Meta: Llama 3.3 70B Instruct

Text input Text output Free Option
Author's Description

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](https://github.com/meta-llama/llama-models/blob/main/models/llama3_3/MODEL_CARD.md)

Key Specifications
Cost
$
Context
131K
Parameters
70B
Released
Dec 06, 2024
Speed
Ability
Reliability
Supported Parameters

This model supports the following parameters:

Tools Tool Choice Response Format Stop Seed Min P Top P Max Tokens Frequency Penalty Temperature Presence Penalty
Features

This model supports the following features:

Tools Response Format
Performance Summary

Meta's Llama 3.3 70B Instruct, released December 6, 2024, demonstrates a strong overall performance profile, particularly excelling in cost-efficiency and reliability. The model consistently offers competitive pricing, ranking in the 84th percentile across benchmarks, making it a cost-effective choice. Its reliability is also a significant strength, with a 92% success rate indicating consistent and usable responses. In terms of speed, the model shows competitive response times, placing in the 58th percentile. Analyzing benchmark results, Llama 3.3 70B Instruct exhibits perfect accuracy in Instruction Following (one instance) and Email Classification, often achieving these results with impressive speed and cost-efficiency. It also performs well in Hallucinations (96.0% accuracy) and Ethics (99.0% accuracy), suggesting a robust understanding of ethical principles and an ability to acknowledge uncertainty. General Knowledge is strong at 98.0% accuracy. However, the model shows notable weaknesses in more complex domains such as Mathematics (64.0% accuracy, 30th percentile) and Coding (37.0% accuracy, 20th percentile), indicating areas for potential improvement in advanced problem-solving and programming tasks. Reasoning also presents a moderate challenge at 58.0% accuracy. Its multilingual support for English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai further enhances its utility for diverse applications.

Model Pricing

Current Pricing

Feature Price (per 1M tokens)
Prompt $0.13
Completion $0.39

Price History

Available Endpoints
Provider Endpoint Name Context Length Pricing (Input) Pricing (Output)
DeepInfra
DeepInfra | meta-llama/llama-3.3-70b-instruct 131K $0.13 / 1M tokens $0.39 / 1M tokens
Kluster
Kluster | meta-llama/llama-3.3-70b-instruct 131K $0.04 / 1M tokens $0.12 / 1M tokens
Lambda
Lambda | meta-llama/llama-3.3-70b-instruct 131K $0.04 / 1M tokens $0.12 / 1M tokens
Phala
Phala | meta-llama/llama-3.3-70b-instruct 131K $0.04 / 1M tokens $0.12 / 1M tokens
Novita
Novita | meta-llama/llama-3.3-70b-instruct 131K $0.13 / 1M tokens $0.39 / 1M tokens
Crusoe
Crusoe | meta-llama/llama-3.3-70b-instruct 131K $0.04 / 1M tokens $0.12 / 1M tokens
Nebius
Nebius | meta-llama/llama-3.3-70b-instruct 131K $0.13 / 1M tokens $0.4 / 1M tokens
DeepInfra
DeepInfra | meta-llama/llama-3.3-70b-instruct 131K $0.23 / 1M tokens $0.4 / 1M tokens
Parasail
Parasail | meta-llama/llama-3.3-70b-instruct 131K $0.15 / 1M tokens $0.5 / 1M tokens
NextBit
NextBit | meta-llama/llama-3.3-70b-instruct 32K $0.04 / 1M tokens $0.12 / 1M tokens
Cloudflare
Cloudflare | meta-llama/llama-3.3-70b-instruct 24K $0.29 / 1M tokens $2.25 / 1M tokens
Cent-ML
Cent-ML | meta-llama/llama-3.3-70b-instruct 131K $0.04 / 1M tokens $0.12 / 1M tokens
InoCloud
InoCloud | meta-llama/llama-3.3-70b-instruct 131K $0.04 / 1M tokens $0.12 / 1M tokens
Hyperbolic
Hyperbolic | meta-llama/llama-3.3-70b-instruct 131K $0.4 / 1M tokens $0.4 / 1M tokens
Atoma
Atoma | meta-llama/llama-3.3-70b-instruct 104K $0.04 / 1M tokens $0.12 / 1M tokens
Groq
Groq | meta-llama/llama-3.3-70b-instruct 131K $0.59 / 1M tokens $0.79 / 1M tokens
Friendli
Friendli | meta-llama/llama-3.3-70b-instruct 131K $0.6 / 1M tokens $0.6 / 1M tokens
SambaNova
SambaNova | meta-llama/llama-3.3-70b-instruct 131K $0.6 / 1M tokens $1.2 / 1M tokens
Google
Google | meta-llama/llama-3.3-70b-instruct 128K $0.72 / 1M tokens $0.72 / 1M tokens
Cerebras
Cerebras | meta-llama/llama-3.3-70b-instruct 131K $0.85 / 1M tokens $1.2 / 1M tokens
Together
Together | meta-llama/llama-3.3-70b-instruct 131K $0.88 / 1M tokens $0.88 / 1M tokens
Fireworks
Fireworks | meta-llama/llama-3.3-70b-instruct 131K $0.9 / 1M tokens $0.9 / 1M tokens
InferenceNet
InferenceNet | meta-llama/llama-3.3-70b-instruct 128K $0.04 / 1M tokens $0.12 / 1M tokens
Crusoe
Crusoe | meta-llama/llama-3.3-70b-instruct 131K $0.04 / 1M tokens $0.12 / 1M tokens
GMICloud
GMICloud | meta-llama/llama-3.3-70b-instruct 131K $0.25 / 1M tokens $0.75 / 1M tokens
WandB
WandB | meta-llama/llama-3.3-70b-instruct 128K $0.04 / 1M tokens $0.12 / 1M tokens
SambaNova
SambaNova | meta-llama/llama-3.3-70b-instruct 16K $0.45 / 1M tokens $0.9 / 1M tokens
Benchmark Results
Benchmark Category Reasoning Strategy Free Executions Accuracy Cost Duration
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