---
title: "awesome-free-llm-apis vs vllm-mlx"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/mnfst-awesome-free-llm-apis-vs-waybarrios-vllm-mlx"
tools: ["mnfst-awesome-free-llm-apis", "waybarrios-vllm-mlx"]
---

# awesome-free-llm-apis vs vllm-mlx

*GraphCanon updated Sep 20, 2026*

## Verdict

Pick awesome-free-llm-apis if awesome-free-llm-apis curates permanent free Large Language Model APIs with notable details on their limitations and access methods; pick vllm-mlx if vllm-mlx is an open-source inference server that runs large language models and vision-language models on Apple Silicon devices with continuous batching and multimodal support using native MLX backend.

[awesome-free-llm-apis](https://github.com/mnfst/awesome-free-llm-apis) reports 7.9k GitHub stars, 746 forks, and 17 open issues, last pushed Aug 21, 2026. [vllm-mlx](https://vllm-mlx.is-a.dev/) has 1.6k stars, 222 forks, and 121 open issues, last pushed Sep 19, 2026. Figures are from public GitHub metadata via [awesome-free-llm-apis's repository](https://github.com/mnfst/awesome-free-llm-apis) and [vllm-mlx's repository](https://github.com/waybarrios/vllm-mlx).

| | [awesome-free-llm-apis](/tools/mnfst-awesome-free-llm-apis.md) | [vllm-mlx](/tools/waybarrios-vllm-mlx.md) |
| --- | --- | --- |
| Tagline | List of Permanent Free LLM API | Server for LLMs and vision-language models compatible with Apple Silicon |
| Stars | 7,852 | 1,588 |
| Forks | 746 | 222 |
| Open issues | 17 | 121 |
| Language | JavaScript | Python |
| Adopt for | awesome-free-llm-apis curates permanent free Large Language Model APIs with notable details on their limitations and access methods. | vllm-mlx is an open-source inference server that runs large language models and vision-language models on Apple Silicon devices with continuous batching and multimodal support using native MLX backend. |
| Persona | - | - |
| Runtime | - | - |
| License | This resource is distributed under the Creative Commons Zero v1.0 Universal Public Domain Dedication (CC0-1.0) license. | Apache-2.0 |
| Categories | Inference & Serving | Inference & Serving, Model Training |

## Trust and health

_Sourced signals - not a safety guarantee. No winner column._

| | [awesome-free-llm-apis](/tools/mnfst-awesome-free-llm-apis.md) | [vllm-mlx](/tools/waybarrios-vllm-mlx.md) |
| --- | --- | --- |
| Maintenance | Active (82%) | Very active (96%) |
| Days since push | 29d | 0d |
| Open issues (now) | 17 | 121 |
| Stars delta | +1.3k (30d) | +116 (30d) |
| Open issues delta | -8 (30d) | +35 (30d) |
| Owner type | Organization | User |
| Full report | [trust report](/tools/mnfst-awesome-free-llm-apis/trust.md) | [trust report](/tools/waybarrios-vllm-mlx/trust.md) |

## Decision facts: awesome-free-llm-apis

- **Pricing:** freemium - The repository lists APIs with permanent free tiers, detailing rate and token limits for those interested in using free Large Language Model services.
- **Requirements:** Before integration, ensure that the specific requirements for API access are met to utilize these models without restriction.; Users should review the rate and token limits associated with each API provider to determine suitability based on their project's needs.
- **Adopt for:** awesome-free-llm-apis curates permanent free Large Language Model APIs with notable details on their limitations and access methods.
- **License detail:** This resource is distributed under the Creative Commons Zero v1.0 Universal Public Domain Dedication (CC0-1.0) license.

## Decision facts: vllm-mlx

- **Adopt for:** vllm-mlx is an open-source inference server that runs large language models and vision-language models on Apple Silicon devices with continuous batching and multimodal support using native MLX backend.

## Choose when

### Choose awesome-free-llm-apis if…

- awesome-free-llm-apis is primarily JavaScript; vllm-mlx is Python.
- License: awesome-free-llm-apis is CC0-1.0, vllm-mlx is Apache-2.0.
- Pricing: The repository lists APIs with permanent free tiers, detailing rate and token limits for those interested in using free Large Language Model services..
- Requirements: Before integration, ensure that the specific requirements for API access are met to utilize these models without restriction.; Users should review the rate and token limits associated with each API provider to determine suitability based on their project's needs..
- Tags unique to awesome-free-llm-apis: ai-agents, gemini, ollama, openai.
- When you need text inference with specialized capabilities like roleplay and storytelling, the Aion Labs API offers a dedicated free tier specifically designed for these purposes.

### Choose vllm-mlx if…

- vllm-mlx is primarily Python; awesome-free-llm-apis is JavaScript.
- License: vllm-mlx is Apache-2.0, awesome-free-llm-apis is CC0-1.0.
- Tags unique to vllm-mlx: apple-silicon, audio-processing, claude-code, computer-vision.
- Also covers Model Training.
- If you need to run LLMs or vision-language models like Llama, Qwen-VL, and LLaVA efficiently on Apple Silicon devices.

