> ## Documentation Index
> Fetch the complete documentation index at: https://docs.zerotwo.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Reasoning Models
> When and how to use extended-thinking models in ZeroTwo for complex reasoning, math, and multi-step problems.
Reasoning models are AI models with extended thinking capabilities. Rather than producing an answer immediately, they work through a problem step by step — exploring approaches, checking their reasoning, and refining their thinking before producing a final response. The result is dramatically better accuracy on complex problems.
## What makes reasoning models different
Standard models generate responses token by token in a single forward pass. Reasoning models have an additional "thinking" phase: an extended internal reasoning process that runs before the final answer is produced. This thinking process is often visible as a collapsed section in ZeroTwo — you can expand it to see how the model worked through the problem.
The tradeoff: reasoning models are **slower** and typically use **more of your premium quota**, but they're significantly more accurate on hard problems where standard models stumble.
## Available reasoning models in ZeroTwo
| Model | Provider | Notes |
| ----------------------------------------------- | --------- | ---------------------------------------------------------------------- |
| **o3** | OpenAI | Advanced reasoning, among the most capable for math and logic |
| **o4-mini** | OpenAI | Faster and more efficient reasoning; strong balance of speed vs. depth |
| **DeepSeek Reasoner** | DeepSeek | Excellent for math, logical proofs, and structured analysis |
| **Claude Sonnet / Opus with extended thinking** | Anthropic | Configurable thinking depth via thinking level slider |
| **Gemini 2.5 Pro** | Google | Built-in reasoning; also multimodal |
## Thinking levels
Some models — especially Claude — expose a **thinking level slider** in the ZeroTwo prompt bar when selected. This controls how much reasoning the model does before responding:
| Thinking level | Behavior | Best for |
| -------------- | ---------------------------------------------- | ----------------------------------------------------------------------- |
| **Low** | Light reasoning, quick response | Problems that need some structured thinking but not exhaustive analysis |
| **Medium** | Balanced depth and speed | Most complex tasks — good default |
| **High** | Deep, extended reasoning; significantly slower | The hardest problems where accuracy is paramount |
Higher thinking levels use more of your premium message quota and take more time, but produce more thorough and accurate output for genuinely hard problems.
Not all reasoning models expose a thinking level slider. For models like o3 and o4-mini, the reasoning depth is handled internally by the model. For Claude, the slider gives you explicit control.
## When to use reasoning models
Reasoning models provide the most benefit for tasks that require careful, multi-step thinking:
**Use a reasoning model for:**
* **Complex math problems**: multi-step calculations, proofs, statistics, algebra
* **Hard coding challenges**: algorithm design, complex debugging, system architecture decisions, performance optimization
* **Logical reasoning**: puzzles, deductive inference, formal logic
* **Research synthesis**: pulling together insights from multiple sources or a long document into a coherent structured analysis
* **Detailed analysis**: when you want the model to carefully consider multiple angles before reaching a conclusion
* **Planning tasks**: anything where the right answer requires working through dependencies and ordering carefully
**Examples:**
* `"Prove that the sum of the first n odd numbers equals n²."`
* `"Debug this recursive algorithm that's producing incorrect output for edge cases: [code]"`
* `"Analyze this 50-page business strategy document and identify the three most significant risks, with supporting evidence from the text."`
* `"Design a database schema for a multi-tenant SaaS application with these requirements: [requirements]"`
## When NOT to use reasoning models
Reasoning models are overkill — and slower — for tasks that don't require deep analysis:
**Use a standard model for:**
* Simple questions with straightforward answers
* Quick drafts and brainstorming
* Conversational exchanges
* Reformatting or transforming text
* Simple code snippets
* Summarizing short content
Standard models are faster, just as good for easy tasks, and don't consume as much of your premium quota.
## Tradeoffs at a glance
| Aspect | Standard models | Reasoning models |
| ----------------- | ----------------- | -------------------- |
| Response time | Seconds | Seconds to minutes |
| Quota cost | 1 premium message | 1+ premium messages |
| Simple tasks | Excellent | Overkill |
| Complex reasoning | Good | Excellent |
| Math and logic | Adequate | Significantly better |
| Multi-step coding | Good | Significantly better |
**Start with a standard model. Switch if needed.** For a new task, try a standard model like Claude Sonnet 4.6 or GPT-4o first. If the answer seems off, shallow, or the problem is genuinely complex, switch to a reasoning model and try again. This strategy saves quota without sacrificing quality when it matters.
## Viewing the thinking process
When a reasoning model is working on a problem, ZeroTwo displays a "Thinking..." indicator. Once complete, you can expand a **Thinking** section in the response to see the model's internal reasoning process — the steps it took, the approaches it considered, and how it arrived at its answer.
This is useful for:
* Verifying the model understood the problem correctly
* Learning from the model's problem-solving approach
* Catching reasoning errors before acting on the conclusion