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. 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.
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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 and supports text generation, code, and complex reasoning tasks.
Yes, Llama 3.1 70B is open-source and free for commercial use under Meta's license. It can be downloaded and self-hosted or accessed through hosted inference providers at per-token rates.
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.
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).
| 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 |
| gemma-2b-it | text generation | English | 8,192 | 2.5B | small | gemma | |
| gemma-4-26B-A4B-it | text to-text | Multilingual | 262,144 | 25.8B | small | apache-2.0 | |
| gemma-4-31B-it | text generation | Multilingual | 131,072 | 30.7B | small | apache-2.0 | |
| meta-llama | Llama-3.3-70B-Instruct | text generation | Multilingual | 99,000 | 70.6B | large | 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-M2.7 | 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 | 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 | 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 | text generation | Multilingual | 400,000 | 0 | large | openai |
| gemini-2.5-flash | text generation | Multilingual | 400,000 | 0 | large | openai | |
| gemini-3.7-flash | text generation | Multilingual | 400,000 | 0 | large | openai | |
| groq | 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 | text generation | Multilingual | 400,000 | 0 | large | openai |
| openai | 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 | 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 |
Running Llama 3.1 70B requires at minimum a GPU with 40+ GB VRAM for quantized inference, or multiple high-end GPUs for full-precision deployment. Cloud GPU instances on AWS, GCP, or specialized inference providers are popular deployment options.
Llama 3.1 70B has a training data cutoff of December 2023. It will not have knowledge of events, releases, or information published after that date.
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 alongside the model release.
