about
Jumping from 8K to 128K tokens of context versus Llama 3, this model was fine-tuned on 25 million synthetic examples 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)131,072
Use cases for Meta-Llama-3.1-8B-Instruct
- Multilingual customer support: Native support for 8 languages (English, German, French, Italian, Portuguese, Hindi, Spanish, Thai) enables single-model deployment across regional support teams.
- Tool-augmented research agents: Built-in tool-calling capability allows it to query APIs, execute code, and retrieve data within multi-step reasoning workflows.
- 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.
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 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 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 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.
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 Meta Llama 3.1 8B Instruct?
Meta Llama 3.1 8B Instruct is a powerful AI model engineered for customer support and diverse applications. It delivers strong performance on complex tasks while maintaining safety and reliability.
What are the key features of Meta Llama 3.1 8B Instruct?
Meta Llama 3.1 8B Instruct offers advanced reasoning performance, safety-by-design, and production-ready reliability. It excels at complex reasoning, analysis, and diverse task execution.
Can Meta Llama 3.1 8B Instruct be used for enterprise applications?
Yes, Meta Llama 3.1 8B Instruct is designed for enterprise scale with strong reasoning depth and safety guarantees. It's trusted by organizations for mission-critical applications.
How does Meta Llama 3.1 8B Instruct compare to other models?
Meta Llama 3.1 8B Instruct offers superior performance on complex reasoning and diverse tasks. It balances capability with practical efficiency, making it ideal for most production use cases.
Where can I deploy Meta Llama 3.1 8B Instruct?
Deploy Meta Llama 3.1 8B Instruct on Telnyx Inference for production use cases. Visit the Telnyx Developer Center for integration guides.
What are best practices for using Meta Llama 3.1 8B Instruct?
Provide detailed context and clear problem specifications for best results. Use system prompts to guide behavior on specialized tasks. For research applications, include relevant materials.
Is Meta Llama 3.1 8B Instruct suitable for my use case?
Meta Llama 3.1 8B Instruct is versatile and suitable for coding, analysis, writing, research, and strategic problem-solving. Evaluate its performance on your specific tasks to determine fit.