--- title: Llama 3 Instruct (70B)—Superior language reasoning for open source canonical: https://telnyx.com/llm-library/llama-3-70b --- # Llama 3 Instruct (70B)—Superior language reasoning for open source [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 Instruct (70B)—Superior language reasoning for open source # Llama 3 Instruct 70B Meta's 70B-parameter Llama 3 model, instruction-tuned for dialogue and code generation with strong benchmark results across reasoning and language tasks. [Start building](https://telnyx.com/sign-up)[GET Available Models](https://developers.telnyx.com/docs/inference/models) ## 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](https://ai.meta.com/blog/meta-llama-3/) 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 ## 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 ### NVIDIA-NemotronLabs-VoiceChat-11B ### Laguna-S-2.1 ### 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-6-luna ### 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 Instruct 70B In the [AI Playground](https://portal.telnyx.com/#/app/next/ai/ai-playground "Telnyx AI Playground") select Llama 3 Instruct 70B 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 ### Is Llama 3.1 70B Instruct free? Llama 3 70B Instruct is released under Meta's community license, which permits free use for commercial applications. Weights are available on [Hugging Face](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct) and through hosted inference providers. ### What is Llama 3.1 70B Instruct? Llama 3 70B Instruct is Meta's instruction-tuned 70 billion parameter model, optimized for dialogue, code generation, and complex reasoning. It is available through and other hosted providers. ## Use cases for Llama 3 Instruct 70B 1. **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. 2. **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. 3. **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. **Llama 3.1 70B Instruct** 1248 **GPT-4 0125 Preview** 1245 **Llama 3 Instruct 70B** 1206 **GPT-4 0314** 1186 **GPT-4** 1165 ## 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). | 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 | | nvidia | NVIDIA-NemotronLabs-VoiceChat-11B | speech to-speech | English | 8,192 | 11.1B | medium | openmdw-1.1 | | poolside | Laguna-S-2.1 | text generation | English | 1,048,576 | 117.6B | unlisted | OpenMDW-1.1 | | 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-6-luna | text generation | Multilingual | 1,050,000 | 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) Telnyx's inference platform ### What is the difference between Llama normal and instruct? The base Llama model is trained for next-token prediction, while the Instruct variant is fine-tuned with RLHF for following instructions and dialogue. The [Instruct version](https://telnyx.com/resources/llama-3-1-70b-instruct) is what most applications should use for chat and task completion. ### What is Llama 3 Instruct? Llama 3 Instruct refers to Meta's family of instruction-tuned models available in 8B and 70B sizes. The 70B variant delivers strong reasoning and coding performance, competitive with [proprietary models](https://telnyx.com/llm-library/llama-3-70b) at a fraction of the cost. ### How does Llama 3 70B compare to GPT-4? Llama 3 70B approaches GPT-4-level performance on many benchmarks, particularly coding and multilingual tasks. For production deployments, it offers a [self-hostable alternative](https://telnyx.com/resources/llama-3-70b-overview) to proprietary API-only models. ### What GPU do you need for Llama 3 70B? Running Llama 3 70B at full precision requires approximately 140GB of VRAM, typically two A100 80GB GPUs. With quantization, a single 48GB GPU can handle it, or you can use [hosted inference](https://telnyx.com/llm-library/llama-3-70b) to avoid GPU provisioning entirely.