There is no defensible universal winner among API providers for custom tool calling. The right fit depends on who executes each tool, which runtime you want to use, and how much orchestration your application should own. OpenAI, Anthropic’s Claude platform, Google’s Gemini API, and Mistral AI Studio all document ways to connect models or agents to tools, but their documented capabilities do not establish which is faster, cheaper, safer, or more reliable for your workload.
What to compare before choosing a provider
A tool call is a request from a model to perform an action, such as looking up an order or running a calculation. With an application-owned tool, the model proposes a call, your application executes it, and the application returns the result so the model can continue. That division of work matters as much as the tool-definition format.
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- Definition format: Check whether the API accepts JSON-schema function definitions, free-form input, or another provider-specific representation.
- Execution ownership: Determine whether your application runs the handler, the provider runs a server tool, or a managed built-in tool performs the task.
- Runtime surface: Compare the direct model API, agent API, SDK, and any streaming interface you actually plan to deploy.
- Call controls: Confirm how the API handles automatic tool selection, required calls, forced functions, or restricting the model to an allowed set.
- Orchestration: Account for sequential or parallel calls, continuation after results, streaming, error handling, retries, and recovery.
These are documented integration differences, not comparative performance results. The available provider documentation does not give a consistent basis for ranking pricing, quotas, regional availability, or contractual terms.
Recommended Free Tools
How the documented options differ
| Provider | Documented tool approach | Useful distinction to assess |
|---|---|---|
| OpenAI API | JSON-schema function tools and custom tools with free-form text input and output; documentation also covers built-in capabilities and remote MCP. | There are multiple integration surfaces to evaluate, including the Responses API, Agents API, and Agents SDK patterns. Select based on the runtime you intend to use. |
| Anthropic Claude Platform | Client tools handled by your application, and server tools that run on Anthropic infrastructure. | The client/server distinction helps determine where tool handlers and execution should live. |
| Google Gemini API | Custom function calling alongside managed built-in tools. | The tools guide describes request-response and Live API streaming interactions; check support for the exact model and API path you plan to use. |
| Mistral AI Studio | Agent tools for external services, including custom local functions. | Consider whether its documented agent-tool interface fits your desired local execution arrangement. |
The table summarizes capabilities described in provider documentation, not hands-on testing or an exhaustive benchmark. It does not establish equivalent feature coverage across all models or plans.
#1 Best Overall
- The NVIDIA Jetson AGX Orin 64GB Developer Kit makes it easy to get started with Jetson Orin. Compact size, lots of connectors, and up to 275 TOPS of AI performance make this developer kit perfect for prototyping advanced AI-powered robots and other autonomous machines.
- The developer kit includes a Jetson AGX Orin 64GB module, and can emulate all the Jetson Orin modules. It supports multiple concurrent AI application pipelines with the NVIDIA Ampere GPU architecture, next-generation deep learning and vision accelerators, high-speed IO and fast memory bandwidth. Now you can develop solutions using your largest and most complex AI models to solve problems such as natural language understanding, 3D perception, and multi-sensor fusion.
- Jetson runs the NVIDIA AI software stack, and use-case specific application frameworks are available, including Isaac for robotics, DeepStream for vision AI, and Riva for conversational AI. You can save significant time with NVIDIA Omniverse Replicator for synthetic data generation (SDG), and by using NVIDIA TAO toolkit to fine-tune pretrained AI models from the NGC catalog.
- Jetson ecosystem partners offer additional AI and system software, developer tools, and custom software development. They can also help with cameras and other sensors, as well as carrier boards and design services for your product.
- With the computing capability of more than 8 Jetson AGX Xavier systems in a developer kit that integrates the latest NVIDIA GPU technology with the world’s most advanced deep learning software stack, you’ll have the flexibility to create tomorrow’s AI solution as well as today’s.
Which execution model fits your application?
Keep execution in your application
Application-owned tools are a natural fit when your service needs to control the handler, access its own systems, and decide what happens with results and failures. The model supplies a requested action; your software remains responsible for validating and executing it. This pattern makes the application’s tool loop and error behavior part of the integration you must implement and test.
Use provider-run or managed tools when they match the task
Server tools and managed built-ins can shift some execution away from your application. Anthropic documents server tools, while Google documents managed built-in tools. That changes the operational boundary: establish which component performs the work and what your application still needs to manage. Do not assume a managed option is available for every model, workflow, region, or account arrangement; verify the current documentation for the specific configuration.
