> ## Documentation Index
> Fetch the complete documentation index at: https://draftify.site/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Models

Draftify offers a tier-based model system designed to give you the perfect balance of speed, reasoning capability, and cost-\\\
efficiency. Instead of worrying about underlying provider names, you target semantic tiers, and our intelligent router ensures\\\
the best results.

## Available Models

### Sonnet 4.6-level

The fastest and most cost-effective model, perfect for general-purpose tasks, frontend code generation, and rapid text processing.

* **Model ID**: `sonnet-4.6-level`
* **Pricing**: €0.20 per 1M input tokens / €0.74 per 1M output tokens
* **Best for**: Chatbots, quick content generation, and simple logic.

### Opus Tier (4.6, 4.7, 4.8)

A highly capable, balanced reasoning tier that excels at complex backend logic, algorithmic problem-solving, and architectural\\\
design. You can select different version levels based on your exact precision and budget needs.

* **Opus 4.8-level** (Highest capability)
  * **Model ID**: `opus-4.8-level`
  * **Pricing**: €2.00 per 1M input tokens / €5.00 per 1M output tokens
* **Opus 4.7-level** (Balanced)
  * **Model ID**: `opus-4.7-level`
  * **Pricing**: €1.00 per 1M input tokens / €3.00 per 1M output tokens
* **Opus 4.6-level** (Efficient)
  * **Model ID**: `opus-4.6-level`
  * **Pricing**: €1.00 per 1M input tokens / €2.00 per 1M output tokens
* **Best for**: Complex reasoning, backend development, and deep logical analysis.

### Fable 5-level

Our most advanced reasoning engine, designed for agentic, long-running, and complex multi-step tasks.

* **Model ID**: `fable-5-level`
* **Pricing**: €2.00 per 1M input tokens / €6.00 per 1M output tokens
* **Best for**: Agent workflows, long and complex tasks, deep architectural reasoning, and extreme edge-case handling.

#### `reasoning_effort` parameter

Fable 5-level accepts an optional `reasoning_effort` tag that controls how much "thinking budget" the model spends before producing the final answer:

```json theme={null}
"reasoning_effort": "xhigh"
```

Supported values:

* **`high`** — Recommended for most demanding workloads: agent loops, long-context synthesis, multi-file refactors, and tasks where the model needs to keep track of many moving parts. Strong reasoning at a predictable latency.
* **`xhigh`** — Reserved for the *hardest* problems where `high` plateaus. The model runs an extended reasoning pass, exploring multiple solution branches, self-verifying intermediate steps, and backtracking when needed.

**When `xhigh` actually shines (and `high` falls short):**

Imagine you hand the model a 40,000-line legacy monorepo with a subtle, intermittent race condition that only triggers under a specific combination of three async event handlers, a stale cache entry, and a misconfigured retry policy spread across 7 files. You ask it to: *find the root cause, prove why it happens, write a minimal failing test, propose three architecturally different fixes with trade-offs, and refactor the affected modules without breaking the 1,200 existing tests.*

* With `high`, the model will usually identify one plausible cause and ship a working patch — but it may miss the interaction with the retry policy and pick the first viable fix.
* With `xhigh`, the model traces the full causal chain across all 7 files, explicitly rules out two red-herring hypotheses, produces the failing test on the first try, and presents the three fixes ranked by blast radius — with the trade-offs you'd expect from a senior engineer who actually read the whole codebase.

Use `xhigh` when correctness, completeness, and depth matter more than latency or cost. Stick with `high` for everything else.

***