How to Use GPT-OSS with Claude Code: Fast, Affordable AI Coding

Supercharge your API development with open-source AI models—right from the command line. This guide shows API engineers and backend teams how to connect GPT-OSS, OpenAI’s open-weight coding model, to Claude Code for fast, cost-efficient code generation, analysis, and more.

Whether you want a private self-hosted setup, a managed proxy, or seamless model switching, we’ll walk through three practical integration methods: Hugging Face, OpenRouter, and LiteLLM. Master these integrations and level up your coding workflow—while keeping costs low and flexibility high.

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Why Pair GPT-OSS with Claude Code?

GPT-OSS is an open-weight large language model from OpenAI (20B and 120B variants), designed for code, reasoning, and agentic tasks. With a 128K token context window under Apache 2.0, it’s highly flexible for developer teams who value freedom and control.

Claude Code, Anthropic’s CLI tool (v0.5.3+), is a favorite among API developers for its conversational, context-rich code generation. By routing Claude Code to GPT-OSS with an OpenAI-compatible API, you unlock:

• Powerful code generation and analysis
• Open-source flexibility—no vendor lock-in
• Lower operational costs (no Anthropic subscription required)
• Familiar, productive workflow for your whole backend team

Prerequisites: What You Need

Before starting, make sure you have:

• Claude Code v0.5.3 or later: Check with claude --version, install via pip install claude-code, or update with pip install --upgrade claude-code.
• Hugging Face account: For self-hosting (Path A). Create a token at huggingface.co (Settings > Access Tokens).
• OpenRouter API key: Optional, for proxy setup (Path B). Get yours at openrouter.ai.
• Python 3.10+ and Docker: Needed for local deployments or LiteLLM (Path C).
• Basic CLI skills: Comfort with environment variables and command line workflows.

Method 1: Self-Host GPT-OSS via Hugging Face (Maximum Control)

Host GPT-OSS privately using Hugging Face Inference Endpoints for full control over data, scaling, and cost.

Step 1: Select and Access the Model

• Go to Hugging Face and find openai/gpt-oss-20b or openai/gpt-oss-120b.
• Accept the Apache 2.0 license.
• For code-centric tasks and lighter hardware, consider Qwen/Qwen3-Coder-480B-A35B-Instruct (GGUF version).

Step 2: Deploy a Text Generation Inference Endpoint

• On the model page, click Deploy > Inference Endpoint.
• Choose the Text Generation Inference (TGI) template (v1.4.0+).
• Enable OpenAI compatibility (via the checkbox or --enable-openai in advanced settings).
• Select hardware:
  • A10G or CPU for 20B
  • A100 for 120B
• Launch your endpoint.

Step 3: Gather Credentials

• Once running, copy:
  • ENDPOINT_URL (e.g., https://<your-endpoint>.us-east-1.aws.endpoints.huggingface.cloud)
  • HF_API_TOKEN (from Hugging Face Settings > Access Tokens)
  • Model ID (e.g., gpt-oss-20b)

Step 4: Configure Claude Code

Set environment variables in your shell:

export ANTHROPIC_BASE_URL="https://<your-endpoint>.us-east-1.aws.endpoints.huggingface.cloud"
export ANTHROPIC_AUTH_TOKEN="hf_xxxxxxxxxxxxxxxxx"
export ANTHROPIC_MODEL="gpt-oss-20b"  # or gpt-oss-120b

Replace <your-endpoint> and hf_xxxxxxxxxxxxxxxxx with your actual values.

Test your setup:

claude --model gpt-oss-20b

Claude Code will now stream answers from your GPT-OSS endpoint.

Step 5: Cost & Scaling Considerations

• Hugging Face Inference Endpoints auto-scale—monitor to avoid unexpected charges.
  • A10G: ~$0.60/hr; A100: ~$3/hr; pause endpoints when idle.
• Local Option: Run TGI locally with Docker for zero cloud cost:

docker run --name tgi -p 8080:80 -e HF_TOKEN=hf_xxxxxxxxxxxxxxxxx ghcr.io/huggingface/text-generation-inference:latest --model-id openai/gpt-oss-20b --enable-openai

Set ANTHROPIC_BASE_URL="http://localhost:8080".

Method 2: Connect GPT-OSS via OpenRouter (Easiest Setup)

If you prefer a managed, no-DevOps approach, use OpenRouter to access GPT-OSS with minimal setup.

