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Meta · Open weights

Anubis 70B v1

Added Jan 1, 2024Open weights

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Use in the app

L3.3 finetune for roleplaying.

Input
Text
Output
Text
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$0.31
Output
$0.31

Reference prices as published by the model provider. API example

Specifications

Context window
65.5K
Max output
16.4K
Average output (7 days)
423 tokens
Longer than 43% of 465 models

Side by side

Anubis 70B v1 compared

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

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Anubis 70B v1MetaThis modelMuse WonderMetaRemove
ProviderMetaMeta
Discovery tierOpen weightsFrontier
AddedJan 1, 2024Oct 12, 2026
Limits
Context window65.54K1.05MBest
Max output16.38K—
Average output423 tokens1.2K tokens
Pricing, USD per 1M tokens
Input$0.31Best$4.00
Output$0.31Best$20.00
Cache read—$0.20
Performance
Throughput—113.9 tok/s
Time to first token—1.8 s
Request success (24h)21.4%99.5%Best
Benchmark indexes
Intelligence Index—52.4
Capabilities
ReasoningNoYes
ToolsNoYes
VisionNoYes
Structured outputNoYes
PDF inputNoYes
Audio inputNoYes
Video inputNoYes
Reasoning effort—minimal, low, medium, high, xhigh, max

Public evaluations

Benchmarks

Independent evaluation results, with how each score ranks among the models in the Noumen catalog.

  • Anubis 70B v1
  • Muse Wonder

Intelligence Index

  • Anubis 70B v1—
  • Muse Wonder52.4
3.8Catalog models by score55.1

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Anubis 70B v1: —
    • Muse Wonder: 68.1%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Anubis 70B v1: —
    • Muse Wonder: 39.1%

  • Harvey LAB-AAv1.1

    Legal tasks passed in full, with no material hallucination

    • Anubis 70B v1: —
    • Muse Wonder: 10.0%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Anubis 70B v1: —
    • Muse Wonder: 1,659 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Anubis 70B v1: —
    • Muse Wonder: 1,770 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Anubis 70B v1: —
    • Muse Wonder: 30.6%

  • AA-LCRv1.1

    Reasoning across long documents

    • Anubis 70B v1: —
    • Muse Wonder: 80.7%

  • MLCR-AA

    Long-context reasoning over medical records

    • Anubis 70B v1: —
    • Muse Wonder: 22.2%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Anubis 70B v1: —
    • Muse Wonder: 50.1%

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Anubis 70B v1: —
    • Muse Wonder: 52.0%

  • SciCode

    Scientific computing in Python

    • Anubis 70B v1: —
    • Muse Wonder: 60.3%

Legacy evaluations

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Anubis 70B v1: —
    • Muse Wonder: 80.7%

Scores from Artificial Analysis, updated Oct 26, 2026. Artificial Analysis

Live routing data

Performance and reliability

Recent averages across requests routed for this model. Your own speed depends on prompt length, reasoning effort and load.

Prompt cache hits
0.0%
Request success (24h)
21.4%

Cache hits are the share of input tokens recently served from a prompt cache across all requests. They are not a forecast of your own hit rate.

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": "TheDrummer/Anubis-70B-v1",
    "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="TheDrummer/Anubis-70B-v1",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Meta

Other Meta models, starting with the same family.