Atlas / Models / Meta / Meta: Llama 3.3 70B Instruct

Meta: Llama 3.3 70B Instruct✓ Catalog verified

meta-llama/llama-3.3-70b-instruct

The Meta Llama 3.3 multilingual large language model (LLM) is a pretrained and instruction tuned generative model in 70B (text in/text out). The Llama 3.3 instruction tuned text only model is optimized for multilingual dialogue use cases and outperforms many of the available open source and closed chat models on common industry benchmarks. Supported languages: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai. Model Card

Input price
$0.10 /1M
Output price
$0.32 /1M
Context
131K
Modalities
text
Released
Dec 6, 2024
Tool calling
✓ Yes
Atlas signal
81/100
01

Overview

The Meta Llama 3.3 multilingual large language model (LLM) is a pretrained and instruction tuned generative model in 70B (text in/text out). The Llama 3.3 instruction tuned text only model is optimized for multilingual dialogue use cases and outperforms many of the available open source and closed chat models on common industry benchmarks. Supported languages: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai. Model Card

Access: available through the official Meta API. Context window: 131K.

Pricing and metadata from the ModelsAtlas catalog.Last refreshed Aug 5, 2026
02

Specifications

API identifiermeta-llama/llama-3.3-70b-instruct
ProviderMeta
Model typeText LLM
Context window131K catalog
Input modalitiestext
Output modalitiestext
ReleasedDec 6, 2024
TokenizerLlama3
Instruction formatllama3
ModeratedNo
Architecture modalitytext->text
Supported parameters
frequency_penaltylogit_biaslogprobsmax_tokensmin_ppresence_penaltyrepetition_penaltyresponse_formatseedstopstructured_outputstemperaturetool_choicetoolstop_ktop_logprobstop_p
Source: provider documentation + catalog feedMethodology →
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Pricing

Live pricing components for meta-llama/llama-3.3-70b-instruct as published in the catalog.

$0.10
Input /1M
$0.32
Output /1M
Per request
Per image
Pricing componentRaw unit priceNormalizedUnit
Prompt tokens$1e-7$0.10 / 1MPer input token
Completion tokens$3.2e-7$0.32 / 1MPer output token
Request feeN/AN/APer request
Image feeN/AN/APer image unit
Web search feeN/AN/APer search request
Source: catalog pricing feedCompare all pricing →Cheapest models →
04

Cost calculator

Estimate monthly spend from your own token volumes.

Scale / Volume

Estimated Total Cost

$0.11Calculated from current list pricing in the ModelsAtlas catalog.
05

Capabilities

CapabilityStatusWhat it means
Visual UnderstandingNot advertisedImage and document analysis support
Audio ProcessingNot advertisedSpeech and voice aligned flows
Tool Calling✓ SupportedSupports tools / function calling
Self-Hosting✓ SupportedDeploy outside managed APIs
Derived from catalog capability tags and supported parameters
06

Capability signals

Directional signals derived from model metadata and capability tags — not official benchmark submissions.

Meta: Llama 3.3 70B Instruct
MMLU Signal (Reasoning)
79signal
Coding Signal (HumanEval proxy)
88signal
Math Signal (GSM8K proxy)
79signal
Science Signal (GPQA proxy)
79signal
Estimated from metadata — not an official benchmark runAll benchmarks →Methodology →
07

Quick start

Call Meta: Llama 3.3 70B Instruct through an OpenAI-compatible client.

from openai import OpenAI

client = OpenAI(
    base_url="https://openrouter.ai/api/v1",
    api_key="YOUR_API_KEY"
)

response = client.chat.completions.create(
    model="meta-llama/llama-3.3-70b-instruct",
    messages=[{"role": "user", "content": "Explain quantum physics."}]
)

print(response.choices[0].message.content)
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Alternatives

Closest models by context window from a different provider.

09

Sources & attribution

Pricing and metadata are maintained in the ModelsAtlas catalog.

Capability bars use metadata tags — directional estimates onlyHow we source and verify data →
10

Frequently asked questions

How much does Meta: Llama 3.3 70B Instruct cost?

$0.10 per 1M input tokens and $0.32 per 1M output tokens on the official Meta API.

What is the context window of Meta: Llama 3.3 70B Instruct?

Meta: Llama 3.3 70B Instruct supports up to 131K tokens of context.

Does Meta: Llama 3.3 70B Instruct support tool / function calling?

Yes, Meta: Llama 3.3 70B Instruct supports tool / function calling — you can register tools and the model will emit structured tool calls.

How do I access Meta: Llama 3.3 70B Instruct?

Use the official Meta API with the model id `meta-llama/llama-3.3-70b-instruct`. See the Quick start section above for code examples.