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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 modelGPT 4.1 NanoOpenAIRemove
ProviderMetaOpenAI
Discovery tierOpen weightsEfficient
AddedJul 3, 2025Jan 1, 2024
Limits
Context window128K1.05MBest
Max output16.38K32.77KBest
Average output339 tokens49 tokens
Pricing, USD per 1M tokens
Input$1.75$0.10Best
Output$2.75$0.40Best
Cache read$1.75$0.025Best
Performance
Request success (24h)—100.0%
Benchmark indexes
Intelligence Index7.77.8Best
Coding Index11.9Best11.1
Math Index7.724.0Best
Capabilities
VisionNoYes
PDF inputNoYes
Reasoning effort——

Public evaluations

Benchmarks

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

  • Llama 3.3 70B
  • GPT 4.1 Nano

Intelligence Index

  • Llama 3.3 70B7.7
  • GPT 4.1 Nano7.8

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: 11.9Top
    • GPT 4.1 Nano: 11.1

  • Math Index

    Composite of the math evaluations

    • Llama 3.3 70B: 7.7
    • GPT 4.1 Nano: 24.0Top

Document reasoning

  • AA-LCRv1.1

    Reasoning across long documents

    • Llama 3.3 70B: 15.7%
    • GPT 4.1 Nano: 20.3%Top

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Llama 3.3 70B: 3.6%
    • GPT 4.1 Nano: 3.8%Top

  • GPQA Diamond

    Graduate-level science questions

    • Llama 3.3 70B: 49.8%
    • GPT 4.1 Nano: 51.2%Top

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Llama 3.3 70B: 71.3%Top
    • GPT 4.1 Nano: 65.7%

  • IFBench

    Following precise output instructions

    • Llama 3.3 70B: 47.1%Top
    • GPT 4.1 Nano: 32.0%

Coding

  • LiveCodeBench

    Recent competitive programming problems

    • Llama 3.3 70B: 28.8%
    • GPT 4.1 Nano: 32.6%Top

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Llama 3.3 70B: 7.7%
    • GPT 4.1 Nano: 24.0%Top

  • AIME

    Competition mathematics

    • Llama 3.3 70B: 30.0%Top
    • GPT 4.1 Nano: 23.7%

  • MATH-500

    Competition math problems

    • Llama 3.3 70B: 77.3%
    • GPT 4.1 Nano: 84.8%Top

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Llama 3.3 70B: 3.0%
    • GPT 4.1 Nano: 3.8%Top

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Llama 3.3 70B: 26.6%Top
    • GPT 4.1 Nano: 17.3%

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Llama 3.3 70B: 15.7%
    • GPT 4.1 Nano: 20.3%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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