: 7%
CE
Celeris-1
55%
Claude Opus 5
Humanity's Last Exam
Score: 55%
Claude Opus 5
56%
Claude Fable 5
Humanity's Last Exam
Score: 56%
Claude Fable 5

Long Context Reasoning

Logical reasoning over long context windows.

37%
Celeris-1
Long Context Reasoning
Score: 37%
CE
Celeris-1
76%
Claude Opus 5
Long Context Reasoning
Score: 76%
Claude Opus 5
77%
Claude Fable 5
Long Context Reasoning
Score: 77%
Claude Fable 5

Independent evaluation data provided by Artificial Analysis. To view the latest benchmarks and full details, visit their official site.

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