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Meta · Open weights

Llama 3.3 70B

Added Jul 3, 2025Open weights

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Use in the app

Meta's Llama 3.3 with 70B parameters. TEE-backed inference with provider attestation support.

Input
Text
Output
Text
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$1.75
Output
$2.75
Cache read
$1.75

Reference prices as published by the model provider. API example

Specifications

Context window
128K
Max output
16.4K
Average output (7 days)
339 tokens
Longer than 36% of 465 models

Side by side

Llama 3.3 70B compared

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

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Llama 3.3 70BMetaThis modelMuse Spark OpenMetaRemove
ProviderMetaMeta
Discovery tierOpen weightsFrontier
AddedJul 3, 2025Oct 15, 2026
Limits
Context window128K1.05MBest
Max output16.38K—
Average output339 tokens1.3K tokens
Pricing, USD per 1M tokens
Input$1.75$1.25Best
Output$2.75Best$4.25
Cache read$1.75$0.15Best
Benchmark indexes
Intelligence Index7.738.9Best
Coding Index11.9—
Math Index7.7—
Capabilities
ReasoningNoYes
ToolsNoYes
VisionNoYes
Structured outputNoYes
Reasoning effort—low, medium, high, xhigh

Public evaluations

Benchmarks

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

  • Llama 3.3 70B
  • Muse Spark Open

Intelligence Index

  • Llama 3.3 70B7.7
  • Muse Spark Open38.9

Llama 3.3 70B: Better than 11% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Llama 3.3 70B: 11.9
    • Muse Spark Open: —

  • Math Index

    Composite of the math evaluations

    • Llama 3.3 70B: 7.7
    • Muse Spark Open: —

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Llama 3.3 70B: —
    • Muse Spark Open: 49.7%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Llama 3.3 70B: —
    • Muse Spark Open: 20.9%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Llama 3.3 70B: —
    • Muse Spark Open: 1,279 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Llama 3.3 70B: —
    • Muse Spark Open: 1,401 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Llama 3.3 70B: —
    • Muse Spark Open: 18.6%

  • AA-LCRv1.1

    Reasoning across long documents

    • Llama 3.3 70B: 15.7%
    • Muse Spark Open: 78.3%Top

  • MLCR-AA

    Long-context reasoning over medical records

    • Llama 3.3 70B: —
    • Muse Spark Open: 7.8%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Llama 3.3 70B: 3.6%
    • Muse Spark Open: 43.9%Top

  • GPQA Diamond

    Graduate-level science questions

    • Llama 3.3 70B: 49.8%
    • Muse Spark Open: 89.0%Top

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Llama 3.3 70B: 71.3%
    • Muse Spark Open: —

  • IFBench

    Following precise output instructions

    • Llama 3.3 70B: 47.1%
    • Muse Spark Open: —

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Llama 3.3 70B: —
    • Muse Spark Open: 8.1%

  • SciCode

    Scientific computing in Python

    • Llama 3.3 70B: —
    • Muse Spark Open: 58.4%

  • LiveCodeBench

    Recent competitive programming problems

    • Llama 3.3 70B: 28.8%
    • Muse Spark Open: —

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Llama 3.3 70B: 7.7%
    • Muse Spark Open: —

  • AIME

    Competition mathematics

    • Llama 3.3 70B: 30.0%
    • Muse Spark Open: —

  • MATH-500

    Competition math problems

    • Llama 3.3 70B: 77.3%
    • Muse Spark Open: —

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Llama 3.3 70B: 3.0%
    • Muse Spark Open: —

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Llama 3.3 70B: 26.6%
    • Muse Spark Open: —

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Llama 3.3 70B: 15.7%
    • Muse Spark Open: 78.3%Top

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": "TEE/llama3-3-70b",
    "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="TEE/llama3-3-70b",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Meta

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