Hey,

Everyone's still talking about ChatGPT and Claude like they're the only game in town. Meanwhile, the open source world has been quietly having its best year ever — and most people haven't noticed.

This week I want to talk about the free, open source AI models you can actually run right now. No API bills. No usage limits. No data leaving your machine. Just powerful models you download, run locally, and own completely.

And yes — one of them literally dropped three days ago. Let's get into it.



1. DeepSeek V4 — The One That Just Dropped

If you followed the AI space in early 2025, you remember when DeepSeek R1 rattled the entire industry. A Chinese startup, trained on limited hardware, putting out a model that genuinely competed with GPT-4. Markets moved. People panicked. It was a moment.

V4 is the follow-up — and it's bigger in every sense.

DeepSeek just released two versions: V4 Flash and V4 Pro. The Pro model has 1.6 trillion total parameters with 49 billion active ones per task — currently the largest open-weight model on the planet. The context window is 1 million tokens, meaning you can feed it entire codebases in a single prompt and it won't blink.

What's actually impressive though isn't the size — it's the efficiency. V4 Pro uses a new Hybrid Attention Architecture that requires only 27% of the compute of V3.2 for the same task. That means faster responses, lower infrastructure cost, and models that actually run on real hardware.

On coding benchmarks, DeepSeek says V4's performance is comparable to GPT-5.4. And the price? V4 Flash starts at $0.14 per million input tokens via API — cheaper than Claude Haiku and GPT Mini. For self-hosting, it's MIT licensed, meaning you can run it commercially with zero restrictions.

This one is genuinely worth your attention right now while it's still fresh.

Best for: Coding, agentic tasks, long document processing Run it via: DeepSeek API or Hugging Face (self-host).

Meta's Llama series is the model family that made local AI mainstream. Llama 3.3 is the current sweet spot — powerful enough to handle most real-world tasks, small enough to run on a decent laptop without melting it.

What I like about Llama 3.3 is how boring it is in the best way. It just works. You pull it via Ollama in one command, it loads up, and you start using it. No setup drama. No weird quirks. Great for writing, summarisation, Q&A, and light coding tasks.

If you've never run a model locally before, this is where you start. Seriously.

Best for: General use, writing, summarisation, beginners Run it via: Ollama (ollama pull llama3.3) .

Google has been quietly building something special with the Gemma family, and Gemma 4 31B is the best version yet. 31 billion parameters, 256K token context window, Apache 2.0 licensed — and it fits on a single 80GB GPU.

What sets it apart is the reasoning and coding performance. On benchmarks like AIME and LiveCodeBench it competes with models two or three times its size. For developers building RAG pipelines or coding assistants, Gemma 4 31B is punching well above its weight right now.

The Apache 2.0 license also means no headaches for commercial projects — you can fine-tune it on your own data and ship it in a product without worrying about terms of service.

Best for: Coding assistants, reasoning tasks, RAG pipelines Run it via: Ollama or HuggingFace.




4. Qwen3.5 — The One That Runs on a MacBook

Alibaba's Qwen series doesn't get nearly enough attention outside of China, and that's a mistake.

Qwen3.5 has 122 billion total parameters but only 10 billion active at any time — thanks to a Mixture of Experts architecture. What that means practically: it runs on a MacBook with 64GB RAM. A laptop. No GPU server. No cloud credits. Just your machine.

The multilingual support is also exceptional — 200+ languages and dialects — making it the go-to choice if your users aren't all English speakers. For indie developers and solo builders, Qwen3.5 is a genuinely exciting option because the barrier to running it is so low.

Best for: Multilingual apps, local inference, solo developers Run it via: Ollama or LM Studio.

Mistral 7B has been around for a while now but it earns its spot on this list every single week. At 7 billion parameters it's tiny — but it's fast, efficient, and genuinely good at instruction following and coding tasks for its size.

The real value of Mistral 7B in 2026 is as a workhorse for low-resource environments. Running AI on a Raspberry Pi? Embedding a model into a lightweight app? Building something that needs to run on edge hardware? Mistral 7B is your answer. No other model at this size comes close to its overall quality-to-weight ratio.

It also runs flawlessly via Ollama with a single command, making it one of the easiest models to get started with.

Best for: Edge devices, lightweight apps, fast prototyping Run it via: Ollama (ollama pull mistral).

🛠️ Dev Tip of the Week

Before picking a model, ask yourself one question: where will this actually run? A 1.6 trillion parameter model is impressive on paper but useless if your server can't handle it. Match the model to your hardware first, then optimise for quality. Start small — Mistral or Llama — get something working, then scale up if you need to. Most use cases don't need the biggest model. They need the fastest one that's good enough.

The open source model race is moving faster than ever — and the gap between free and paid is shrinking every month. You don't need a $20/month API subscription to build something real anymore.

Which of these are you most excited to try? Hit reply — I read every single one.

— Dhanush from Tech Zenith