5 Free Copilot Alternatives That Actually Work in 2026
<h1> 5 Free Copilot Alternatives That Actually Work in 2026 </h1> <p>GitHub Copilot costs $19/month. For a lot of developers—students, hobbyists, people between jobs—that's not nothing. I've spent the last few months testing every free AI coding assistant I could find. Most are garbage. These five aren't.</p> <h2> Why I Stopped Paying for Copilot </h2> <p>Don't get me wrong, Copilot is good. But I kept asking myself: am I getting $228/year of value? The answer was complicated. Some days it saved me hours. Other days it hallucinated APIs that don't exist and I spent more time debugging its suggestions than I would have writing the code myself.</p> <p>So I went looking for alternatives. Here's what actually works.</p> <h2> 1. Codeium — The Best Free Option Overall </h2> <p><strong>Cost</stro
5 Free Copilot Alternatives That Actually Work in 2026
GitHub Copilot costs $19/month. For a lot of developers—students, hobbyists, people between jobs—that's not nothing. I've spent the last few months testing every free AI coding assistant I could find. Most are garbage. These five aren't.
Why I Stopped Paying for Copilot
Don't get me wrong, Copilot is good. But I kept asking myself: am I getting $228/year of value? The answer was complicated. Some days it saved me hours. Other days it hallucinated APIs that don't exist and I spent more time debugging its suggestions than I would have writing the code myself.
So I went looking for alternatives. Here's what actually works.
1. Codeium — The Best Free Option Overall
Cost: Free for individuals, forever (they claim)
Codeium is what Copilot should be at this price point. It supports 70+ languages, integrates with VS Code, JetBrains, Vim, and basically everything else.
The completions are fast—usually under 200ms—and surprisingly accurate. It's trained on permissively licensed code only, so you're not going to get sued for using its suggestions.
# Type this: def fetch_user_data(user_id: int) -># Type this: def fetch_user_data(user_id: int) ->Codeium completes:
def fetch_user_data(user_id: int) -> dict: """Fetch user data from the database by ID.""" response = requests.get(f"{API_BASE}/users/{user_id}") response.raise_for_status() return response.json()`
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The catch? They're building a business on enterprise sales. Free individual tier is the loss leader. That's fine by me—just know the model.
Verdict: Install this first. If it works for you, stop reading.
2. Continue.dev — For Local LLM Enthusiasts
Cost: Free and open source
Continue is different. It's not a hosted service—it's a VS Code extension that connects to any LLM. You can use OpenAI, Anthropic, or run models locally with Ollama.
Here's my setup:
{ "models": [ { "title": "DeepSeek Coder", "provider": "ollama", "model": "deepseek-coder:6.7b" } ], "tabAutocompleteModel": { "title": "StarCoder", "provider": "ollama", "model": "starcoder2:3b" } }{ "models": [ { "title": "DeepSeek Coder", "provider": "ollama", "model": "deepseek-coder:6.7b" } ], "tabAutocompleteModel": { "title": "StarCoder", "provider": "ollama", "model": "starcoder2:3b" } }Enter fullscreen mode
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Running starcoder2:3b locally for autocomplete is snappy on any machine with 8GB+ RAM. The suggestions aren't as good as Copilot, but they're yours. No telemetry, no code leaving your machine, no monthly bill.
Verdict: Best option if you care about privacy or want to tinker.
3. Tabnine — The Veteran
Cost: Free tier with basic completions
Tabnine has been around since 2018. They pivoted hard into AI when GPT-3 dropped and have stayed competitive.
The free tier is limited—you get shorter completions and no whole-function generation. But it's stable, fast, and doesn't require an account if you use the local model.
// Tabnine handles boilerplate well const express = require('express'); const app = express();// Tabnine handles boilerplate well const express = require('express'); const app = express();app.get('/users/:id', (req, res) => { // Start typing and it fills in the obvious stuff const userId = req.params.id; // ... fetches and returns user });`
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Verdict: Solid if you just want autocomplete without the AI hype.
4. Amazon CodeWhisperer — The Enterprise Play
Cost: Free for individual developers
AWS's answer to Copilot. It's actually good, especially if you're writing AWS infrastructure code. It knows your CloudFormation, CDK, and boto3 patterns better than anything else.
import boto3
CodeWhisperer nails AWS SDK patterns
def upload_to_s3(file_path: str, bucket: str, key: str): s3_client = boto3.client('s3') s3_client.upload_file(file_path, bucket, key) return f"s3://{bucket}/{key}"`
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The downside: it's AWS. You need an AWS Builder ID, and you're feeding code to Amazon's telemetry. For personal projects, I don't care. For work, check with legal.
Verdict: Best free option for AWS-heavy work.
5. Supermaven — The Speed Demon
Cost: Free tier available
Supermaven is built by one of the original Tabnine founders. The entire pitch is speed—they claim 200ms average latency, which matches what I've measured.
It's newer, so the ecosystem isn't as mature. But if you've tried other tools and found the latency annoying, give this one a shot.
Verdict: Try it if other tools feel slow.
The Honest Comparison
Tool Speed Quality Privacy Setup
Codeium Fast High Cloud Easy
Continue + Ollama Medium Medium Local Medium
Tabnine (free) Fast Medium Both Easy
CodeWhisperer Fast High Cloud Medium
Supermaven Fastest Medium-High Cloud Easy
What I Actually Use
My current setup:
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Codeium for day-to-day coding. It just works.
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Continue + DeepSeek Coder when I'm working on something sensitive or when I want to experiment with different models.
I stopped paying for Copilot six months ago. I don't miss it.
The Real Productivity Hack
Here's the thing nobody talks about: the tool matters less than you think. The developers I know who ship fast aren't the ones with the fanciest AI setup. They're the ones who:
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Know their codebase
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Write code they can read next month
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Don't over-engineer
AI autocomplete helps at the margins. It doesn't fix bad architecture or unclear requirements.
That said, free is free. Pick one from this list, use it for a week, and see if it sticks.
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Motivation The current initialization logic for DeviceOpOverrides relies on checking whether device_op_overrides_dict is empty: DeviceOpOverrides: assert isinstance(device, str), type(device) if not device_op_overrides_dict: from . import ( # noqa: F401 # noqa: F401 cpu_device_op_overrides, mps_device_op_overrides, ) from .cuda import device_op_overrides # noqa: F401 from .mtia import device_op_overrides as mtia_op_overrides # noqa: F401 from .xpu import device_op_overrides as xpu_op_overrides # noqa: F401 if device not in device_op_overrides_dict: # For backends like TPU that only need no-op overrides (Pallas handles codegen) from .cpu_device_op_overrides import CpuDeviceOpOverrides register_device_op_overrides(device, CpuDeviceOpOverrides()) return device_op_overrides_dict[device]"> def



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