Step 1: Register & Choose Your Model

• Sign up at openrouter.ai and get your API key from the Keys section.
• Model slugs:
  • openai/gpt-oss-20b
  • openai/gpt-oss-120b
  • qwen/qwen3-coder-480b (for Qwen’s coding model)

Step 2: Configure Claude Code

Set environment variables:

export ANTHROPIC_BASE_URL="https://openrouter.ai/api/v1"
export ANTHROPIC_AUTH_TOKEN="or_xxxxxxxxx"
export ANTHROPIC_MODEL="openai/gpt-oss-20b"

Replace or_xxxxxxxxx with your API key.

Test with:

claude --model openai/gpt-oss-20b

Claude Code will connect to GPT-OSS via OpenRouter’s unified endpoint.

Step 3: Pricing Insights

• OpenRouter Rates: Around $0.50/M input tokens and $2.00/M output tokens for GPT-OSS-120B—much cheaper than GPT-4 or Claude Opus.
• Billing: Only pay for what you use, with no infrastructure to manage.

Method 3: Use LiteLLM for Multi-Model Flexibility

Need to switch between GPT-OSS, Qwen, and Anthropic models in one workflow? LiteLLM acts as a smart proxy for unified model access.

Step 1: Install & Configure LiteLLM

Install:

pip install litellm

Create litellm.yaml with your model list:

model_list:
  - model_name: gpt-oss-20b
    litellm_params:
      model: openai/gpt-oss-20b
      api_key: or_xxxxxxxxx
      api_base: https://openrouter.ai/api/v1
  - model_name: qwen3-coder
    litellm_params:
      model: openrouter/qwen/qwen3-coder
      api_key: or_xxxxxxxxx
      api_base: https://openrouter.ai/api/v1

Start the proxy:

litellm --config litellm.yaml

Step 2: Point Claude Code to LiteLLM

Set:

export ANTHROPIC_BASE_URL="http://localhost:4000"
export ANTHROPIC_AUTH_TOKEN="litellm_master"
export ANTHROPIC_MODEL="gpt-oss-20b"

Test:

claude --model gpt-oss-20b

LiteLLM will send requests to GPT-OSS or other models as configured.

Step 3: Tips & More

• Use simple-shuffle routing in LiteLLM to prevent model compatibility issues.
• LiteLLM logs usage for transparency.
• Learn how to call LLM APIs with OpenAI format using LiteLLM for advanced setups.

Real-World Example: Using GPT-OSS in Claude Code

Put your integration to the test:

Generate a Flask REST API:

claude --model gpt-oss-20b "Write a Python REST API with Flask"

Sample output:

from flask import Flask, jsonify
app = Flask(__name__)

@app.route('/api', methods=['GET'])
def get_data():
    return jsonify({"message": "Hello from GPT-OSS!"})

if __name__ == '__main__':
    app.run(debug=True)

Analyze a codebase:

claude --model gpt-oss-20b "Summarize src/server.js"

GPT-OSS will provide a concise summary using its 128K context window.

Debug code:

claude --model gpt-oss-20b "Debug this buggy Python code: [paste code here]"

With an 87.3% HumanEval pass rate, GPT-OSS excels at diagnosing and fixing issues.

Troubleshooting Common Issues

• 404 errors on /v1/chat/completions: Ensure OpenAI compatibility is enabled in TGI or check model availability in OpenRouter.
• Empty responses: Confirm the ANTHROPIC_MODEL value matches the model slug.
• 400 errors after model swap: Use simple-shuffle in LiteLLM.
• Slow initial response: Warm up endpoints with a small prompt after scaling to zero.
• Claude Code crashes: Update to v0.5.3+ and verify all environment variables.

The Benefits: Why API Teams Choose GPT-OSS + Claude Code

• Lower your costs: OpenRouter’s rates are a fraction of proprietary LLMs, and self-hosted setups are free after hardware.
• Open licensing: Apache 2.0 lets you customize and deploy privately.
• Enhanced productivity: Claude Code’s CLI and GPT-OSS’s performance (94.2% MMLU, 96.6% AIME) streamline coding workflows.
• Flexibility: Easily switch between GPT-OSS, Qwen, and Anthropic models using LiteLLM or OpenRouter.

Apidog users report significant productivity gains when integrating AI coding tools. For teams handling complex APIs, combining Claude Code with GPT-OSS keeps your workflow efficient, scalable, and cost-effective.

Conclusion: Start Building Smarter with GPT-OSS and Claude Code

You now have three proven ways to connect GPT-OSS with Claude Code—self-hosted, managed proxy, or hybrid model routing. Each path empowers API and backend engineers to automate code generation, debugging, and analysis with open-source AI.

Try integrating GPT-OSS today, experiment with code prompts, and share your results. For a unified platform that streamlines API design, testing, and collaboration, explore Apidog—trusted by teams who value speed, quality, and seamless documentation.

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