---
title: "Autohand Models - Fantail, Moa, and Recommended Coding Models"
source: https://docs.autohand.ai/models/
---

# Autohand models

Fantail and Moa are Autohand's first-party coding models. Use Fantail when latency matters. Use Moa when the task needs more context, planning, or repository-level reasoning. For hosted coding agents, Autohand also recommends a curated set of high-context open and partner models.

## Choose a model

| Model | Use it for | Context | Serving profile |
|---|---|---|---|
| Fantail | Completions, quick fixes, review comments, test scaffolds, and short agent loops. | 32K | Latency-first, low-cost coding assistance. |
| Moa | Large refactors, architecture work, security reviews, and multi-file planning. | 200K | Reasoning-first execution for complex software engineering tasks. |

## Recommended coding models

Use these models for coding agents, long-horizon repo work, tool-heavy sessions, and hosted team workflows.

| Model | Provider | Best for | Context | License | Top signal |
|---|---|---|---|---|---|
| Autohand Fantail Pro | Autohand | Fast coding loops, completions, reviews, and short agent tasks. | 32K | Hosted | Latency-first Autohand model |
| Autohand Moa | Autohand | Larger refactors, planning, and multi-step repository changes. | 200K | Hosted | Reasoning-first Autohand model |
| GLM-5.1 | Z.ai | Long-horizon agentic engineering. | 200K | MIT | Terminal-Bench 2.0 SOTA |
| GLM-5.2 | Z.ai | 1M-context long-horizon agentic engineering. | 1M | MIT | Terminal-Bench 2.1 81.0 |
| Sakana AI Fugu | Sakana AI | Balanced low-latency multi-agent coding and chat. | 1M | Orchestrated | SWE-Bench Pro 59.0 |
| Sakana AI Fugu Ultra | Sakana AI | Multi-agent orchestration across frontier models. | 1M | Orchestrated | SWE-Bench Pro 73.7 |
| Kimi K2.6 | Moonshot AI | Agent swarms and long autonomous runs. | 256K | Modified MIT | SWE-Bench Pro 58.6% |
| DeepSeek V4 Pro | DeepSeek | 1M-context coding and competitive programming. | 1M | MIT | LiveCodeBench 93.5 |
| Qwen3-Coder-Next | Alibaba Qwen | Efficiency per active parameter. | 256K | Apache 2.0 | SWE-Bench Verified 71.3 |
| Qwen3.6-27B | Alibaba Qwen | Dense repo-level coding. | 262K | Apache 2.0 | SWE-Bench Verified 77.2 |
| MiniMax M2.5 | MiniMax | Free hosted open-weight coding. | 200K | Open weights | Free hosted variant |
| GLM-5 | Z.ai | Local self-hosting foundations. | 200K | MIT | #1 Vending Bench 2 OSS |
| Devstral 2 | Mistral AI | Mistral coding workloads. | 256K | Apache 2.0 | SWE-Bench Verified 72.2 |
| Devstral Small 2 | Mistral AI | Consumer GPU coding workflows. | 128K | Apache 2.0 | SWE-Bench Verified 68% |
| Trinity Large Thinking | Arcee AI | US-origin open reasoning and long agent loops. | 128K | Apache 2.0 | #2 PinchBench |

## Use a model

Pass the model identifier where you start the run. The identifiers are `fantail` and `moa`.

## Fantail

Fantail is built for interactive coding where feedback speed matters. It is the best default for small, local edits and repeated assistant loops.

-   Best for autocomplete, quick bug fixes, code review notes, documentation snippets, and small tests.
-   Works well when the relevant files are already in the prompt or easy for the agent to inspect.
-   Use Moa instead when the task depends on broad architecture or a large amount of repository context.

[Read the Fantail technical report](/models/fantail/).

## Moa

Moa is built for deeper software engineering work. It has a larger context window and is a better fit when the model needs to reason across many files or constraints.

-   Best for large refactors, migration plans, security audits, technical design, and repository analysis.
-   Use it when the model needs to compare patterns across a codebase before making changes.
-   Switch back to Fantail for final polish, small follow-up edits, and fast review cycles.

[Read the Moa technical report](/models/moa/).

## Common patterns

-   **Start fast.** Use Fantail for the first pass when the task is scoped to a file, function, or failing test.
-   **Escalate for context.** Switch to Moa when the answer depends on architecture, cross-package contracts, or hidden coupling.
-   **Keep the model explicit.** Include the model in scripts and SDK calls so runs are repeatable.