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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 modelSolar Pro 3upstageRemove
ProviderMetaupstage
Discovery tierOpen weightsEfficient
AddedJul 3, 2025Mar 3, 2026
Limits
Context window128K131.07KBest
Max output16.38K—
Average output339 tokens—
Pricing, USD per 1M tokens
Input$1.75$0.15Best
Output$2.75$0.60Best
Cache read$1.75$0.015Best
Benchmark indexes
Intelligence Index7.77.8Best
Coding Index11.916.2Best
Math Index7.7—
Capabilities
Reasoning effort——

Public evaluations

Benchmarks

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

  • Llama 3.3 70B
  • Solar Pro 3

Intelligence Index

  • Llama 3.3 70B7.7
  • Solar Pro 37.8

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: 11.9
    • Solar Pro 3: 16.2Top

  • Math Index

    Composite of the math evaluations

    • Llama 3.3 70B: 7.7
    • Solar Pro 3: —

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Llama 3.3 70B: —
    • Solar Pro 3: 0.5%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Llama 3.3 70B: —
    • Solar Pro 3: 0.0%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Llama 3.3 70B: —
    • Solar Pro 3: 99 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Llama 3.3 70B: —
    • Solar Pro 3: 264 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Llama 3.3 70B: —
    • Solar Pro 3: 1.6%

  • AA-LCRv1.1

    Reasoning across long documents

    • Llama 3.3 70B: 15.7%
    • Solar Pro 3: 32.3%Top

  • MLCR-AA

    Long-context reasoning over medical records

    • Llama 3.3 70B: —
    • Solar Pro 3: 0.6%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Llama 3.3 70B: 3.6%
    • Solar Pro 3: 10.3%Top

  • GPQA Diamond

    Graduate-level science questions

    • Llama 3.3 70B: 49.8%
    • Solar Pro 3: 72.4%Top

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Llama 3.3 70B: 71.3%
    • Solar Pro 3: —

  • IFBench

    Following precise output instructions

    • Llama 3.3 70B: 47.1%
    • Solar Pro 3: 71.2%Top

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Llama 3.3 70B: —
    • Solar Pro 3: 0.0%

  • SciCode

    Scientific computing in Python

    • Llama 3.3 70B: —
    • Solar Pro 3: 25.5%

  • LiveCodeBench

    Recent competitive programming problems

    • Llama 3.3 70B: 28.8%
    • Solar Pro 3: —

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Llama 3.3 70B: 7.7%
    • Solar Pro 3: —

  • AIME

    Competition mathematics

    • Llama 3.3 70B: 30.0%
    • Solar Pro 3: —

  • MATH-500

    Competition math problems

    • Llama 3.3 70B: 77.3%
    • Solar Pro 3: —

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Llama 3.3 70B: 3.0%
    • Solar Pro 3: 7.6%Top

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Llama 3.3 70B: 26.6%
    • Solar Pro 3: 86.3%Top

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Llama 3.3 70B: 15.7%
    • Solar Pro 3: 32.3%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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