about
The 3.1 update expanded the context window from 8K to 128K tokens using progressive RoPE frequency scaling, making it the first open model with strong long-document performance at this scale. It added native tool use for Brave Search, Wolfram Alpha, and a code interpreter, and shipped under a more permissive license that explicitly allowed using its outputs to train other models.
Licensellama3.1
Context window(in thousands)131072
Use cases for Llama 3.1 70B Instruct
- Long-context enterprise analysis: The 128K context window with strong needle-in-a-haystack performance processes full contracts, regulatory filings, and codebases in a single pass for the first time in an open-weight model.
- Distillation-source model: The Llama 3.1 license explicitly permits using outputs to train other models, making the 70B a validated teacher for distilling smaller domain-specific models.
- Native tool orchestration: Built-in support for Brave Search, Wolfram Alpha, and code interpreter enables multi-tool agentic workflows without custom function-calling implementations.
Quality
Arena Elo1248
MMLUN/A
MT BenchN/A
Llama 3.1 70B Instruct scores 86.0% on MMLU (0-shot CoT) and 73.0% on MMLU-Pro (5-shot), matching GPT-4 Turbo (86.5% MMLU) on the same sheet at a fraction of the cost. Compared to Llama 3 70B Instruct (82.0% MMLU), the 4-point improvement comes alongside a 16x context window expansion from 8K to 128K tokens with strong needle-in-a-haystack retrieval.
pricing
The cost of running Llama 3.1 70B Instruct with Telnyx Inference is $0.0006 per 1,000 tokens. Analyzing 1,000,000 customer chats at 1,000 tokens each would cost $600, delivering GPT-4 Turbo-class quality (86.0% MMLU) at a fraction of GPT-4 Turbo's API pricing ($10/$30 per million tokens).
What's Twitter saying?
- Developers praise Llama 3.1 70B Instruct for its advanced instruction-following accuracy and seamless integration, making it ideal for enterprise chatbots and data analysis.
- Tech commentator Christopher Penn highlights strong improvements in instruction following and reasoning over Llama 3.1 405B in benchmarks, though noting a slight dip in tool use.
- Databricks positions it as a balanced model excelling in speed and intelligence for workloads like agentic workflows and code generation.
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 3.1 70B Instruct?
Llama 3.1 70B Instruct is Meta's 70-billion-parameter model with a 128K context window, optimized for multilingual dialogue across eight languages. It was trained on approximately 15 trillion tokens and supports text generation, code, and complex reasoning tasks.
Is Llama 3.1 70B Instruct free?
Yes, Llama 3.1 70B is open-source and free for commercial use under Meta's license. It can be downloaded and self-hosted or accessed through hosted inference providers at per-token rates.
What do I need to run Llama 3.1 70B?
Running Llama 3.1 70B requires at minimum a GPU with 40+ GB VRAM for quantized inference, or multiple high-end GPUs for full-precision deployment. Cloud GPU instances on AWS, GCP, or specialized inference providers are popular deployment options.
What is the cutoff for Llama 3.1 70B knowledge?
Llama 3.1 70B has a training data cutoff of December 2023. It will not have knowledge of events, releases, or information published after that date.
Is Llama 3 completely free?
Llama 3.1 is released under Meta's community license which permits commercial use. While the model weights are free, some restrictions apply for very large-scale deployments. The license details are documented in Meta's terms alongside the model release.