NOUMENOpen app
NOUMEN

Meta · Open weights

Llama 3.3 70B

Added Jul 3, 2025Open weights

Compare models

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

Loading models…

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.

Compare models

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

Loading models…

Llama 3.3 70BMetaThis modelMuse WonderMetaRemove
ProviderMetaMeta
Discovery tierOpen weightsFrontier
AddedJul 3, 2025Oct 12, 2026
Limits
Context window128K1.05MBest
Max output16.38K—
Average output339 tokens1.2K tokens
Pricing, USD per 1M tokens
Input$1.75Best$4.00
Output$2.75Best$20.00
Cache read$1.75$0.20Best
Performance
Throughput—113.9 tok/s
Time to first token—1.8 s
Request success (24h)—99.5%
Benchmark indexes
Intelligence Index7.752.4Best
Coding Index11.9—
Math Index7.7—
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.

  • Llama 3.3 70B
  • Muse Wonder

Intelligence Index

  • Llama 3.3 70B7.7
  • Muse Wonder52.4

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 Wonder: —

  • Math Index

    Composite of the math evaluations

    • Llama 3.3 70B: 7.7
    • Muse Wonder: —

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Llama 3.3 70B: —
    • Muse Wonder: 68.1%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Llama 3.3 70B: —
    • Muse Wonder: 39.1%

  • Harvey LAB-AAv1.1

    Legal tasks passed in full, with no material hallucination

    • Llama 3.3 70B: —
    • Muse Wonder: 10.0%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Llama 3.3 70B: —
    • Muse Wonder: 1,659 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Llama 3.3 70B: —
    • Muse Wonder: 1,770 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Llama 3.3 70B: —
    • Muse Wonder: 30.6%

  • AA-LCRv1.1

    Reasoning across long documents

    • Llama 3.3 70B: 15.7%
    • Muse Wonder: 80.7%Top

  • MLCR-AA

    Long-context reasoning over medical records

    • Llama 3.3 70B: —
    • Muse Wonder: 22.2%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Llama 3.3 70B: 3.6%
    • Muse Wonder: 50.1%Top

  • GPQA Diamond

    Graduate-level science questions

    • Llama 3.3 70B: 49.8%
    • Muse Wonder: —

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Llama 3.3 70B: 71.3%
    • Muse Wonder: —

  • IFBench

    Following precise output instructions

    • Llama 3.3 70B: 47.1%
    • Muse Wonder: —

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Llama 3.3 70B: —
    • Muse Wonder: 52.0%

  • SciCode

    Scientific computing in Python

    • Llama 3.3 70B: —
    • Muse Wonder: 60.3%

  • LiveCodeBench

    Recent competitive programming problems

    • Llama 3.3 70B: 28.8%
    • Muse Wonder: —

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Llama 3.3 70B: 7.7%
    • Muse Wonder: —

  • AIME

    Competition mathematics

    • Llama 3.3 70B: 30.0%
    • Muse Wonder: —

  • MATH-500

    Competition math problems

    • Llama 3.3 70B: 77.3%
    • Muse Wonder: —

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Llama 3.3 70B: 3.0%
    • Muse Wonder: —

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Llama 3.3 70B: 26.6%
    • Muse Wonder: —

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Llama 3.3 70B: 15.7%
    • Muse Wonder: 80.7%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

Other Meta models, starting with the same family.