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Meta · Open weights

Sao10K Stheno 8b

Added Jan 1, 2024Open weights

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Use in the app

Sao10K's latest Stheno fine-tune optimized for instruction following.

Input
Text
Output
Text
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$0.20
Output
$0.20

Reference prices as published by the model provider. API example

Specifications

Context window
16.4K
Max output
8.2K
Average output (7 days)
177 tokens
Longer than 18% of 468 models

Side by side

Sao10K Stheno 8b compared

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

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Sao10K Stheno 8bMetaThis modelGemma 3 4B ITGoogleRemove
ProviderMetaGoogle
Discovery tierOpen weightsOpen weights
AddedJan 1, 2024Jan 1, 2024
Limits
Context window16.38K128KBest
Max output8.19K8.19K
Average output177 tokens115 tokens
Pricing, USD per 1M tokens
Input$0.20$0.20
Output$0.20$0.20
Performance
Throughput30.4 tok/s—
Time to first token2.1 s—
Request success (24h)67.2%100.0%Best
Benchmark indexes
Intelligence Index—4.8
Coding Index—2.7
Math Index—12.7
Capabilities
PDF inputNoYes
Reasoning effort——

Public evaluations

Benchmarks

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

  • Sao10K Stheno 8b
  • Gemma 3 4B IT

Intelligence Index

  • Sao10K Stheno 8b—
  • Gemma 3 4B IT4.8
3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Sao10K Stheno 8b: —
    • Gemma 3 4B IT: 2.7

  • Math Index

    Composite of the math evaluations

    • Sao10K Stheno 8b: —
    • Gemma 3 4B IT: 12.7

Document reasoning

  • AA-LCRv1.1

    Reasoning across long documents

    • Sao10K Stheno 8b: —
    • Gemma 3 4B IT: 6.7%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Sao10K Stheno 8b: —
    • Gemma 3 4B IT: 5.3%

  • GPQA Diamond

    Graduate-level science questions

    • Sao10K Stheno 8b: —
    • Gemma 3 4B IT: 29.1%

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Sao10K Stheno 8b: —
    • Gemma 3 4B IT: 41.7%

  • IFBench

    Following precise output instructions

    • Sao10K Stheno 8b: —
    • Gemma 3 4B IT: 28.3%

Coding

  • LiveCodeBench

    Recent competitive programming problems

    • Sao10K Stheno 8b: —
    • Gemma 3 4B IT: 11.2%

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Sao10K Stheno 8b: —
    • Gemma 3 4B IT: 12.7%

  • AIME

    Competition mathematics

    • Sao10K Stheno 8b: —
    • Gemma 3 4B IT: 6.3%

  • MATH-500

    Competition math problems

    • Sao10K Stheno 8b: —
    • Gemma 3 4B IT: 76.6%

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Sao10K Stheno 8b: —
    • Gemma 3 4B IT: 0.8%

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Sao10K Stheno 8b: —
    • Gemma 3 4B IT: 5.0%

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Sao10K Stheno 8b: —
    • Gemma 3 4B IT: 6.7%

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.

Throughput
30.4 tok/s
Faster than 21% of 254 models
Time to first token
2.1 s
Quicker than 74% of 254 models
Prompt cache hits
0.0%
Request success (24h)
67.2%

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": "Sao10K/L3-8B-Stheno-v3.2",
    "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="Sao10K/L3-8B-Stheno-v3.2",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Meta

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