## When NOT to use awesome-free-llm-apis

- Avoid using Aion Labs if your project involves heavy commercial activity, given its limited rate and token limits.
- Do not use Cohere for projects that will scale beyond 1,000 API calls per month or require credit card details for access to higher-tier plans.
- Google Gemini's free tier is inaccessible in the EU/UK/Switzerland regions and requires consent to let Google improve their products through your usage.
- Mistral AI should be avoided if you need strict confidentiality, as they use prompts from users' interactions to refine and train additional model iterations.

## When NOT to use vllm-mlx

- If your target environment is not an Apple device equipped with the required hardware to run models via MLX backend.
- When seeking a solution that offers high-speed token throughput beyond 400 tok/s as vllm-mlx may not be adequate for such performance needs.

## Common questions

### What is the difference between awesome-free-llm-apis and vllm-mlx?

awesome-free-llm-apis: List of Permanent Free LLM API. vllm-mlx: Server for LLMs and vision-language models compatible with Apple Silicon. See the comparison table for live GitHub stats and shared categories.

### When should I choose awesome-free-llm-apis over vllm-mlx?

Choose awesome-free-llm-apis over vllm-mlx when awesome-free-llm-apis is primarily JavaScript; vllm-mlx is Python; License: awesome-free-llm-apis is CC0-1.0, vllm-mlx is Apache-2.0; Pricing: The repository lists APIs with permanent free tiers, detailing rate and token limits for those interested in using free Large Language Model services.; Requirements: Before integration, ensure that the specific requirements for API access are met to utilize these models without restriction.; Users should review the rate and token limits associated with each API provider to determine suitability based on their project's needs.; Tags unique to awesome-free-llm-apis: ai-agents, gemini, ollama, openai; When you need text inference with specialized capabilities like roleplay and storytelling, the Aion Labs API offers a dedicated free tier specifically designed for these purposes.

### When should I choose vllm-mlx over awesome-free-llm-apis?

Choose vllm-mlx over awesome-free-llm-apis when vllm-mlx is primarily Python; awesome-free-llm-apis is JavaScript; License: vllm-mlx is Apache-2.0, awesome-free-llm-apis is CC0-1.0; Tags unique to vllm-mlx: apple-silicon, audio-processing, claude-code, computer-vision; Also covers Model Training; If you need to run LLMs or vision-language models like Llama, Qwen-VL, and LLaVA efficiently on Apple Silicon devices.

### When should I avoid awesome-free-llm-apis?

Avoid using Aion Labs if your project involves heavy commercial activity, given its limited rate and token limits. Do not use Cohere for projects that will scale beyond 1,000 API calls per month or require credit card details for access to higher-tier plans. Google Gemini's free tier is inaccessible in the EU/UK/Switzerland regions and requires consent to let Google improve their products through your usage. Mistral AI should be avoided if you need strict confidentiality, as they use prompts from users' interactions to refine and train additional model iterations.

### When should I avoid vllm-mlx?

If your target environment is not an Apple device equipped with the required hardware to run models via MLX backend. When seeking a solution that offers high-speed token throughput beyond 400 tok/s as vllm-mlx may not be adequate for such performance needs.

### Is awesome-free-llm-apis or vllm-mlx more popular on GitHub?

awesome-free-llm-apis has more GitHub stars (7,852 vs 1,588). Stars measure visibility, not whether either tool fits your constraints.

### Are awesome-free-llm-apis and vllm-mlx open source?

Yes - both are open-source projects on GitHub (awesome-free-llm-apis: CC0-1.0, vllm-mlx: Apache-2.0).

### Where can I find alternatives to awesome-free-llm-apis or vllm-mlx?

GraphCanon lists graph-backed alternatives at [awesome-free-llm-apis alternatives](/tools/mnfst-awesome-free-llm-apis/alternatives) and [vllm-mlx alternatives](/tools/waybarrios-vllm-mlx/alternatives) ([awesome-free-llm-apis markdown twin](/tools/mnfst-awesome-free-llm-apis/alternatives.md), [vllm-mlx markdown twin](/tools/waybarrios-vllm-mlx/alternatives.md)), ranked by typed relationship edges rather than popularity votes.

### Is there a machine-readable version of this comparison?

Yes. The markdown twin at [this comparison](/compare/mnfst-awesome-free-llm-apis-vs-waybarrios-vllm-mlx.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, awesome-free-llm-apis or vllm-mlx?

awesome-free-llm-apis: Active. vllm-mlx: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.

### Where are the full trust reports for awesome-free-llm-apis and vllm-mlx?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-free-llm-apis trust report](/tools/mnfst-awesome-free-llm-apis/trust); [vllm-mlx trust report](/tools/waybarrios-vllm-mlx/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=mnfst-awesome-free-llm-apis`](/api/graphcanon/graph?tool=mnfst-awesome-free-llm-apis)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_