--- title: Llama 3.1 70B Instruct—LLM Evaluation by Telnyx canonical: https://telnyx.com/llm-library/meta-llama-3-1-70b-instruct --- # Llama 3.1 70B Instruct—LLM Evaluation by Telnyx [Start building](https://telnyx.com/sign-up) ### Ask AI * [![GPT](https://d2efw2akbx8gaf.cloudfront.net/https://dotcom-payload-assets-prod.s3.us-east-1.amazonaws.com/cms-media/chatgpt_icon-ea55e09d.jpeg?width=92&format=webp)](https://chat.openai.com/?q=What makes Telnyx's global network different, breakdown call quality for normal phone calls and voice AI calls.) * [![Claude](https://d2efw2akbx8gaf.cloudfront.net/https://dotcom-payload-assets-prod.s3.us-east-1.amazonaws.com/cms-media/claude_icon-c7c8eef3.webp?width=92&format=webp)](https://claude.ai/new?q=What makes Telnyx's global network different, breakdown call quality for normal phone calls and voice AI calls.) * [![Perplexity](https://d2efw2akbx8gaf.cloudfront.net/https://dotcom-payload-assets-prod.s3.us-east-1.amazonaws.com/cms-media/perplexity_icon-cd5b74f1.webp?width=92&format=webp)](https://www.perplexity.ai/search?q=What makes Telnyx's global network different, breakdown call quality for normal phone calls and voice AI calls.) * [![Gemini](https://d2efw2akbx8gaf.cloudfront.net/https://dotcom-payload-assets-prod.s3.us-east-1.amazonaws.com/cms-media/gemini_icon-4ff1cbf4.webp?width=92&format=webp)](https://www.google.com/search?udm=50&aep=11&q=What makes Telnyx's global network different, breakdown call quality for normal phone calls and voice AI calls.) * [![Grok](https://d2efw2akbx8gaf.cloudfront.net/https://dotcom-payload-assets-prod.s3.us-east-1.amazonaws.com/cms-media/grok_icon-e588e5bd.webp?width=92&format=webp)](https://x.com/i/grok?text=What makes Telnyx's global network different, breakdown call quality for normal phone calls and voice AI calls.) Llama 3.1 70B Instruct—LLM Evaluation by Telnyx # Llama 3.1 70B Instruct Meta's 70B Llama 3.1 model with a 128k context window, optimized for multilingual dialogue, code generation, and complex reasoning across eight languages. [Start building](https://telnyx.com/sign-up)[GET Available Models](https://developers.telnyx.com/docs/inference/models) ## 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](https://ai.meta.com/blog/meta-llama-3-1/). 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) ## 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. ### DeepSeek-V4-Flash-0731 OrganizationTelnyx Model NameDeepSeek-V4-Flash-0731 Taskstext generation Languages SupportedEnglish Context Length1,048,576 Parameters284.0B Model Tierunlisted Licensemit ### DeepSeek-V4.1-Flash ### gemma-2b-it ### gemma-4-26B-A4B-it ### gemma-4-31B-it ### Llama-3.3-70B-Instruct ### Meta-Llama-3.1-70B-Instruct ### Meta-Llama-3.1-8B-Instruct ### MiniMax-M2.7 ### MiniMax-M3-MXFP8 ### Kimi-K2.5 ### Kimi-K2.6 ### Kimi-K3 ### Qwen3-235B-A22B ### Qwen3.8-27B ### GLM-5.1-FP8 ### GLM-5.2 ### GLM-5.3 ### GLM-5.3-Flash ### claude-haiku-4-5 ### gemini-2.5-flash ### gemini-3.7-flash ### gpt-oss-120b ### gpt-4.1 ### gpt-4o ### gpt-4o-mini ### gpt-5 ### gpt-5.1 ### gpt-5.2 ### gpt-5.4 ### gpt-5.4-mini ### gpt-5.6-luna ### gpt-5.6-sol ### gpt-live-1 HOW IT WORKS ## Selecting LLMs for Voice AI [GET Available Models](https://developers.telnyx.com/docs/inference/models) ### Create an account Sign-up to the Telnyx platform to get access to our suite of intuitive AI tools. ### Choose Llama 3.1 70B Instruct In the [AI Playground](https://portal.telnyx.com/#/app/next/ai/ai-playground "Telnyx AI Playground") select Llama 3.1 70B Instruct from the dropdown. ### Enter your API key If you’re using a proprietary model, you’ll need your API key before prompting the LLM. Our vast selection of OS models allow you to get started right away. ### Prompt the LLM Get responses from an LLM in minutes, and scale with our [OpenAI-compatible APIs](https://developers.telnyx.com/docs/inference/getting-started "Inference API Quickstart"). Step 1/4 **RESOURCES** ## Get started Check out our helpful tools to help get you started. * ### Test in the portal Easily browse and select your preferred model in the AI Playground. [Test today](https://portal.telnyx.com/#/ai/assistants) ## Sign up and start building [Sign up](https://telnyx.com/sign-up)[Contact sales](https://telnyx.com/contact-us) ## 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](https://telnyx.com/llm-library/meta-llama-3-1-70b-instruct) 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](https://vercel.com/ai-gateway/models/llama-3.1-70b) or accessed through hosted inference providers at per-token rates. 131072 ## Use cases for Llama 3.1 70B Instruct 1. **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. 2. **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. 