GitHub Models is a free developer platform that provides rate-limited access to industry-leading large language models directly within GitHub. Launched on August 1, 2024, the service enables more than 100 million developers to experiment with AI models including GPT-4o, Llama 3.3, DeepSeek-R1, and Microsoft’s Phi-4 without requiring separate API keys or upfront payment. The platform bridges the gap between experimentation and production by offering a built-in playground for prompt testing, side-by-side model comparisons, and a seamless migration path to Azure AI for scaled deployments.
What is GitHub Models?
GitHub Models is a suite of developer tools designed to integrate AI model experimentation directly into existing GitHub workflows. According to GitHub’s official documentation, the platform provides “a workspace lowering the barrier to enterprise-grade AI adoption” by embedding AI development tools into the same environment where developers already manage their source code[^1].
The platform serves multiple functions within a unified interface:
- Model Catalog: A curated selection of models from leading providers including OpenAI, Meta, Microsoft, Mistral AI, DeepSeek, and xAI
- Interactive Playground: A browser-based environment for testing prompts and adjusting model parameters in real-time
- Prompt Management: Version-controlled prompt configurations stored as
.prompt.ymlfiles in repositories - Evaluation Tools: Structured metrics for comparing model outputs including similarity, relevance, and groundedness scores
- REST API: Programmatic access to models for integration into applications and CI/CD pipelines[^2]
GitHub emphasizes that “no prompts or outputs in GitHub Models will be shared with model providers, nor used to train or improve the models”[^3], addressing privacy concerns that often deter developers from experimenting with third-party AI services.
How Does GitHub Models Work?
Developers access GitHub Models through github.com/marketplace/models, where they can browse available models and launch them in the interactive playground. The workflow follows a progression from experimentation to production.
Free Tier and Rate Limits
All GitHub accounts receive rate-limited access to GitHub Models at no cost. These limits vary by model and are designed specifically to support prototyping and experimentation rather than production workloads[^4]. As of February 2025, GitHub Models remains in public preview, and rate limits are subject to change as the service evolves.
The free tier includes:
- Access to all supported models in the catalog
- Rate-limited requests per model
- Usage through both the GitHub Marketplace catalog and direct API calls
- Side-by-side model comparison capabilities
Model Comparison and Evaluation
A distinguishing feature of GitHub Models is the ability to compare multiple models simultaneously. Developers can submit identical prompts to two different models and view responses side-by-side, making it easier to evaluate trade-offs between accuracy, speed, and cost[^5].
The Comparisons view supports structured evaluation workflows where developers can:
- Test multiple prompt configurations against consistent input datasets
- Apply automated evaluators including string matching, semantic similarity, and custom LLM-as-a-judge criteria
- Track performance metrics across different model and parameter combinations
- Export results for team review and decision documentation[^6]
Prompt Version Control
GitHub Models treats prompts as first-class development artifacts. When developers create prompts in the playground, they can save configurations as .prompt.yml files directly in their repositories. These files capture the model selection, system instructions, parameter settings, and test inputs—enabling version control, pull request workflows, and collaborative refinement[^7].
This approach addresses a common pain point in AI development: the lack of reproducibility in prompt engineering. By storing prompts alongside application code, teams can track changes, review modifications through standard Git workflows, and roll back to previous versions when needed.
Which Models Are Available?
As of February 2025, GitHub Models provides access to dozens of models across multiple providers. The catalog includes both commercial and open-weight models spanning different capabilities and price points.
Available Model Categories
| Provider | Notable Models | Primary Use Cases |
|---|---|---|
| OpenAI | GPT-4o, GPT-4o mini, GPT-4.1 | General reasoning, multimodal applications, coding tasks |
| Meta | Llama 3.3 70B Instruct, Llama 4 Maverick | Open-weight general purpose, cost-effective deployment |
| Microsoft | Phi-4, Phi-4 mini, Phi-4 multimodal | Lightweight reasoning, edge deployment, multilingual |
| DeepSeek | DeepSeek-R1, DeepSeek-V3 | Advanced reasoning, mathematical tasks, coding |
| Mistral AI | Mistral Large 2 | High-performance European alternative |
| xAI | Grok 3, Grok 3 Mini | Extended context, real-time information |
| Cohere | Command R+ | Enterprise retrieval and generation |
Billing and Pricing Structure
For usage beyond the free tier, GitHub Models employs a unified token-based billing system. Rather than managing separate billing relationships with each provider, developers pay GitHub directly using standardized “token units” calculated by applying multipliers to input and output tokens[^8].
