Free AI Tools for Developers in 2026#
We are in 2026 and artificial intelligence is no longer a novelty, it is the standard. However, the bottleneck remains the same for many independent developers and students: the cost of subscriptions. Maintaining access to frontier models can easily amount to hundreds of dollars a month.
Luckily, the fierce competition between big tech and the open source community has gifted us an ecosystem where it is possible to access cutting-edge tools for free. This is my analysis of the definitive free AI stack for this year.
Antigravity: Google Deepmind’s Bet#
Antigravity has quickly become the must-have development environment for those of us working close to the Google ecosystem. What makes it irresistible is not just its deep integration, but its generous free quota on frontier models.
Its integration is deep. On one hand, the browser extension allows controlling the browser, inspecting the console, taking screenshots and analyzing the site, facilitating not just implementation but also management.
On the other hand, and more relevant to the workflow, is its handling of artifacts. Antigravity generates documents with the implementation plan where comments can be made directly, improving feedback with the agent before starting to code. upon completion, it produces a summary of the work. It is crucial to read this summary carefully, as it often contains warnings about pending tasks.
Antigravity offers daily free access to Gemini 3 Pro and Gemini 3 Flash. But what really surprises is the inclusion of third-party models: you have limited but functional access to Claude Sonnet and Opus (from Anthropic) at no additional cost. It is perfect for those of us who need to contrast solutions between models with different reasoning styles, but also for those who identify strengths in models and want to use them according to the tasks. In my case, I have identified the following:
Claude Sonnet 4.5: Direct code implementation with clear definitions and architecture on my part. To the extent that the specification is clearer, I consider Sonnet’s implementation level better than the code generated by Gemini.
Gemini 3 Flash: Code implementation in which I require greater support for definition and research from the model, searching external sources. It has a good balance of quality and speed, and potentially lower token consumption than Sonnet.
Gemini 3 Pro: Elaboration of implementation plans, research of external sources, code understanding and documentation elaboration.
I do not usually use Claude Opus 4.5 (thinking) for its high token consumption compared to the benefit I get with Sonnet (with whom it shares the quota). Identifying strengths is a way to take advantage of independent token quotas instead of using a model until its quota is exhausted to only then switch to another.
It is important to properly use the planning mode for the elaboration of complex tasks or those requiring research or better planning, in contrast to the fast mode for simple tasks and where we can give clearer specification and only require implementation help (somehow, what we would be able to set up without difficulty, but need speed). This helps optimize token usage and make better use of quotas. Currently the free quota renews weekly.
Important, to help better direct tasks, the use of rules and skills is recommended, which help define the general behavior of the agent (agent rules) and the way to execute specific tasks (agent skills, which is a standard, although its route definition is not).
Ampcode: Free Opus (with a twist)#
If the Opus quota in Antigravity falls short for you, AmpCode is the solution. Through ad-based funding (yes, ads in your IDE, but surprisingly non-invasive), Ampcode allows you to use Opus 4.5 for code elaboration and GPT 5.2 for planning and external search work (the Oracle) in a predefined way in the smart mode (the definition of what each model is better for), and fast models with lower token consumption (and less capable) for simpler tasks with the rush mode. Unlike Antigravity, in Ampcode it is better to have a prior planning process (even with agent help, but indicating not to implement code, just do planning) and then manually partition tasks. This way we can have greater control over execution, as I have observed it tends to be highly autonomous, but at the cost of some deviations from the initial objective if open points remain that it must infer, which in the Antigravity flow is less common due to the integration of explicit planning.
AmpCode is available as CLI and extension (for example, available for VSCode/Antigravity)
Zed: The ultra-fast editor continues to reign#
Zed continues to be my go-to editor for its absurd speed, continuous development, native AI integration, debugging protocol, git integration and its growing ecosystem of extensions.
Basically, Zed natively integrates Gemini through its ACP protocol (first integrated agent), and with the Google account you have access to the Gemini family of models (determined by the CLI accessed). It is important that this quota can be affected by general Google AI usage (i.e., your usage in Zed, Antigravity, Gemini).
Opencode: Open source agents#
Opencode is an open source agent solution available as CLI, desktop application and extension for multiple IDEs (available not just in Zed, also for VSCode/Antigravity). It has a unified interface for multiple models with free quota (temporarily, while these models collect information and make improvements), as well as compatibility for connection with many more providers. It distinguishes the planning (plan) and implementation (build) modes explicitly. Regarding the models with free quota currently:
Grok Code Fast 1: Fast and economical model from xAI, focused on code. It is found available for free in some editors and announced as limited time.
GPT-5 Nano: Fast and economical model from OpenAI. Good for simple tasks, mainly text, but in simple code it can be a good option.
MiniMax M2.1 and GLM-4.7: Excellent open source options available with free quota. So far I haven’t advanced much in this exploration, but GLM has generated more confidence in me, although its reasoning is good, in several cases it has gotten stuck in generation. And with MiniMax language mixes have occurred (texts in Spanish and Chinese at the same time).
Text Mode in Zed#
My favorite workflow currently is using Text Mode in Zed. I combine the agility of Github Copilot (in its free tier for Open Source maintainers) for line-by-line suggestions, with Gemini in the side panel for architecture queries. Latency is low and the writing experience is fluid, without the visual overload of other IDEs.
Local Models: Privacy and power in your team#
We cannot talk about a free stack without mentioning local execution. With current hardware, running quantized models is viable even on mid-range laptops.
Using ollama, my local configuration includes:
gemma3n:e4b: The nano version of Gemma 3. Incredibly efficient for simple tasks and grammar correction, only in text mode.qwen3:8b: The king of local reasoning. Its logical capacity rivals closed models of 2025. Available in zed agent.qwen2.5-code:7b: A classic that remains valid, specific for code. Available in zed agent.
It is important to highlight that Zed agent (zed agent) discovers available models, through subscription or locals like ollama that you must install previously. Ensure the Ask permission mode if you want greater control, or Write to allow default modifications.
Conclusion#
In 2026, you don’t need to pay a fortune to code with superpowers. Combining the raw power of Antigravity and its extensions like Ampcode, with the speed of Zed vitaminized by Gemini, Opencode, and backed by local models in Ollama, you have a world-class development environment at zero cost.
Analyze which tools adapt to your style, and as always, happy coding!