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Meta Llama 3.1 8B Instruct is the small, open-weight model that a great deal of the world quietly runs on. Eight billion parameters, a 128,000 token context window, and a licence that lets you download the weights and run them on your own hardware. It is the model people build on when they do not want to depend on anyone's API. On this page you can try it first, free, without an account and without downloading 16 gigabytes to find out whether it suits you.
Not because it beats the big closed ones. It does not, and anyone telling you otherwise is selling something. It matters for a different reason: it is the model you can actually own. Nobody can reprice it, deprecate it, change its behaviour overnight or refuse you access to it.
Llama 3.1 8B Instruct was released by Meta in July 2024 as part of the Llama 3.1 family, alongside the 70B and 405B models. It is text only: it does not read images. Its context window is 128K tokens. It is officially multilingual across English, German, French, Italian, Portuguese, Hindi, Spanish and Thai, and it handles others with less reliability. Its training data has a cutoff, so it does not know about recent events unless you tell it.
The weights are published under the Llama 3.1 Community License, which permits commercial use with conditions. Read the licence before you build a business on it.
| Self-hosted | Hosted API | AIFreeForever Llama 3.1 | |
|---|---|---|---|
| Price | Your hardware | Metered per token | Free |
| Setup | Download, GPU, serving stack | Account and key | None |
| Data leaves your machine | No | Yes | Yes |
| Model can change under you | No | Possibly | Possibly |
| Good for | Production | Scale | Finding out if it fits |
That table is deliberately honest about the one thing this page cannot give you. If the reason you want an open model is that the data must never leave your building, this page does not solve that problem. Use it to decide, then self-host.
| Model | Context | Weights | Reads images |
|---|---|---|---|
| Meta Llama 3.1 | 128K | Open | No |
| Microsoft Phi-4 | 16K | Open (MIT) | No |
| DeepSeek V3.2 | 128K | Open (MIT) | No |
| DeepSeek V4 | 1M | Open (MIT) | No |
| GPT 5 | 400K | Closed | Yes |
Both are small open models and they are good at different things. Phi-4 is 14B and unusually strong at maths and structured reasoning for its size, with a 16K window. Llama 3.1 8B is smaller, faster and has a 128K window, which matters if you are feeding it documents. If the job is reasoning, try Phi-4. If the job is length or throughput, try this one.
DeepSeek V3.2 is a far larger model and it shows on anything difficult. It is also open weights, but running 671B parameters yourself is a different conversation from running 8B. Llama 3.1 is the one that fits on hardware you might already own.
Riddles and trick questions tell you almost nothing about whether a model can classify your support tickets. Paste ten real examples of the thing you actually need done.
Big models forgive vague instructions. Small ones do not. Say the format, give an example of a good answer, and state what to do when the input is unexpected. Most of the gap between an 8B and a frontier model closes with a better prompt.
One job per prompt. Small models are good at "extract the dates" and unreliable at "extract the dates, summarise the tone and suggest a reply" in a single request.
Open GPT 5 or DeepSeek V4 in the bar and run it again. The difference tells you what self-hosting would actually cost you in quality, which is the number you are really trying to find.
The chat box at the top of this page is already on Meta Llama 3.1.
Give it a real task from your own work, with an example of the output you want.
Attach a document if you have one. PDF, Word and Excel are read directly; images are not.
Run the same prompt on a larger model in the bar and compare. That comparison is the point.
Your message goes over HTTPS through our server to the model. We do not keep conversations and files are deleted at the end of the session. Note the honest limitation stated above: this is still a hosted service, so if your requirement is that data never leaves your own infrastructure, this page is a trial and not a solution.
More than a frontier model does, and in predictable ways. It invents citations and specifics with a straight face. It loses the thread in a long, multi-part instruction. Its arithmetic is unreliable past small numbers. Its knowledge is older than the others on this site. None of that makes it a bad model; it makes it a model with a job. Give it narrow, well-specified work and it is genuinely useful. Give it an open-ended research question and it will disappoint you.
Yes. No login, no card, no message cap.
The 8B Instruct model, the smallest of the Llama 3.1 family.
Yes, from Meta and from Hugging Face, under the Llama 3.1 Community License. That is the whole point of an open model.
No. Llama 3.1 is text only. The vision models came with Llama 3.2.
128K tokens, which is a few hundred pages. Quality on very long inputs is weaker than on a frontier model with the same window.
It can read and explain code and write small functions. For real debugging, DeepSeek V4 in the bar is much stronger.
Officially English, German, French, Italian, Portuguese, Hindi, Spanish and Thai. Others work with less reliability.
No. This is an independent site offering free access to an openly published model.
Try the open model before you host it. Meta Llama 3.1 is free at the top of this page, no account needed.