The following table shows representative pricing for direct GitHub Models usage (beyond free tier):
| Model | Input Price (per 1M tokens) | Output Price (per 1M tokens) |
|---|---|---|
| GPT-4o | $2.50 | $10.00 |
| GPT-4o mini | $0.15 | $0.60 |
| GPT-4.1 | $2.00 | $8.00 |
| Llama 3.3 70B | $0.71 | $0.71 |
| DeepSeek-R1 | $1.35 | $5.40 |
| Phi-4 | $0.13 | $0.50 |
| Grok 3 | $3.00 | $15.00 |
For comparison, direct OpenAI API pricing for GPT-4o is $2.50 per million input tokens and $10.00 per million output tokens[^9], indicating that GitHub Models maintains competitive parity with direct provider pricing while adding the convenience of unified billing and integrated tooling.
Why Does GitHub Models Matter?
The introduction of GitHub Models represents a strategic shift in how developers interact with AI infrastructure. Several factors make this platform significant for the broader AI development ecosystem.
Lowering Barriers to Experimentation
Before GitHub Models, developers wishing to compare multiple AI models needed to create separate accounts with each provider, manage multiple API keys, and implement custom evaluation frameworks. GitHub Models consolidates this fragmented landscape into a single interface where experimentation requires only a GitHub account—something most developers already possess.
Harvard’s CS50 course adopted GitHub Models for the Fall 2024 semester, enabling students to experiment with AI without managing individual API credentials[^10]. This educational adoption signals the platform’s accessibility for newcomers to AI development.
Integration with Existing Workflows
GitHub Models embeds AI development directly into established software engineering practices. By storing prompts as YAML files in repositories, teams can apply the same review processes, version control, and collaboration patterns they use for application code. The gh models eval command-line interface further extends this integration by enabling automated evaluation in CI/CD pipelines[^11].
This tight integration addresses what GitHub identifies as a key challenge: moving “beyond isolated experimentation by embedding AI development directly into familiar GitHub workflows”[^12].
Production Migration Path
While GitHub Models excels at prototyping, it also provides a clear path to production deployment. The platform uses Azure AI Inference SDK for model access, meaning code developed against GitHub Models requires minimal modification when migrating to Azure AI for scaled production workloads[^13].
Azure AI offers enterprise-grade features including provisioned throughput units, availability across 25+ Azure regions, and compliance certifications that GitHub Models’ free tier is not designed to provide. This architectural continuity—from free experimentation to paid production—reduces friction in AI application deployment.
Prompt Testing Workflows with GitHub Models
Effective prompt engineering requires systematic iteration and evaluation. GitHub Models supports several workflows for refining AI interactions.
Playground-Based Iteration
The Model Playground provides immediate feedback for prompt refinement. Developers can adjust parameters including temperature (controlling output randomness), maximum token length, and system instructions while observing real-time responses. The Code tab automatically generates corresponding API calls in Python, JavaScript, or raw HTTP, facilitating the transition from experimentation to implementation[^14].
Structured Evaluation
For teams requiring quantitative comparison, the Comparisons view supports test-driven prompt development. Developers define input datasets, apply multiple prompt configurations, and receive scored evaluations across configurable criteria. This structured approach helps prevent regressions when modifying prompts and provides objective metrics for model selection decisions.
Team Collaboration
Because prompts are stored as .prompt.yml files in repositories, they benefit from GitHub’s native collaboration features. Team members can propose prompt improvements through pull requests, review changes with diffs, and maintain a history of modifications. The natural language prompt editor enables non-technical stakeholders to contribute to prompt refinement within a familiar interface[^15].
Limitations and Considerations
While GitHub Models offers significant value for prototyping, developers should understand its constraints:
- Rate Limiting: The free tier is explicitly designed for experimentation, not production traffic. Applications requiring consistent availability or high throughput must migrate to paid usage or direct provider APIs.
- Public Preview Status: As of February 2025, GitHub Models remains in public preview, meaning features and rate limits may change without extensive notice.
- Geographic Availability: Access to specific models may vary by region due to provider restrictions and regulatory requirements.
- Enterprise Controls: Organizations can restrict which models are available to their teams, and enterprises must explicitly enable paid usage before teams can opt into billing[^16].
Frequently Asked Questions
Q: Is GitHub Models completely free? A: GitHub Models offers free, rate-limited access for prototyping. All GitHub accounts can experiment with models at no cost, but usage beyond specified rate limits requires opting into paid billing.
Q: How does GitHub Models compare to using OpenAI’s API directly? A: GitHub Models provides the same models at competitive pricing while adding integrated tooling for comparison, evaluation, and prompt version control. Direct API access may be preferable for production applications requiring guaranteed throughput.
Q: Can I use GitHub Models for commercial applications? A: Yes, though the free tier’s rate limits are designed for prototyping. Commercial applications should migrate to paid GitHub Models usage or Azure AI for production workloads.
Q: Do I need an Azure account to use GitHub Models? A: No, GitHub Models works with your existing GitHub account. An Azure account is only required when migrating to Azure AI for scaled production deployment.
Q: Are my prompts and outputs used to train AI models? A: No. According to GitHub’s privacy commitments, prompts and outputs in GitHub Models are not shared with model providers nor used to train or improve the models.