--- title: "Meta Llama 3.1 8B Instruct: Powerful AI Model for Diverse Tasks" canonical: https://telnyx.com/llm-library/meta-llama-3.1-8b-instruct --- # Meta Llama 3.1 8B Instruct: Powerful AI Model for Diverse Tasks [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.) # Meta-Llama-3.1-8B-Instruct Powerful AI model optimized for diverse use cases. [Start building](https://telnyx.com/sign-up)[GET Available Models](https://developers.telnyx.com/docs/inference/models) ## about Jumping from 8K to 128K tokens of context versus Llama 3, this model was fine-tuned on [25 million synthetic examples](https://ai.meta.com/blog/meta-llama-3-1/) generated from the larger 405B variant and aligned using a combination of rejection sampling and Direct Preference Optimization. It was the first open-weight 8B model to ship with native tool-calling support across 8 languages, trained on over 15 trillion tokens of public data. Licensellama 3.1 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 ### personaplex-7b-v1 ### 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-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 Meta-Llama-3.1-8B-Instruct In the [AI Playground](https://portal.telnyx.com/#/app/next/ai/ai-playground "Telnyx AI Playground") select Meta-Llama-3.1-8B-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 8B Instruct good for? Llama 3.1 8B Instruct is well suited for conversational AI, code generation, and text summarization tasks where a balance of capability and efficiency is needed. Its compact size makes it a practical choice for [production inference deployments](https://telnyx.com/llm-library/llama-3-instruct-8b) that require low latency and manageable compute costs. ### What is Llama 3 8B Instruct? Llama 3.1 8B Instruct is Meta's instruction-tuned 8 billion parameter model from the [Llama 3.1 family](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct), released in July 2024\. It supports a 128K context window and multiple languages, with strong performance on reasoning and code tasks relative to its size. 131,072 ## Use cases for Meta-Llama-3.1-8B-Instruct 1. **Multilingual customer support:** Native support for 8 languages (English, German, French, Italian, Portuguese, Hindi, Spanish, Thai) enables single-model deployment across regional support teams. 2. **Tool-augmented research agents:** Built-in tool-calling capability allows it to query APIs, execute code, and retrieve data within multi-step reasoning workflows. 3. **Long-document question answering:** The 128K context window processes entire technical manuals or codebases in a single prompt for targeted information extraction. ## Quality Arena EloN/A MMLUN/A MT BenchN/A Llama 3.1 8B Instruct scores 69.4% on MMLU (5-shot) and 73.0% on MMLU (0-shot CoT), improving over Llama 3 8B Instruct (67.4% on 5-shot) by about 2 points on the same configuration. It also scores 72.6% on HumanEval, more than double the scores of Mistral 7B v0.2 (30.5%) and Gemma 7B IT (32.3%) on the same sheet. **Claude-Opus-4-6** 1501 **GLM-5** 1456 **gpt-5.1** 1455 **Kimi-K2.5** 1454 **gpt-5.2** 1440 ## pricing The cost of running Llama 3.1 8B Instruct with Telnyx Inference is $0.0002 per 1,000 tokens. Analyzing 1,000,000 customer chats at 1,000 tokens each would cost $200, the same as Llama 3 8B Instruct but with stronger benchmark performance across the board. ## What's Twitter saying? * **Benchmark improvements don't always translate to real-world performance**: [While Llama 3.1 8B showed significant](https://designforonline.com/ai-models/meta-llama-3-1-8b-instruct/) benchmark gains (reportedly double the quality compared to previous versions), tech commentator Matthew Berman found the practical results "very disappointing" when actually testing the model. * **Excellent balance for local deployment**: [Developers praise the 8B model for](https://www.youreverydayai.com/llama-405b-review-whats-new-in-llama-3-1/) offering a strong compromise between performance and efficiency, making it practical to run locally on consumer hardware like an RTX 4070 Ti without sacrificing quality. * **Competitive with larger open-source alternatives**: [The model is positioned as a](https://www.youtube.com/watch?v=4rk9fHIOGTU) fast and efficient option that competes well with other open-source models of similar size, though it arrived nearly 9 months after competing models like Mistral 7B. | 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 | personaplex-7b-v1 | speech to-speech | English | 3,000 | 7.0B | small | nvidia-open-model-license | | 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-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 you need for Llama 3.1 8B? Llama 3.1 8B requires approximately 16GB of VRAM for full-precision inference, or 8GB when using 4-bit quantization. Alternatively, hosted inference platforms like [Telnyx](https://developers.telnyx.com/docs/inference/llama-index) provide API access without managing local GPU infrastructure. ### Is Llama 3 8B better than ChatGPT 4? Llama 3.1 8B does not match GPT-4's performance on complex reasoning and multi-step tasks, as GPT-4 is a significantly larger model. However, for straightforward generation and [code assistance tasks](https://telnyx.com/llm-library/llama-3-instruct-8b), the 8B model offers competitive results at a fraction of the cost. ### What GPU is needed for Llama 3 8B? An NVIDIA GPU with at least 8GB of VRAM (such as an RTX 3070 or above) can run Llama 3.1 8B using quantized formats. For full-precision inference, [16GB+ GPUs](https://telnyx.com/resources/llama-3-1-70b-instruct) like the RTX 4090 or A100 are recommended. Meta Llama 3.1 8B Instruct: Powerful AI Model for Diverse Tasks