Why We Made DeepSeek R1 the Default AI

Not all AI is the same

Most language models work the same way: read the prompt, predict the next token, output an answer. Fast, but shallow. For simple questions — "how do I center a div?" — this is fine. For hard coding problems — "why does my auth middleware let through unauthenticated requests in production but not development?" — shallow reasoning often produces plausible-sounding answers that are just wrong.

DeepSeek R1 is different. It's a reasoning model.

What reasoning models actually do

Before DeepSeek R1 outputs an answer, it generates an extended chain of thought — it literally thinks through the problem step by step. In Spinini's UI you can see this reasoning trace as a collapsible section above the final answer.

For a debugging question, the reasoning might look like:

> "The user says auth works in development but not production. Let me think about what differs between those environments. Environment variables? PORT? The middleware order? Let me consider each case..."

Then the final answer reflects that analysis rather than a first-instinct guess.

Why this matters for coding

Coding tasks are overwhelmingly about reasoning:

• Debugging requires forming a hypothesis, tracing execution, ruling out causes
• Architecture decisions require weighing trade-offs across many constraints
• Refactoring requires understanding what a complex piece of code actually does before touching it
• Multi-file changes require keeping a mental model of how parts interact

Standard LLMs handle these by pattern-matching to similar code they've seen. R1 actually works through the logic. The difference shows most clearly on problems that are unusual, subtle, or span many files.

Our benchmark results

When we tested R1 against a standard mid-tier model on a suite of 50 real debugging tasks submitted by Spinini users:

The gap widens the harder the problem gets. For simple edits, both models do fine. For the genuinely hard stuff, R1 wins decisively.

The trade-off: speed

R1's reasoning chain takes time. A simple question that Sonnet answers in 2 seconds might take R1 8–12 seconds because it's generating a full chain of thought first.

For quick questions and simple edits, this is annoying. That's why we also offer DeepSeek V3 — the fast sibling — for when you want instant responses and the problem isn't particularly complex.

How to choose

Our recommendation:

• DeepSeek R1 (default) — use for anything non-trivial: debugging, building features, understanding unfamiliar code
• DeepSeek V3 — use for quick questions, simple edits, rapid prototyping where you just want fast iteration
• Spinini Smart — great all-rounder for everyday building
• Spinini Deep — maximum quality for the hardest problems

You can switch models at any time using the picker in the agent toolbar or chat panel header.

What's next

We're experimenting with automatic model routing — the system would detect whether your task is simple or complex and pick the right model automatically. We'll share more when it's ready.

In the meantime, R1 stays the default. For most of what Spinini users actually build, a model that thinks before it answers is the right call.