celeris · Balanced
Celeris 1
Celeris 1 is a diffusion language model built for ultra-low-latency classification, extraction, judging, query rewriting, and other short structured responses.
- Input
- Text
- Output
- Text
- Discovery tier
- Balanced
Pricing
USD per 1M tokens- Input
- $2.00
- Output
- $6.00
Reference prices as published by the model provider. API example
Specifications
- Context window
- 8.2K
- Max output
- 8.2K
Side by side
Celeris 1 compared
Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.
| Celeris 1celerisThis model | Mistral SabaMistralRemove | |
|---|---|---|
| Provider | celeris | Mistral |
| Discovery tier | Balanced | Open weights |
| Added | Jul 25, 2026 | Jan 1, 2024 |
| Limits | ||
| Context window | 8.19K | 32.77KBest |
| Max output | 8.19K | 26.21KBest |
| Average output | — | 63 tokens |
| Pricing, USD per 1M tokens | ||
| Input | $2.00 | $0.20Best |
| Output | $6.00 | $0.60Best |
| Performance | ||
| Request success (24h) | — | 100.0% |
| Benchmark indexes | ||
| Intelligence Index | 6.3 | 6.5Best |
| Coding Index | 14.4 | — |
| Capabilities | ||
| Reasoning effort | — | — |
Public evaluations
Benchmarks
Independent evaluation results, with how each score ranks among the models in the Noumen catalog.
- Celeris 1
- Mistral Saba
Intelligence Index
- Celeris 16.3
- Mistral Saba6.5
Celeris 1: Better than 4% of 326 scored models
Indexes
Coding Index
Composite of the coding evaluations
- Celeris 1: 14.4
- Mistral Saba: —
Agentic work
AutomationBench-AA
Workflow automation, penalized for guardrail violations
- Celeris 1: 0.4%
- Mistral Saba: —
AutomationBench-AA tasks completed
Workflows finished end to end without a guardrail violation
- Celeris 1: 0.2%
- Mistral Saba: —
AA-Briefcasev1.1
Agentic knowledge work, rated head to head
- Celeris 1: 151 Elo
- Mistral Saba: —
GDPval-AAv2.1
Economically valuable occupational tasks, rated head to head
- Celeris 1: 301 Elo
- Mistral Saba: —
Document reasoning
GDP.pdf
Professional reasoning over PDF documents, all-pass rate
- Celeris 1: 1.2%
- Mistral Saba: —
AA-LCRv1.1
Reasoning across long documents
- Celeris 1: 38.0%
- Mistral Saba: —
Reasoning and knowledge
Humanity’s Last Exam
Expert-level questions across academic fields
- Celeris 1: 6.8%Top
- Mistral Saba: 4.3%
GPQA Diamond
Graduate-level science questions
- Celeris 1: 63.1%Top
- Mistral Saba: 42.4%
MMLU-Pro
Multi-subject knowledge and reasoning
- Celeris 1: 78.0%Top
- Mistral Saba: 61.1%
Coding
Terminal-Benchv4.0
Practical software engineering in a terminal
- Celeris 1: 0.0%
- Mistral Saba: —
SciCode
Scientific computing in Python
- Celeris 1: 21.6%
- Mistral Saba: —
Math
AIME
Competition mathematics
- Celeris 1: —
- Mistral Saba: 13.0%
MATH-500
Competition math problems
- Celeris 1: —
- Mistral Saba: 67.7%
Legacy evaluations
AA-LCR (unversioned)
Long-context reasoning, earlier grading
- Celeris 1: 38.0%
- Mistral Saba: —
Scores from Artificial Analysis, updated Oct 26, 2026. Artificial Analysis
Developers
Use it through the API
Noumen’s API is OpenAI-compatible: any Chat Completions client works with a base URL and a key.
curl https://www.noumen.xyz/api/v1/chat/completions \
-H "Authorization: Bearer $NOUMEN_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "celeris-1",
"messages": [{ "role": "user", "content": "Explain this stack trace." }]
}'import os
from openai import OpenAI
client = OpenAI(base_url="https://www.noumen.xyz/api/v1", api_key=os.environ["NOUMEN_API_KEY"])
reply = client.chat.completions.create(
model="celeris-1",
messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)