Liberate your OpenClaw

The decision by Anthropic to restrict access to Claude models for Pro and Max plan subscribers within open agent platforms has triggered an immediate reaction from the open-source community. This change directly impacts users of popular tools such as OpenClaw, Pi, and Open Code, who relied on Anthropic's infrastructure to power their autonomous assistants. However, the response from Hugging Face is clear: closed ecosystems are not the only way, and alternatives based on open weights are currently not only efficient but also significantly cheaper to operate.

This situation sheds light on a broader problem in the AI industry — dependence on centralized API providers who can change their terms of service at any time. For developers and creative technology enthusiasts, "freeing" their OpenClaw is becoming not just a matter of convenience, but of technological sovereignty. Transitioning to open models offers two main paths: rapid cloud implementation via inference providers or full independence by running models locally.

Hugging Face infrastructure as an alternative to the Claude API

For users who want to restore the functionality of their agents without investing in powerful workstations, Hugging Face Inference Providers represents the most logical choice. It is an open platform that aggregates access to various open-source model providers, offering flexibility unattainable in closed subscription models. The key advantage of this solution is the speed of deployment — the migration process boils down to generating a token and changing the configuration in the terminal.

Deploying a new model in OpenClaw is done using a simple command: openclaw onboard --auth-choice huggingface-api-key. After entering the key, the user has thousands of models at their disposal; however, experts from Hugging Face point to one specific choice: GLM-5. This model stands out with excellent results in Terminal Bench tests, making it an ideal replacement for Claude in tasks related to coding and CLI handling. The configuration involves editing a JSON file:

• Primary model: huggingface/zai-org/GLM-5:fastest
• Subscriber bonus: HF PRO account holders receive $2 in free credits every month toward the use of Inference Providers.

Choosing the hosted path is optimal for those who need state-of-the-art (SOTA) performance without the need to manage their own hardware. It is a "plug-and-play" solution that eliminates the problem of being suddenly cut off from services by major players like Anthropic.

Local control thanks to llama.cpp and Qwen3.5

For those who prioritize privacy and zero operational costs, the only right path is to run the model locally. Utilizing the llama.cpp library allows for running advanced models even on hardware with limited resources. It is a fully open-source solution, available for macOS, Linux (via brew install llama.cpp), and Windows (using winget install llama.cpp).

In the context of agentic work, a particularly recommended model is Qwen3.5-35B-A3B in GGUF format (specifically version unsloth/Qwen3.5-35B-A3B-GGUF:UD-Q4_K_XL). This specific variant is optimized for machines equipped with 32GB of RAM, which is becoming the standard in professional laptops and workstations. Running a local server compatible with the OpenAI API allows for seamless integration with OpenClaw without sending any data to external clouds.

Local configuration requires a bit more attention during the first run but rewards the user with zero network latency and no rate limits. An example command to initialize OpenClaw in local mode is as follows:

openclaw onboard --non-interactive --auth-choice custom-api-key --custom-base-url "http://127.0.0.1:8080/v1" --custom-model-id "unsloth-qwen3.5-35b-a3b-gguf" --custom-api-key "llama.cpp" --secret-input-mode plaintext --custom-compatibility openai

Performance Analysis: Do open models match Claude?

Switching from the Claude model to GLM-5 or Qwen3.5 is not just a compromise forced by licensing restrictions. Analysis of technical benchmark results indicates that in specific tasks, such as system file manipulation or code generation within OpenClaw agents, these models perform surprisingly well. GLM-5 was designed with terminal interactions in mind, which directly translates to fewer errors in script execution by the agent.

The economic aspect is also worth noting. While Claude Pro subscriptions are burdened with rigid limits and high monthly costs, models hosted on Hugging Face are billed based on actual usage, which typically represents a fraction of the Anthropic subscription price. On the other hand, a local model, after the initial hardware purchase cost, generates zero cost. For developers building complex workflows where an agent makes hundreds of calls per day, the difference in costs becomes a key factor in project scalability.

Limitations of open models may appear in the case of very long contexts or specific, rare programming languages where Claude still maintains a slight edge. However, for 90% of OpenClaw applications, models like Qwen3.5 offer sufficient precision so that the user does not experience a degradation in the quality of their assistant's work.

The AI tools market is evolving toward diversification. Anthropic's move, while disruptive for users, may paradoxically accelerate the adoption of local and open solutions. Developers who decide to migrate toward Hugging Face or llama.cpp today are building the foundations for more resilient and independent systems. The era of relying on a single, closed API provider is slowly coming to an end, giving way to an ecosystem where the user decides where and how their data is processed.