Rank #2
- POWERFUL PROCESSOR: Equipped with the Allwinner A733 octa-core CPU, delivering fast and efficient performance for a wide range of computing tasks.
- AI CAPABILITY: Features a built-in 3 TOPS NPU, enabling on-device artificial intelligence and machine learning applications with impressive processing power.
- COMPACT DESIGN: Pocket-sized single-board computer form factor makes it ideal for embedded projects, prototyping, and space-constrained deployments.
- VERSATILE CONNECTIVITY: Onboard interfaces include GPIO headers, USB ports, and networking options to support a broad variety of peripherals and project needs.
- ONBOARD STORAGE: Includes eMMC flash storage for fast, reliable read and write speeds, providing a stable foundation for your operating system and applications.
How to make a practical choice
- Write down the tool contract. List each tool’s inputs, outputs, side effects, and failure cases. Decide whether strict schema validation or free-form text is appropriate for each interface.
- Set the execution boundary. Choose which actions must run in your application and whether any provider-managed tools meet the workflow’s needs.
- Choose the runtime you will ship. Identify the intended direct API, agent API, SDK, or streaming interaction, then confirm its tool support rather than inferring compatibility from another surface.
- Test the actual target models. Run a small compatibility test with representative tools. Check schema acceptance, required and allowed tool behavior, result handling, error returns, and whether your application must manage continuation or streaming.
- Evaluate the whole workflow. Measure correctness, completeness, evidence coverage, token use, latency, cost, model turns, retries, recovery, safety outcomes, and whether the executed route matched the intended workflow. These are evaluation dimensions, not published comparative results for the providers above.
- Verify deployment constraints. Before production selection, check the exact model’s current support, request format, call limits, pricing, rate limits, regional access, data retention, and contractual terms in current official materials.
What the available documentation cannot settle
Provider documentation can show the integration patterns a provider describes, but it cannot by itself determine the best fit for a particular workload. The documentation comparison here does not establish current relative prices, quotas, regional availability, reliability, or contract terms consistently across providers. Those details can depend on the target model and deployment, so verify them directly before committing.
Likewise, a provider’s support for a tool-calling feature does not guarantee that every model or runtime supports the same controls. Treat model-specific compatibility as a requirement to test, not as an assumption based on the provider name.
Quick Recap
Rank #3
- 【Core Parameters】★AI Perf:34-67 TOPS ★GPU:512-core NVIDIA Ampere architecture GPU with 16 Tensor Cores ★CPU:6-core Arm Corte-A78AE v8.2 64-bit CPU 1.5MB L2 + 4MB L3 ★Memory:4GB 64-bit LPDDR5 51 GB/s ★Storage: external NVMe via M.2 Key M (NOTE:SUB Board No SD Card Slot)
- 【Empowered by Large Al Model, Enhanced Human-Computer Interaction】Jetson Orin Super leverages three AI models and incorporates an AI voice interaction module. This multimodal visual system matches the scene being described, enabling environmental awareness and AI visual gameplay. Combined with a large-scale voice module and camera, it enables speech-to-text, semantic analysis, natural conversation, and real-time video analysis, enabling advanced embodied AI applications.
- 【AI Upgrade】Jetson Orin Nano series modules are compact in size but can deliver up to 34-67 TOPS of AI performance, with power consumption ranging from 7 watts to 25 watts. Compared to the Jetson Nano B01, it offers up to 80 times the performance and sets a new standard for entry-level edge AI.
- 【Highly compatible carrier board】Yahboom's carrier board is fully compatible with orin nano module. Compared to carrier boards that use Jetson Nano on the market, the newly upgraded circuit supports 25W power mode, which enables larger and more complex neural networks and fully leverages the performance of the core module. The resources, size, and interfaces of the Yahboom carrier board are consistent with the official board, with the only difference addition of power switch button.
- 【Tutorial materials provided】The JETSON system based on Ubuntu 22.04 provides a complete desktop Linux environment with accelerated graphics, supporting NVIDI-ACUDA 12.6, TensorRT 10.7.0, cuDNN 9.6.0, OpenCV 4.10.0, etc. The performance on AI LLM, VLM and visual Transformer is significantly improved compared with the previous generation.
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