--- title: "Llama 3.3 70B Instruct: Advanced Open-Source LLM" canonical: https://telnyx.com/llm-library/llama-3.3-70b-instruct --- # Llama 3.3 70B Instruct: Advanced Open-Source LLM [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-3.3-70B-Instruct Meta's 70B Llama 3.3 model delivering 405B-class performance in coding, reasoning, and instruction-following with a 128k context window across eight languages. [Start building](https://telnyx.com/sign-up)[GET Available Models](https://developers.telnyx.com/docs/inference/models) ## about The December 2024 release scores [92.1 on IFEval](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct), surpassing both Llama 3.1 405B at 88.6 and GPT-4o at 84.6 on the same instruction-following benchmark despite being roughly 6x smaller than the 405B. It hits 88.4% on HumanEval for code generation and runs at 276 tokens per second on Groq, making the 405B largely redundant for most production workloads. Licensellama 3.3 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 ### 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-3.3-70B-Instruct In the [AI Playground](https://portal.telnyx.com/#/app/next/ai/ai-playground "Telnyx AI Playground") select Llama-3.3-70B-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 3.3 70B Instruct? Llama 3.3 70B Instruct is Meta's latest instruction-tuned 70B model, achieving performance on par with the much larger Llama 3.1 405B on many tasks. It supports a [128K context window](https://telnyx.com/llm-library/llama-3-70b) and excels at coding, reasoning, and multilingual generation. ### Is Llama 3.3 70B Instruct free? Yes, Llama 3.3 70B Instruct is released under Meta's community license for free commercial use. Weights are available on [Hugging Face](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct) and the model can be accessed through various hosted inference providers. 99,000 ## Use cases for Llama-3.3-70B-Instruct 1. **405B-class instruction following:** Scoring 92.1 on IFEval, higher than both Llama 3.1 405B and GPT-4o, it handles complex multi-constraint instructions that require precise adherence to formatting, tone, and content rules. 2. **Multilingual code generation:** With 88.4% on HumanEval across 8 supported languages, it generates and explains code in non-English developer contexts without quality loss. 3. **Self-hosted enterprise deployment:** Delivering 405B-quality at 70B compute requirements, it runs on standard GPU infrastructure for organizations that need frontier performance without external API dependencies. ## Quality Arena Elo1318 MMLU86 MT BenchN/A Llama 3.3 70B Instruct scores 86.0% on MMLU (0-shot CoT) and 88.4% on HumanEval, matching GPT-4 Turbo (86.5% MMLU) on general knowledge while significantly exceeding it on code. Its IFEval score of 92.1 surpasses both Llama 3.1 405B (88.6) and GPT-4o (84.6), making it the strongest instruction-following model at the 70B scale on the sheet. **gpt-4-turbo-preview** 1324 **llama-3.3-70b-versatile** 1318 **Llama-3.3-70B-Instruct** 1318 **GPT-4 Omni** 1316 **Claude-3-7-Sonnet-Latest** 1268 ## pricing The cost of running Llama 3.3 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, the same price as Llama 3.1 70B but with improved instruction-following (IFEval 92.1 vs 88.6). ## What's Twitter saying? * Developers praise **Llama 3.3 70B Instruct's superior instruction following**, scoring 92.1 on IFEval and outperforming Llama 3.1 405B (88.6) and GPT-4o (84.6). * Tech reviewers highlight its **strong coding performance**, with 88.4 on HumanEval (near Llama 3.1 405B's 89.0) and improvements over prior 70B models. * Commentators note its **efficiency and cost-effectiveness**, achieving 276 tokens/sec inference speed (25% faster than Llama 3.1 70B) at $0.10/M input tokens. | 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) ### What is the difference between Llama normal and instruct? The base Llama model generates text through next-token prediction, while the Instruct variant is fine-tuned for following instructions and dialogue. For production applications, the [Instruct version](https://telnyx.com/resources/llama-3-70b-overview) is recommended. ### How many tokens can you have in Llama 3.3 70B? Llama 3.3 70B supports a 128K token context window, enabling long-document processing and extended conversations. This matches the [Llama 3.1 series](https://www.llama.com/docs/model-cards-and-prompt-formats/llama3%5F3/) context length. ### Is Llama 3.3 70B good for coding? Llama 3.3 70B Instruct delivers strong coding performance, approaching the results of Llama 3.1 405B on many programming benchmarks. It handles code generation, review, and debugging effectively through [hosted inference platforms](https://telnyx.com/llm-library/llama-3-70b). ### How does Llama 3.3 compare to 3.1? Llama 3.3 70B achieves similar performance to Llama 3.1 405B on many benchmarks despite being roughly 6x smaller. It represents Meta's most [efficient 70B model](https://telnyx.com/resources/llama-3-70b-overview) to date. Llama 3.3 70B Instruct: Advanced Open-Source LLM