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Llama 3.3 70b Instruct

Added Feb 27, 2025Open weights

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Use in the app

Llama 3.3 is optimized for multilingual dialogue use cases and outperforms many of the available open source and closed chat models on common industry benchmarks.

  • Tools
  • Structured output
Input
Text
Output
Text
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$0.05
Output
$0.23

Reference prices as published by the model provider. API example

Specifications

Context window
131.1K
Max output
16.4K
Average output (7 days)
37 tokens
Longer than 2% of 465 models

Side by side

Llama 3.3 70b Instruct 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 70b InstructMetaThis modelMuse Spark 1.3MetaRemove
ProviderMetaMeta
Discovery tierOpen weightsFrontier
AddedFeb 27, 2025Sep 2, 2026
Limits
Context window131.07K1.05MBest
Max output16.38K—
Average output37 tokens1.2K tokens
Pricing, USD per 1M tokens
Input$0.05Best$1.25
Output$0.23Best$4.25
Cache read—$0.15
Performance
Throughput11.1 tok/s323.3 tok/sBest
Time to first token3.7 sBest8.8 s
Request success (24h)99.9%100.0%Best
Benchmark indexes
Intelligence Index7.748.1Best
Coding Index11.975.8Best
Math Index7.7—
Capabilities
ReasoningNoYes
ToolsYesYes
VisionNoYes
Structured outputYesYes
PDF inputNoYes
Audio inputNoYes
Video inputNoYes
Reasoning effort—minimal, 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 Instruct
  • Muse Spark 1.3

Intelligence Index

  • Llama 3.3 70b Instruct7.7
  • Muse Spark 1.348.1

Llama 3.3 70b Instruct: 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 Instruct: 11.9
    • Muse Spark 1.3: 75.8Top

  • Math Index

    Composite of the math evaluations

    • Llama 3.3 70b Instruct: 7.7
    • Muse Spark 1.3: —

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Llama 3.3 70b Instruct: —
    • Muse Spark 1.3: 57.9%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Llama 3.3 70b Instruct: —
    • Muse Spark 1.3: 29.8%

  • Harvey LAB-AAv1.1

    Legal tasks passed in full, with no material hallucination

    • Llama 3.3 70b Instruct: —
    • Muse Spark 1.3: 8.9%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Llama 3.3 70b Instruct: —
    • Muse Spark 1.3: 1,581 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Llama 3.3 70b Instruct: —
    • Muse Spark 1.3: 1,681 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Llama 3.3 70b Instruct: —
    • Muse Spark 1.3: 26.6%

  • AA-LCRv1.1

    Reasoning across long documents

    • Llama 3.3 70b Instruct: 15.7%
    • Muse Spark 1.3: 83.0%Top

  • MLCR-AA

    Long-context reasoning over medical records

    • Llama 3.3 70b Instruct: —
    • Muse Spark 1.3: 13.9%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Llama 3.3 70b Instruct: 3.6%
    • Muse Spark 1.3: 48.7%Top

  • GPQA Diamond

    Graduate-level science questions

    • Llama 3.3 70b Instruct: 49.8%
    • Muse Spark 1.3: 93.5%Top

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Llama 3.3 70b Instruct: 71.3%
    • Muse Spark 1.3: —

  • IFBench

    Following precise output instructions

    • Llama 3.3 70b Instruct: 47.1%
    • Muse Spark 1.3: —

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Llama 3.3 70b Instruct: —
    • Muse Spark 1.3: 33.3%

  • SciCode

    Scientific computing in Python

    • Llama 3.3 70b Instruct: —
    • Muse Spark 1.3: 58.8%

  • LiveCodeBench

    Recent competitive programming problems

    • Llama 3.3 70b Instruct: 28.8%
    • Muse Spark 1.3: —

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Llama 3.3 70b Instruct: 7.7%
    • Muse Spark 1.3: —

  • AIME

    Competition mathematics

    • Llama 3.3 70b Instruct: 30.0%
    • Muse Spark 1.3: —

  • MATH-500

    Competition math problems

    • Llama 3.3 70b Instruct: 77.3%
    • Muse Spark 1.3: —

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Llama 3.3 70b Instruct: 3.0%
    • Muse Spark 1.3: —

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Llama 3.3 70b Instruct: 26.6%
    • Muse Spark 1.3: —

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Llama 3.3 70b Instruct: 15.7%
    • Muse Spark 1.3: 83.0%Top

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
11.1 tok/s
Faster than 1% of 247 models
Time to first token
3.7 s
Quicker than 46% of 247 models
Prompt cache hits
0.0%
Request success (24h)
99.9%
Reliable

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.

Hourly request success · last 72 hoursLowest hour 93.0%
  • Healthy (98% or more)
  • Degraded (90–98%)
  • Down (under 90%)
  • No requests

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": "meta-llama/llama-3.3-70b-instruct",
    "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="meta-llama/llama-3.3-70b-instruct",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Meta

Other Meta models, starting with the same family.