about
The 70B variant debuted on ChatBot Arena with an ELO of roughly 1207, placing it between GPT-4-0613 and GPT-4 Turbo as the first open-weight model to compete directly with GPT-4 on human preference rankings. It scores 81.7% on HumanEval, surpassing GPT-4-0613 on code generation, and 82.0% on MMLU across 80 transformer layers.
Licensellama3
Context window(in thousands)8192
Use cases for Llama 3 Instruct 70B
- Open-weight GPT-4 alternative: With an ELO of 1207 on Chatbot Arena and 82.0% on MMLU, it provides GPT-4-competitive quality in environments requiring self-hosted inference and full weight access.
- High-accuracy code generation: Scoring 81.7% on HumanEval, it surpasses GPT-4-0613 on code tasks, making it suited for automated programming pipelines running on private infrastructure.
- Enterprise-scale reasoning: At 70B parameters with DPO alignment, it handles complex multi-turn advisory workflows in legal, financial, and technical domains without sending data to external APIs.
Quality
Arena Elo1206
MMLU82
MT BenchN/A
Llama 3 70B Instruct scores 82.0% on MMLU (5-shot) and 81.7% on HumanEval, placing it between GPT-4 (86.4% MMLU) and Llama 2 70B Chat (68.9% MMLU) on the same sheet. On code generation it surpasses GPT-4 (67.0% HumanEval), making it the first open-weight model to beat a GPT-4 variant on a major benchmark.
pricing
The cost per 1,000 tokens for utilizing the model with Telnyx Inference stands at $0.0010. To provide a perspective, analyzing 1,000,000 customer chats, presuming each chat is 1,000 tokens long, would cost $1,000.
What's Twitter saying?
- Developers praise Llama 3.3 70B Instruct for exceptional instruction following, scoring 99.2% on benchmarks and outperforming Llama 3.1 405B in tests on IBM WatsonX.ai.
- Benchmarks show Llama 3 70B excels in Python coding (15% better than GPT-4), grade school math, and cost-efficiency (up to 50x cheaper, 10x faster), but lags in complex reasoning.
- Community notes Llama 3 70B Instruct is highly helpful with fewer false refusals than Llama 2, optimized via SFT and RLHF for dialogue and outperforming open-source peers on MMLU (82.0).
Explore Our LLM Library
Discover the power and diversity of large language models available with Telnyx. Explore the options below to find the perfect model for your project.
| Organization | Model Name | Tasks | Languages Supported | Context Length | Parameters | Model Tier | License |
|---|---|---|---|---|---|---|---|
| deepseek-ai | DeepSeek-R1-Distill-Qwen-14B | text generation | English | 43,000 | 14.8B | medium | deepseek |
| fixie-ai | ultravox-v0_4_1-llama-3_1-8b | audio text-to-text | Multilingual | 8,000 | 8.7B | small | mit |
| gemma-2b-it | text generation | English | 8,192 | 2.5B | small | gemma | |
| gemma-7b-it | text generation | English | 8,192 | 8.5B | small | gemma | |
| meta-llama | Llama-3.3-70B-Instruct | text generation | Multilingual | 99,000 | 70.6B | large | llama3.3 |
| meta-llama | Llama-Guard-3-1B | safety classification | Multilingual | 128,000 | 1.5B | small | llama3.3 |
| meta-llama | Meta-Llama-3.1-70B-Instruct | text generation | Multilingual | 99,000 | 70.6B | large | llama3.1 |
| meta-llama | Meta-Llama-3.1-8B-Instruct | text generation | Multilingual | 131,072 | 8.0B | small | llama3.1 |
| minimaxai | MiniMax-M2.5 | text generation | English | 2,000,000 | 0 | large | minimaxai |
| minimaxai | MiniMax-M2.7 | text generation | English | 200,000 | 0 | large | minimaxai |
| mistralai | Mistral-7B-Instruct-v0.1 | text generation | English | 8,192 | 7.2B | small | apache-2.0 |
| mistralai | Mistral-7B-Instruct-v0.2 | text generation | English | 32,768 | 7.2B | small | apache-2.0 |
| mistralai | Mixtral-8x7B-Instruct-v0.1 | text generation | Multilingual | 32,768 | 46.7B | medium | apache-2.0 |
| moonshotai | Kimi-K2.5 | text generation | English | 256,000 | 1.0T | large | modified-mit |
| Qwen | Qwen3-235B-A22B | text generation | English | 32,768 | 235.1B | large | apache-2.0 |
| zai-org | GLM-5.1-FP8 | text generation | English | 202,752 | 753.9B | large | mit |
| anthropic | claude-3-7-sonnet-latest | text generation | Multilingual | 200,000 | 0 | large | anthropic |
| anthropic | claude-haiku-4-5 | text generation | Multilingual | 200,000 | 0 | large | anthropic |
| anthropic | claude-opus-4-6 | text generation | Multilingual | 200,000 | 0 | large | anthropic |
| anthropic | claude-sonnet-4-20250514 | text generation | Multilingual | 200,000 | 0 | large | anthropic |
| gemini-2.0-flash | text generation | Multilingual | 1,048,576 | 0 | large | ||
| gemini-2.5-flash | text generation | Multilingual | 1,048,576 | 0 | large | ||
| gemini-2.5-flash-lite | text generation | Multilingual | 1,048,576 | 0 | large | ||
| groq | gpt-oss-120b | text generation | English | 131,072 | 117.0B | large | groq |
| groq | kimi-k2-instruct | text generation | English | 131,072 | 1.0T | large | groq |
| groq | llama-3.3-70b-versatile | text generation | Multilingual | 131,072 | 70.6B | large | llama3.3 |
| groq | llama-4-maverick-17b-128e-instruct | text generation | Multilingual | 1,000,000 | 400.0B | large | llama4 |
| groq | llama-4-scout-17b-16e-instruct | text generation | Multilingual | 128,000 | 109.0B | large | llama4 |
| openai | gpt-3.5-turbo | text generation | Multilingual | 4,096 | 0 | large | openai |
| openai | gpt-4 | text generation | Multilingual | 128,000 | 0 | large | openai |
| openai | gpt-4-0125-preview | text generation | Multilingual | 128,000 | 0 | large | openai |
