NOUMENOpen app

Meta · Open weights

Llama 3.1 70B Celeste v0.1

Added Jan 1, 2024Open weights

Compare models

Choose up to 3 models to compare.1 of 3 chosen

Loading models…

Use in the app

Creative model based on Llama 3.1 70B

Input
Text
Output
Text
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$0.49
Output
$0.49

Reference prices as published by the model provider. API example

Specifications

Context window
32.8K
Max output
—

Side by side

Llama 3.1 70B Celeste v0.1 compared

Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.

Compare models

Choose up to 3 models to compare.1 of 3 chosen

Loading models…

Llama 3.1 70B Celeste v0.1MetaThis modelMuse Spark 1.3 ContributorMetaRemove
ProviderMetaMeta
Discovery tierOpen weightsEfficient
AddedJan 1, 2024Sep 2, 2026
Limits
Context window32.77K1.05MBest
Average output—1.2K tokens
Pricing, USD per 1M tokens
Input$0.49$0.10Best
Output$0.49$0.20Best
Cache read—$0.002
Performance
Throughput—145.4 tok/s
Time to first token—3.9 s
Request success (24h)—99.4%
Capabilities
ReasoningNoYes
ToolsNoYes
VisionNoYes
Structured outputNoYes
PDF inputNoYes
Audio inputNoYes
Video inputNoYes
Reasoning effort—minimal, low, medium, high, xhigh

Public evaluations

Benchmarks

There are no public benchmark results for these models yet.

Developers

Use it through the API

Noumen’s API is OpenAI-compatible: any Chat Completions client works with a base URL and a key.

Read the quickstart
curl https://www.noumen.xyz/api/v1/chat/completions \
  -H "Authorization: Bearer $NOUMEN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "nothingiisreal/L3.1-70B-Celeste-V0.1-BF16",
    "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="nothingiisreal/L3.1-70B-Celeste-V0.1-BF16",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Meta

Other Meta models, starting with the same family.