Best AI Agent Models for Android: Tool APIs and GUI Specialists

Choose a Model by Phone Task

The best AI agent model depends on the work it must plan and the route that will carry out that plan. For an Android phone agent, first decide whether the task uses named tools, such as a calendar or memo action, or requires interpreting a screen and navigating its controls. Those are different integration jobs. The model also needs an available endpoint, a compatible protocol, and an executor that can perform the intended action on the device.

This is a task-fit shortlist, not a speed or success-rate ranking. For everyday FoneClaw use, the free default model is a practical starting point. Builders evaluating another model should confirm the exact endpoint and action contract before changing the route.

General Models for Tool-Based Agents

General API models can reason about a request, accept supported context, and return a tool call or structured response. The phone-agent runtime still defines available tools and performs actions. A model's consumer chat app, API endpoint, and Android executor are separate products, so availability in one does not establish behavior in the others.

GPT-6 Sol. OpenAI lists Sol among its current model choices as a balanced intelligence and cost option. The gpt-6-sol API page documents image input, function calling, and structured outputs. Built-in tools and function calling use the Responses API. If an integration uses Chat Completions, function calling requires reasoning_effort set to none for this model. That protocol detail matters more to a phone-agent connector than a general description of tool support.

Claude Opus 5.5. Anthropic's Opus 5.5 announcement identifies the API model as claude-opus-5-5. Its model documentation describes vision and tool use and positions Opus 5.5 for long-running agentic coding and knowledge work. It is a candidate for workflows that need sustained planning and careful instruction handling. Its native Messages API needs an appropriate connector; a model name alone does not make that API interchangeable with another provider's protocol.

Gemini 3.8 Flash. Google's stable gemini-3.8-flash endpoint accepts text, image, video, audio, and PDF input and returns text. Function calling and structured outputs are supported, while computer use is listed as preview. This exact endpoint does not provide the Live API or audio generation. Those belong to separate model routes, and consumer Gemini phone features should not be assumed to work through this API.

Grok 4.7. The grok-4.7 API documentation lists text and image input, text output, function calling, structured output, and configurable reasoning. Those make it a candidate to evaluate for tasks that combine visual context with explicit tools. Confirm the account's endpoint access and test actual tool arguments before using it for an Android action.

All four have provider-documented capabilities. None of those documents proves that a particular model is already configured in FoneClaw or that a cloud benchmark predicts success on your handset. The practical question is whether the available API route produces valid calls for the tools your runtime exposes.

Models Built for Screen Interaction

A GUI specialist starts from a different kind of input: what is visible on a screen and which interface action should come next. That can help with tasks whose controls are not available as named tools, but the model's proposed tap or swipe still needs a device connection, an executor, and checks after the screen changes.

AutoGLM-Phone-9B-Multilingual is an open phone-GUI model with multimodal screen understanding, described in its model card. The related Open-AutoGLM Phone Agent framework connects to devices through ADB and handles action execution, sensitive-action confirmation, and human fallback for login or verification. Its deployment guide describes hosted services and local model serving with vLLM. Serving on your own computer or server is not the same as running the weights on the Android phone.

GUI-Owl-1.5 is a multiplatform GUI model family with open weights for mobile, desktop, and browser tasks. The MobileAgent repository distinguishes the model from Mobile-Agent-v3.5, its companion agent system, and links online inference and cloud-phone demonstrations. An available GUI-Owl-1.5-8B-Instruct checkpoint provides weights, while serving requirements, action schemas, and a device executor remain engineering work. A cloud-phone demonstration does not establish control of your own handset.

These specialist routes deserve consideration when screen interaction is the core task. They are not drop-in substitutes for a general tool-calling API, and their weights do not establish direct FoneClaw model compatibility.

Seven Practical Selection Criteria

Assess each candidate against these seven criteria for the task and endpoint you will deploy:

1. Tool calling: Does the model select a supported tool and supply accurate arguments, including names, dates, and target accounts?
2. Instruction following: Does it stay within the request and pause when a required detail is missing?
3. Latency: How long does the complete device workflow take, including tools, network calls, and retries?
4. Context discipline: Does it keep track of the current screen and task state without treating an old observation as current?
5. Multimodality: Can the endpoint accept the image, audio, or document input the workflow actually needs? Vision support matters when a screenshot is essential.
6. Cost: What do model calls, tools, hosting, and repeated attempts cost for the whole task?
7. Recovery: What happens after permission denial, an app change, interruption, or an invalid result?

Account, region, and endpoint availability determine which candidates can enter the test at all. Fluent prose does not demonstrate correct tool selection, and a GUI model's screen-reading ability does not supply an action executor.

Compare Cost and Response Time

Provider pages give a starting price, not the full cost of a phone task. OpenAI's Sol model page, Anthropic's Opus 5.5 announcement, Google's Gemini model page, and xAI's Grok model page are the appropriate places to confirm current rates and terms. Token charges can change with long context, caching, reasoning modes, service tiers, regional processing, and tool use. Open-weight routes add serving hardware and operations costs.

For a fair comparison, record the full time from request to verified phone result on the same device and network. Include failed calls and retries in both time and cost. A provider's reported model speed or cloud benchmark cannot substitute for that end-to-end result. If repeated tasks need different model strengths, Phone Agent Model Routing: Kimi, DeepSeek, GLM, Cost, and Android Actions explains how to evaluate routing without assuming one model should handle every request.

Choose a FoneClaw Model Route

In FoneClaw, the configured model supplies reasoning and plans; our runtime governs supported Android tools with the permissions and approvals those actions require. There are three distinct starting routes: use the free default model, configure a compatible online custom model, or import a compatible on-device model. The FoneClaw Features page describes these choices and the supported phone-tool surface.

For a custom online route, confirm that the provider account grants API access, the endpoint protocol matches the connector, and the model returns tool calls in the required schema. A subscription to a consumer assistant does not itself provide API entitlement. For an on-device route, check model format, memory, performance, and the supported import path on the actual phone. Running a model server on your own computer is a different deployment choice from on-device inference. In either case, local Android tool execution does not mean every model request stays offline.

The six candidates above are options to investigate, not a list of verified FoneClaw integrations. Start with the default path for routine supported work; change the model only when a specific task justifies the setup and has passed a device-level check. Connect an AI Model API to an Android Phone Agent in FoneClaw covers the configuration steps for a compatible API route. The FoneClaw Download page provides the current Android installation choices.

Test Before Changing Your Default

Use a small, reversible set of tasks on the phone that will run the agent. Begin with a read-only question about visible state and a draft that is not sent. Then try one supported action whose result you can inspect. Deliberately deny a permission in a separate run, interrupt another request, and ask the model to report what actually changed.

Repeat the set with the same account, region, network, and device conditions for each candidate. Note task completion, retries, elapsed time, and total cost together. Android Phone Agent Benchmark Guide: Reliability, Safety, and Task Success gives a fuller method for comparing phone workflows after this initial check.