2.5 Open-Source Reasoning Models

BeginnerFree

Prerequisites: 2.1 AI Landscape

Why Do We Need It? (Problem)

You've been happily calling GPT-5 and Claude Opus 4.6 APIs when your boss walks in:

"The monthly AI bill is HOW MUCH?! Can't we just run something locally?"

Or maybe you work in healthcare / finance / government where data cannot leave the building. Sending patient records to OpenAI's API? That's not a career move, that's a career-ending move.

Open-source models solve three problems:

1. Cost — Run locally, pay only for electricity (and your GPU's therapy bills)
2. Privacy — Data never leaves your infrastructure
3. Customization — Fine-tune for your specific domain without asking permission

Fun Fact

DeepSeek-V3.2 matches GPT-5 on many benchmarks while costing 10-50x less via their API. The open-source AI revolution isn't coming — it already happened while we were arguing about which proprietary model is better.

What Is It? (Concept)

The 2026 Open-Source Model Landscape:

Key Players:

Model	Params	Strengths	Context	Vibe Check
DeepSeek-V3.2	671B (37B active, MoE)	GPT-5 level general performance, absurdly cheap API	128K	"The model that made OpenAI nervous"
DeepSeek-R1	671B	Best open-source reasoning, shows thinking process	128K	"Watches the AI think out loud — fascinating and terrifying"
Qwen 2.5 / Qwen3	0.5B-235B	Multi-size lineup, excellent Chinese support	128K	"China's Swiss Army knife of AI"
Llama 4 Scout	MoE	10 MILLION token context window	10M	"Could read every Harry Potter book at once"
GPT-OSS 120B	120B	OpenAI's first open model, strong reasoning	varies	"OpenAI finally said 'fine, have some open source'"
GPT-OSS 20B	20B	Compact, runs on a single GPU, great for code	varies	"The model you can actually run on your laptop"

Common Mistake

"Open-source = free to run" is misleading. Running DeepSeek-R1 at full precision requires ~1.3TB of VRAM. That's roughly 16 A100 GPUs. Most people use quantized versions (GGUF/GPTQ) that fit on consumer hardware with some quality trade-off.

Open-Source vs Closed-Source Decision Tree:

Soul-Searching Question

If DeepSeek's API costs $0.28 per million input tokens (vs GPT-5's $1.25), and the quality is "close enough" for 80% of tasks... why are you still paying 5x more? Is it the brand name? The familiar API? Be honest.

How to Run Locally

Option 1: Ollama (Easiest)

# Install
curl -fsSL https://ollama.ai/install.sh | sh

# Run a model
ollama run deepseek-r1:8b

# Or run with API
ollama serve  # Starts OpenAI-compatible API on localhost:11434

Option 2: LM Studio (GUI)

• Download from lmstudio.ai
• Browse and download models from the UI
• One-click local API server

Summary (Reflection)

• What we solved: Understood open-source model options for cost savings, privacy, and customization
• What remains: Know the models, but when should you fine-tune vs use RAG vs prompt engineering? — Chapter 3.4 covers Fine-tuning
• Key takeaways:
  1. Open-source models in 2026 rival proprietary ones in many tasks
  2. DeepSeek-R1 = best open reasoning; Llama 4 Scout = largest context; Qwen = best multilingual
  3. Running locally requires significant hardware — quantized versions are your friend
  4. The real question isn't "open vs closed" but "what's the cheapest option that meets my quality bar"

"The best model is the one that solves your problem without bankrupting your department."

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Last updated: 2026-02-22