| openai | gpt-4-0314 | text generation | Multilingual | 128,000 | 0 | large | openai |
| openai | gpt-4-0613 | text generation | Multilingual | 128,000 | 0 | large | openai |
| openai | gpt-4-1106-preview | text generation | Multilingual | 128,000 | 0 | large | openai |
| openai | gpt-4-32k-0314 | text generation | Multilingual | 128,000 | 0 | large | openai |
| openai | gpt-4-turbo-preview | text generation | Multilingual | 128,000 | 0 | large | openai |
| openai | gpt-4.1 | text generation | Multilingual | 1,047,576 | 0 | large | openai |
| openai | gpt-4.1-mini | text generation | Multilingual | 1,047,576 | 0 | large | openai |
| openai | gpt-4o | text generation | Multilingual | 128,000 | 0 | large | openai |
| openai | gpt-4o-mini | text generation | Multilingual | 128,000 | 0 | large | openai |
| openai | gpt-5 | text generation | Multilingual | 400,000 | 0 | large | openai |
| openai | gpt-5-mini | text generation | Multilingual | 400,000 | 0 | large | openai |
| openai | gpt-5.1 | text generation | Multilingual | 400,000 | 0 | large | openai |
| openai | gpt-5.2 | text generation | Multilingual | 400,000 | 0 | large | openai |
| openai | o1-mini | text generation | Multilingual | 128,000 | 0 | large | openai |
| openai | o1-preview | text generation | Multilingual | 128,000 | 0 | large | openai |
| openai | o3-mini | text generation | Multilingual | 200,000 | 0 | large | openai |
| xai-org | grok-2 | text generation | Multilingual | 131,072 | 0 | large | xai |
| xai-org | grok-2-latest | text generation | Multilingual | 131,072 | 0 | large | xai |
| xai-org | grok-3 | text generation | Multilingual | 131,072 | 0 | large | xai |
| xai-org | grok-3-beta | text generation | Multilingual | 131,072 | 0 | large | xai |
| xai-org | grok-3-fast | text generation | Multilingual | 131,072 | 0 | large | xai |
| xai-org | grok-3-fast-beta | text generation | Multilingual | 131,072 | 0 | large | xai |
| xai-org | grok-3-fast-latest | text generation | Multilingual | 131,072 | 0 | large | xai |
| xai-org | grok-3-latest | text generation | Multilingual | 131,072 | 0 | large | xai |
| xai-org | grok-3-mini | text generation | Multilingual | 131,072 | 0 | large | xai |
| xai-org | grok-3-mini-fast | text generation | Multilingual | 131,072 | 0 | large | xai |
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faqs
What is Llama-3-70B-Instruct?
Llama-3-70B-Instruct is part of the Meta Llama 3 family, a large language model with 70 billion parameters designed for various tasks, including dialogue. It features a decoder-only transformer architecture and is pretrained on a dataset of over 15 trillion tokens for superior performance in multilingual support, efficiency, and versatility in tasks like coding, trivia, and creative writing.
How does Llama-3-70B-Instruct compare to GPT models?
Llama-3-70B-Instruct is reported to be comparable to GPT-4 in performance, excelling in areas such as email chain summarization and coding. It establishes a new state-of-the-art for large language models, outperforming other open-source chat models on common benchmarks.
What makes Llama-3-70B-Instruct efficient?
The model uses Grouped-Query Attention (GQA) across both its 8B and 70B versions, which ensures improved inference efficiency and scalability. This technique optimizes the model for faster and more efficient performance during tasks.
Can Llama-3-70B-Instruct handle tasks in languages other than English?
Yes, while currently optimized for English, Llama-3-70B-Instruct includes a significant amount of non-English data in its pretraining dataset. This makes it versatile for multilingual use cases and increases its future potential for global applications.
How is Llama-3-70B-Instruct fine-tuned for better performance?
Llama-3-70B-Instruct undergoes Supervised Fine-tuning (SFT) and Reinforcement Learning with Human Feedback (RLHF) to align more closely with human preferences for helpfulness and safety. This process ensures that the model is better suited to provide valuable and safe interactions.
What are some use cases for Llama-3-70B-Instruct?
Llama-3-70B-Instruct is designed to perform well across a variety of tasks, including trivia questions, STEM fields, coding, historical knowledge, and creative writing. Its versatility makes it suitable for a wide range of applications in different industries.
How does community feedback influence Llama-3-70B-Instruct development?
The development team behind Llama-3-70B-Instruct values community feedback highly, using it to refine the model's performance and safety over time. Future versions of the tuned models will be released as improvements are made based on this feedback.
Where can I use Llama-3-70B-Instruct for my projects?
Users can integrate Llama-3-70B-Instruct into their connectivity apps through platforms like Telnyx. This allows developers to leverage the model's capabilities for a wide range of applications, from customer service chatbots to more complex AI-driven solutions. For more information on how to start building with Llama-3-70B-Instruct on Telnyx, visit Telnyx's developer documentation.
Is Llama-3-70B-Instruct suitable for creative writing and content generation?
Yes, Llama-3-70B-Instruct excels in creative writing and content generation tasks. Its large dataset and sophisticated architecture allow it to generate high-quality, creative text outputs, making it a valuable tool for writers, marketers, and content creators.