3. **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. **GPT-4 1106 Preview** 1251 **Llama-4-Scout-Instruct** 1250 **Llama 3.1 70B Instruct** 1248 **GPT-4 0125 Preview** 1245 **Llama 3 Instruct 70B** 1206 ## 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. | Organization | Model Name | Tasks | Languages Supported | Context Length | Parameters | Model Tier | License | | ------------ | ----------------------------------------------------------------------- | ---------------- | ------------------- | -------------- | ---------- | ---------- | ----------------- | | Telnyx | DeepSeek-V4-Flash-0731 | text generation | English | 1,048,576 | 284.0B | unlisted | mit | | Telnyx | DeepSeek-V4.1-Flash | text generation | English | 1,048,576 | 552.0B | small | mit | | google | [gemma-2b-it](https://telnyx.com/llm-library/gemma-2b-it) | text generation | English | 8,192 | 2.5B | small | gemma | | google | gemma-4-26B-A4B-it | text to-text | Multilingual | 262,144 | 25.8B | small | apache-2.0 | | google | gemma-4-31B-it | text generation | Multilingual | 131,072 | 30.7B | small | apache-2.0 | | meta-llama | [Llama-3.3-70B-Instruct](https://telnyx.com/llm-library/llama-3.3-70b-instruct) | text generation | Multilingual | 99,000 | 70.6B | large | llama3.3 | | meta-llama | [Meta-Llama-3.1-70B-Instruct](https://telnyx.com/llm-library/meta-llama-3-1-70b-instruct) | text generation | Multilingual | 99,000 | 70.6B | large | llama3.1 | | meta-llama | [Meta-Llama-3.1-8B-Instruct](https://telnyx.com/llm-library/meta-llama-3.1-8b-instruct) | text generation | Multilingual | 131,072 | 8.0B | small | llama3.1 | | minimaxai | MiniMax-M2.7 | text generation | Multilingual | 200,000 | 0 | large | minimaxai | | minimaxai | MiniMax-M3-MXFP8 | text generation | Multilingual | 1,000,000 | 428.0B | small | minimax-community | | moonshotai | [Kimi-K2.5](https://telnyx.com/llm-library/kimi-k2.5) | text generation | Multilingual | 256,000 | 1.0T | large | modified-mit | | moonshotai | Kimi-K2.6 | text generation | Multilingual | 262,144 | 1.0T | medium | modified-mit | | moonshotai | Kimi-K3 | text generation | English | 1,000,000 | 2.8M | large | MIT Modified | | Qwen | [Qwen3-235B-A22B](https://telnyx.com/llm-library/qwen3-235b-a22b) | text generation | English | 32,768 | 235.1B | large | apache-2.0 | | Telnyx | Qwen3.8-27B | text generation | English | 262,144 | 27.0B | small | Apache 2.0 | | zai-org | GLM-5.1-FP8 | text generation | Multilingual | 202,752 | 753.9B | large | mit | | zai-org | GLM-5.2 | text generation | English | 1,000,000 | 753.9B | medium | MIT | | Telnyx | GLM-5.3 | text generation | English | 1,048,576 | 753.0B | medium | glm-5.3 | | zai-org | GLM-5.3-Flash | text generation | English | 1,048,576 | 320.0B | small | mit | | anthropic | [claude-haiku-4-5](https://telnyx.com/llm-library/claude-haiku-4-5) | text generation | Multilingual | 400,000 | 0 | large | openai | | google | [gemini-2.5-flash](https://telnyx.com/llm-library/gemini-2.5-flash) | text generation | Multilingual | 400,000 | 0 | large | openai | | google | gemini-3.7-flash | text generation | Multilingual | 400,000 | 0 | large | openai | | groq | [gpt-oss-120b](https://telnyx.com/llm-library/gpt-oss-120b) | text generation | Multilingual | 400,000 | 0 | large | openai | | openai | gpt-4.1 | text generation | Multilingual | 400,000 | 0 | large | openai | | openai | [gpt-4o](https://telnyx.com/llm-library/gpt-4-omni) | text generation | Multilingual | 400,000 | 0 | large | openai | | openai | [gpt-4o-mini](https://telnyx.com/llm-library/gpt-4o-mini) | text generation | Multilingual | 400,000 | 0 | large | openai | | openai | gpt-5 | text generation | Multilingual | 400,000 | 0 | large | openai | | openai | gpt-5.1 | text generation | Multilingual | 400,000 | 0 | large | openai | | openai | [gpt-5.2](https://telnyx.com/llm-library/gpt-5.2) | text generation | Multilingual | 400,000 | 0 | large | openai | | openai | gpt-5.4 | text generation | Multilingual | 400,000 | 0 | large | openai | | openai | gpt-5.4-mini | text generation | Multilingual | 400,000 | 0 | large | openai | | openai | gpt-5.6-luna | text generation | Multilingual | 400,000 | 0 | large | openai | | openai | gpt-5.6-sol | text generation | Multilingual | 400,000 | 0 | large | openai | | openai | gpt-live-1 | speech to-speech | Multilingual | 400,000 | 0 | large | openai | * ### Explore the docs Don’t wait to scale, start today with our public API endpoints. [Get started](https://developers.telnyx.com/api-reference/assistants/list-assistants) * ### Stay up to date Keep an eye on our AI changelog so you don't miss a beat. [See updates](https://telnyx.com/release-notes) ### 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](https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct) 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](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/llama-3%5F1-70b-instruct-nemo). 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](https://medium.com/gptalk/is-llama-3-1-really-open-source-73ac220f9aa2) alongside the model release.