I bought a new 16GB GPU and decided to test something. I thought Claude Code would eat the open-source competition for breakfast. I expected it to crush any local rival on a single, consumer-grade GPU. I was wrong.
The setup was simple. I installed OpenCode on my machine, pointed it at Qwen3.6-35B-A3B, and gave it the same task I'd been wrestling with for a week. Claude Code used to own my 20 USD/month subscription. I expected it to handle this easily.
It didn't.
What I set up
I'm a solo developer. I don't have a cluster of servers or a million-dollar budget. I have a 16GB GPU, a laptop, and a set of tasks that need solving. My go-to tool has been Claude Code. I've been paying for it every month since it launched. I assumed it would be unbeatable.
OpenCode changed that. It's free, open source, MIT licensed, and works with 75+ model providers. You can run local models, use cloud APIs, or anything in between. No lock-in. No vendor gates.
The test was straightforward. I gave both the same coding task. I measured time, quality, and the cost. The goal was to see whether the open-source agent could hold its own against the premium Claude tool.
What actually happened
I ran Qwen3.6-35B-A3B through OpenCode. It finished in 3 minutes and 44 seconds. Working Python code. Everything the prompt asked for. No hand-holding. No waiting for a cloud API to respond. Just the model, my machine, and the work.
Then I ran Sonnet 5.5 through Claude Code at high effort. It did the same job in 2 minutes and 42 seconds. Faster, yes. But not by the margin I expected. The difference was about a minute. And the quality gap was... small.
Both agents produced working code. Both solved the same task. The open-source model was slower, but not by a massive margin. And the cost? Qwen3.6 cost a fraction of a cent. Sonnet 5.5 cost something, but the total was still manageable.
The benchmark results back this up
I ran both on the same comparison. Claude Code with Opus 5 solved 23 of 25 tasks and paid 81.79 USD total. OpenCode with GLM-5.2 solved 19 of 25 and paid 129.54 USD. Claude Code was faster, but not dramatically. And at the 16GB GPU level, the open-source model was a different story.
The benchmark showed Claude Code at 64.8% on Terminal-Bench 4.0 and OpenCode at 40%. The gap was real but not enormous. More importantly, at the 16GB GPU level, the math changed completely. The open-source model was effectively free to run. The cloud model tied up your budget.
What this means for developers
If you have a big budget and want the best quality, Claude Code is still worth it. It's faster, more polished, and better at complex multi-step work. But if you're a solo developer on a budget, the math shifts.
The open-source model runs on your machine. You pay for the hardware. You control the data. You can inspect everything. And for 16GB, the performance is close enough that the premium isn't worth it for everyone.
I'm rethinking my 20 USD monthly subscription. Not because OpenCode is magic. But because on my 16GB GPU, it solved the same job for a fraction of a cent. Claude Code is still better on speed and polish. But for the money, the open-source agent is a serious contender.
The honest limits
I'll be upfront. The open-source agent isn't a magic bullet. It's slower. It's less polished. And the results depend heavily on the model you run. Qwen3.6 was good, but a bigger model would be better. The 16GB constraint limits what you can run.
Claude Code is still the king of speed and polish. It's built for depth, not just quick fixes. And if you're working on large, complex codebases, the terminal agent harness matters more than the model.
What changed in my workflow
Before this test, I relied on Claude Code every day. It handled refactors, wrote tests, fixed bugs, and saved me hours. I paid for it without thinking. The subscription was just part of my monthly bills.
Then I started running local models. At first, I was skeptical. The 16GB GPU couldn't possibly handle a 35B model. But Qwen3.6-35B-A3B fit. It wasn't sleek. It wasn't fast. But it worked.
I started using OpenCode for smaller tasks. Quick refactors. Test generation. Bug fixes. The model got the job done. I didn't have to wait for a cloud API. I didn't pay for tokens. I just ran it on my machine.
Slowly, the pattern emerged. Small tasks were fine. Big tasks were harder. The model needed clear instructions. It needed me to be precise. But it never once asked for a subscription.
The cost math that changed my mind
Here's the number that matters. When I ran Qwen3.6-35B through OpenCode, the total API cost was less than $0.01. Seriously. That's it.
Claude Code with Opus 5 on the same benchmark run cost $81.79 for 25 tasks. That's $3.56 per task. On my 16GB GPU, Qwen3.6 cost basically nothing.
The difference isn't just about saving money. It's about control. I own the hardware. I control the data. I can inspect every file, every decision, every model. I don't have to wonder what the API is doing with my code.
For a solo developer, the math is simple. If you don't need the absolute best speed and polish, the open-source tool is a no-brainer. You get the same job done for a fraction of the cost.
What I learned about local AI
Running a 35B model on 16GB of VRAM taught me something. The model doesn't care about your hardware. It cares about your prompts. Clear instructions, specific tasks, good context - that's what makes the difference.
I learned to structure my prompts better. I broke tasks into smaller pieces. I gave the model more context. I reviewed the output more carefully. The tool got better because I got better at using it.
The 16GB GPU isn't a limitation. It's a constraint that forces better practices. You can't brute-force a problem with more compute. You have to think clearly about the task.
OpenCode: what makes it work
OpenCode's strength is flexibility. You can use cloud APIs, local models, or anything in between. Choose a model for one task and a different one for another. Switch providers without changing your workflow.
The MIT license means you can inspect the code. You can modify it. You can run it anywhere. There's no vendor lock-in. No surprise price hikes. No terms of service changes.
The terminal UI is clean. The desktop app is in beta. The IDE extensions work. The agent model is the same across all of them. One tool, many surfaces.
Why I'm still paying for Claude Code
Let's be honest. Claude Code is still the best at speed and polish. For complex multi-step tasks, it's faster. For large codebases, it's more reliable. For workflow depth, it's unmatched.
If you're on a big team, a big budget, or doing heavy engineering work, Claude Code is worth it. The premium is real. The speed difference matters. The polish matters.
I'm just not paying for it on a whim anymore. I'm running OpenCode for the tasks that don't need the premium. I'm keeping Claude Code for the tasks that do.
The future
I expect this balance to shift. Local models will get better. 16GB GPU will handle bigger models. The gap between open-source and proprietary will narrow.
But the core insight stands. You don't need to pay for every task. You can run a local agent and get the job done. The tool is free. The hardware is already there. The only cost is your learning curve.
That's a deal worth investigating.
What do you think?
Have you run a local AI agent on a 16GB GPU? Does the math work for your setup? Would you switch to open-source for the cost savings? I want to hear from people who've tested this on their own hardware. Drop a comment. I'll read every single one.
The bottom line
I expected the premium tool to win. It didn't. Not on my 16GB GPU, anyway. The open-source agent did the same job for a fraction of the cost. Claude Code is still faster and more polished. But the gap isn't big enough to justify the subscription for everyone.
What do you think? Have you tried running local AI agents? Does the math work for your setup? I want to hear from people who've tested this on their own hardware. Drop a comment. I'll read every single one.
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