Welcome to the NZRT Wiki Podcast. Today we’re looking at Models & Providers.

If you’ve been working with AI tools at NZRT, you’ve probably noticed there’s no shortage of model options out there. This episode walks you through the main providers we work with, how to pick the right model for a given job, and what you can expect to pay.

Let’s start with Anthropic, the company behind Claude. There are three Claude models you’ll encounter. First is Claude Haiku 4.5 — this is your go-to for fast, high-volume work where cost matters. Think classification tasks, quick extractions, anything you need to run at scale. Then there’s Claude Sonnet 4.6, which sits in the middle and is probably the model you’ll reach for most often. It handles coding, reasoning, and writing really well without breaking the budget. At the top end is Claude Opus 4.7, the most capable of the three — best suited for complex reasoning tasks and working through long documents. All three Claude models support tool use, vision, extended thinking, computer use, and MCP integration. They all also carry a two-hundred-thousand token context window, which is substantial.

Next up is OpenAI’s GPT lineup. You have four key models here. GPT-4o is multimodal, fast, and broadly capable across a wide range of tasks. GPT-4o mini is the lighter, cheaper version — great for classification and anything you need done quickly at low cost. Then there are the reasoning-focused models: o1 uses extended thinking tokens to work through problems step by step, and o3 pushes that even further with more advanced reasoning and a two-hundred-thousand token context. GPT-4o and GPT-4o mini both sit at one-hundred-and-twenty-eight thousand tokens of context.

Moving on to Google’s Gemini family. This is where context windows get truly massive — all three Gemini models support up to one million tokens. Gemini 1.5 Pro is the standout for long-context multimodal work. Gemini 2.0 Flash gives you speed and low cost. And Gemini Ultra sits at the top for maximum capability. If you ever find yourself dealing with an enormous document — something north of five-hundred-thousand tokens — Gemini 1.5 Pro is where you want to be.

Now let’s talk about open-source and self-hosted options, because not every use case needs a cloud API. There are five models worth knowing here. Llama 3.3 from Meta at seventy billion parameters is a strong open-source baseline. Mistral Large at a hundred-and-twenty-three billion parameters is European-built and particularly solid for coding tasks. Qwen 2.5 from Alibaba comes in at seventy-two billion parameters and handles multilingual work well. DeepSeek R1 is a Chinese-built model using a mixture-of-experts architecture, weighing in at six-hundred-and-seventy-one billion parameters, with strong reasoning capability. And Phi-4 from Microsoft is a compact fourteen-billion-parameter model — efficient and well-suited for running on less powerful hardware. You can run all of these locally using tools like Ollama, LM Studio, vLLM, or llama.cpp.

So how do you choose? The wiki lays out a handy decision guide. For high-volume classification or extraction work, reach for Haiku 4.5 or GPT-4o mini. For code generation and general reasoning, Sonnet 4.6 or GPT-4o are your best bets. Complex architecture work or long documents point you toward Opus 4.7. If your context needs are enormous, Gemini 1.5 Pro handles that. Privacy-sensitive or on-premise requirements mean you’re looking at Llama 3.3 running locally via Ollama. And for extended step-by-step reasoning chains, o1, o3, or Claude with extended thinking enabled are your options.

On pricing — all the major APIs charge per token, and they bill input and output separately. The wiki groups these into three tiers. At the cheap end, models like Haiku and GPT-4o mini run somewhere between ten cents and forty cents per million tokens. The mid-tier — Sonnet and GPT-4o — sits between one and five dollars per million tokens. Premium models like Opus or o1 can run anywhere from fifteen to seventy-five dollars per million tokens. One thing worth knowing: both Claude and Gemini support prompt caching, which means repeated system prompts don’t cost you full price every time — a useful lever if you’re running the same setup across many requests.

That’s it for this episode of the NZRT Wiki Podcast. Thanks for listening.