Free AI that runs on your laptop: the best open model for each task
We read the Hugging Face configs and model cards of 10 free open-weight models: how many tokens each handles, what hardware it needs and which task it wins.
4 min read
"Tokens allowed": what the numbers on a model card mean
Every model has a context window: the most text it can read and write in one go, counted in tokens. A token is a piece of a word; in English, 1,000 tokens is about 750 words. Hindi and other Indian languages usually take more tokens for the same sentence, so budget extra.
| Context window | About this many words | About this many pages |
|---|---|---|
| 128K tokens | 96,000 | 300 |
| 256K tokens | 190,000 | 600 |
| 1M tokens | 750,000 | 2,500 |
Two warnings. First, the window covers the answer too, so a model that reads 128K tokens can't also write a long reply on top. Second, the number in a model's config file and the number on its card don't always match: Mistral Small 4's config says 1,048,576 tokens, but its card promises 256K. Trust the card.
10 free models, checked on Hugging Face
| Model | Size | Context | Licence | Rough hardware |
|---|---|---|---|---|
| Gemma 4 E4B (Google) | 8B, 4.5B effective | 128K | Apache 2.0 | Phone or laptop |
| LFM2.5 2.6B (Liquid AI) | 2.6B | 128K | LFM licence | Any laptop CPU, under 2.5 GB |
| Ministral 3 8B (Mistral) | 8.8B | 256K | Apache 2.0 | 12 GB graphics card |
| Granite 4.2 8B (IBM) | 8B | 128K, 512K extended | Apache 2.0 | 16 GB laptop |
| gpt-oss-20b (OpenAI) | 21B, 3.6B active | 128K | Apache 2.0 | 16 GB of memory |
| Gemma 4 26B A4B (Google) | 25.2B, 3.8B active | 256K | Apache 2.0 | Gaming GPU or 32 GB Mac |
| Qwen3.8 27B (Alibaba) | 27B | 262K, 1M extended | Apache 2.0 | Gaming GPU or 32 GB Mac |
| GLM-4.7-Flash (Z.ai) | 31B, 3B active | 200K | MIT | Gaming GPU or 32 GB Mac |
| Qwen3-Coder-Next (Alibaba) | 80B, 3B active | 256K | Apache 2.0 | Workstation |
| DeepSeek V4.1 Flash | Data-centre size | 1M | MIT | Servers only |
"Active" parameters matter for speed. A mixture-of-experts model like gpt-oss-20b stores 21B parameters but uses only 3.6B for each word, so it runs much faster than its size suggests. It still needs memory for all of them.
The best free pick for each job
| Your job | Free pick | Why |
|---|---|---|
| Pull amounts, dates and names out of emails or bills | LFM2.5 2.6B | Its makers recommend it for data extraction and tool use, and it runs on a plain CPU. Not for coding or general knowledge. |
| A private assistant on a phone or old laptop | Gemma 4 E4B | Built for on-device use, and it understands voice notes and photos as well as text. |
| Drafting emails, translating, summarising on a 16 GB laptop | Ministral 3 8B | Its card lists translation, document description and a local daily driver as its uses. |
| Maths, planning and step-by-step reasoning | gpt-oss-20b | You can set its reasoning effort to low, medium or high, and it fits in 16 GB. |
| Business workflows with tools and an easy licence | Granite 4.2 8B | IBM aims it at enterprise use, with tool calling and an Apache 2.0 licence. |
| Reading a whole contract or report at once | Qwen3.8 27B | 262K tokens natively, extendable to a million, and it reads scanned pages as images. |
| Coding on one machine | GLM-4.7-Flash | On its own card it scores 59.2 on SWE-bench Verified, against 34.0 for gpt-oss-20b. |
| A coding agent inside your editor, with a powerful PC | Qwen3-Coder-Next | Built for coding agents, with only 3B active parameters out of 80B. |
“A 2.6-billion-parameter model now runs in under 2.5 GB of memory. For pulling numbers out of invoices, that's all the AI most small businesses need.”
Benchmarks on model cards are published by the model makers themselves, so treat them as a shortlist, not a verdict. The test that counts is 20 of your own real tasks.
How to run one for free
- Install a desktop app that runs local models, such as LM Studio or Ollama. Both are free.
- Search for the model name inside the app, or on Hugging Face with "GGUF" added (the compressed format these apps load).
- Start with the smallest version that fits your memory, try your 20 test tasks, then move up a size if the answers aren't good enough.
- Read the licence before using it for clients. Apache 2.0 and MIT allow commercial use; custom licences like LFM's have conditions.
What about free online options?
Hugging Face's Inference Providers now give free accounts no monthly credits: you buy credits or pay $9 a month for PRO, which includes $2 of credits. Many models have free demo Spaces, which are fine for trying a model but not for daily work. Google's Gemini API also has a free tier on its Flash and Flash-Lite models, with usage limits.
If you'd rather pay a few rupees than run anything yourself, see our list of the cheapest hosted AI models.
Questions people ask
Which free AI model is best for a normal laptop?
For 16 GB of memory, gpt-oss-20b or Ministral 3 8B. On older laptops, Gemma 4 E4B or LFM2.5 2.6B, which runs in under 2.5 GB.
How many tokens can a free open model read?
Most of the models here read 128K to 262K tokens, about 300 to 600 pages of English; DeepSeek V4.1 Flash reads a million.
Is Hugging Face free to use for AI models?
Downloading open models is free. Hosted inference needs paid credits; PRO costs $9 a month and includes $2 of credits.
Can I use these models for my business?
Models under Apache 2.0 or MIT allow commercial use. Read any custom licence, such as Liquid AI's LFM licence, before using it with clients.
Sources
- Hugging Face — Inference Providers pricing and credits
- Hugging Face — PRO plan pricing
- Google — Gemma 4 E4B model card
- Liquid AI — LFM2.5 2.6B model card
- Mistral — Ministral 3 8B model card
- IBM — Granite 4.2 8B model card
- OpenAI — gpt-oss-20b model card
- Alibaba — Qwen3.8 27B model card
- Z.ai — GLM-4.7-Flash model card
- Alibaba — Qwen3-Coder-Next model card
- DeepSeek — V4.1 Flash model card
- Mistral — Mistral Small 4 model card
Checked on 22 Oct 2026. Prices and rules change; check the sources before relying on a number.