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Steelskull Electra R1 70b

Added Jan 1, 2024Open weights

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Steelskull Electra R1 70b

Input
Text
Output
Text
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$0.70
Output
$0.70

Reference prices as published by the model provider. API example

Specifications

Context window
32.8K
Max output
—

Side by side

Steelskull Electra R1 70b compared

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

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Steelskull Electra R1 70bMetaThis modelGLM 5.1 TEEZ.aiRemove
ProviderMetaZ.ai
Discovery tierOpen weightsOpen weights
AddedJan 1, 2024Apr 20, 2026
Limits
Context window32.77K202.75KBest
Max output—65.54K
Average output—675 tokens
Pricing, USD per 1M tokens
Input$0.70Best$0.70
Output$0.70Best$2.50
Cache read—$0.18
Performance
Request success (24h)—100.0%
Benchmark indexes
Intelligence Index—26.1
Coding Index—55.8
Capabilities
ReasoningNoYes
ToolsNoYes
Reasoning effort—none, high

Public evaluations

Benchmarks

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

  • Steelskull Electra R1 70b
  • GLM 5.1 TEE

Intelligence Index

  • Steelskull Electra R1 70b—
  • GLM 5.1 TEE26.1
3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Steelskull Electra R1 70b: —
    • GLM 5.1 TEE: 55.8

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Steelskull Electra R1 70b: —
    • GLM 5.1 TEE: 20.3%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Steelskull Electra R1 70b: —
    • GLM 5.1 TEE: 3.0%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Steelskull Electra R1 70b: —
    • GLM 5.1 TEE: 964 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Steelskull Electra R1 70b: —
    • GLM 5.1 TEE: 1,117 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Steelskull Electra R1 70b: —
    • GLM 5.1 TEE: 8.4%

  • AA-LCRv1.1

    Reasoning across long documents

    • Steelskull Electra R1 70b: —
    • GLM 5.1 TEE: 73.7%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Steelskull Electra R1 70b: —
    • GLM 5.1 TEE: 30.1%

  • GPQA Diamond

    Graduate-level science questions

    • Steelskull Electra R1 70b: —
    • GLM 5.1 TEE: 86.8%

  • IFBench

    Following precise output instructions

    • Steelskull Electra R1 70b: —
    • GLM 5.1 TEE: 76.3%

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Steelskull Electra R1 70b: —
    • GLM 5.1 TEE: 2.0%

  • SciCode

    Scientific computing in Python

    • Steelskull Electra R1 70b: —
    • GLM 5.1 TEE: 44.8%

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Steelskull Electra R1 70b: —
    • GLM 5.1 TEE: 43.2%

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Steelskull Electra R1 70b: —
    • GLM 5.1 TEE: 97.7%

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Steelskull Electra R1 70b: —
    • GLM 5.1 TEE: 73.7%

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.

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": "Steelskull/L3.3-Electra-R1-70b",
    "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="Steelskull/L3.3-Electra-R1-70b",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Meta

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