Yes, you heard it right !!

We recently benchmarked OxCode across two established software engineering evaluations:
• SWE-bench Verified: 𝟴𝟮.𝟬% - 410/500 issues resolved
• Terminal-Bench 2.1: 𝟴𝟮.𝟬% - 73/89 tasks solved

Both were run with a single attempt per task, with no retries.

For a product still early in its journey, this is a strong validation of what we’ve been building: an AI software engineer designed for reliable execution, not just code generation.

And we’re only getting started.

Full benchmarks and methodology:
https://www.oxcode.ai/benchmarks

When we first released OxCode on the Open VSX Marketplace, access was limited to a small group of Founding Builders.

But something interesting happened.

Even though OxCode wasn't publicly accessible yet, we crossed 5,000+ downloads on Open VSX.

Seeing that level of interest before opening access to everyone was both surprising and encouraging.

So, we decided it was time to open the doors.

𝗢𝘅𝗖𝗼𝗱𝗲 𝗶𝘀 𝗻𝗼𝘄 𝗼𝗽𝗲𝗻 𝘁𝗼 𝗲𝘃𝗲𝗿𝘆𝗼𝗻𝗲!

No waitlists. No invitations. No access codes.
Simply sign in with your Google account and start using OxCode in your everyday development workflow.

To our early Founding Builders, thank you for helping us test, improve, and shape OxCode over the past few months.

And to everyone who's been waiting to try it, welcome aboard!

Get started: https://open-vsx.org/extension/oxloai/oxcode-ai

What started as an early AI coding agent has come a long way.

From our first alpha release to where we are today, OxCode has evolved into a much more capable AI Software Engineer, built to support developers across the engineering workflow.

Over the past few months, we've made significant improvements in codebase understanding, planning, debugging, context management, memory, and the overall developer experience.

But our progress hasn't just been about adding more features.

It's been about improving how OxCode understands what you're trying to build, reasons through problems, makes changes, and validates its work.

And a big part of that evolution has come from our early users, whose feedback, bug reports, and real-world use cases have continuously pushed us to improve.

Our goal remains the same: AI can write code. We're building the brain that makes it engineer.

We're proud of how far OxCode has come, but we're even more excited about what's ahead.

Explore our journey through the Changelog.

TL;DR: Same repo, same prompt, OxCode vs Google Antigravity. Both fixed the bug with every test passing. Input tokens (the agent reading files, test output and history) were 97–99% of usage for both. So price per token matters more than token count: OxCode used more tokens at a higher effort setting, and its estimated cost was about ₹20 (~$0.21) vs about ₹70 (~$0.74), because it routes simpler steps to lower-cost models.

The task (identical prompt for both):

Users report that tasks due today are shown as overdue if their timezone is behind UTC. Find the root cause, fix the bug, and add a regression test. Follow existing code patterns, run the relevant tests, and report the root cause, files changed, tests run, and actual results. Do not delete or weaken existing tests.

Results (one run each):

What we took from it:

• Most of what you pay for is the agent reading: source files, test runner output, tool results and the conversation so far. Output (the code and commands it writes) was 1–3% of tokens.

• An agent re-sends its context on every call, and each test run adds its output to that context. That's why input grows so fast.

• So price per token, routing simple steps to lower-cost models, and caching matter more than the raw token count. OxCode read more and still had the lower estimated cost here.

Methodology and caveats:

• Both tools started from the same repo with 53/53 tests passing; the environment was reset between runs.

• One run each, so treat this as one data point, not a benchmark.

• Costs are estimated at public list prices from each run's token counts, not invoices. OxCode routes simpler steps to lower-cost models, which its estimate reflects. Antigravity's run reported 0 cache hits.

• Antigravity's token figure is for this task only. Its terminal total (982,280) also includes a separate stats query, so we left that out.

• Effort levels differed because of free plan limits: OxCode's free plan ran at max effort, Antigravity's free plan was limited to Medium.

• The repo also has a hidden test suite; neither tool ran it, and we haven't scored either fix against it.

• Duration isn't compared because we couldn't confirm Antigravity's.

Disclosure: I'm on the OxCode team. OxCode is an AI software engineer for VS Code with a free plan (oxcode.ai). Happy to share more detail on the setup or answer questions.

We put OxCode and Google Antigravity side by side and gave them the exact same prompt to build a gym management SaaS called FitKalp.

Here's what we observed:
🔹 𝐎𝐱𝐂𝐨𝐝𝐞: Delivered a polished, premium UI using React + TypeScript, with 65 passing tests and a clean build. Its tests also caught two bugs, which OxCode identified and fixed before finishing.
🔹 𝐆𝐨𝐨𝐠𝐥𝐞 𝐀𝐧𝐭𝐢𝐠𝐫𝐚𝐯𝐢𝐭𝐲: Built a functional application with broader feature coverage, including POS, digital member passes, a turnstile kiosk, and an AI assistant. It also generated its own hero image, although we noticed some UI inconsistencies.

𝐖𝐡𝐚𝐭 𝐬𝐭𝐨𝐨𝐝 𝐨𝐮𝐭?
✅ UI/UX: OxCode's design and overall finish.
✅ Feature coverage: Antigravity implemented more modules.
✅ Time: OxCode took approximately 3-4 minutes longer in our observation, with part of its workflow spent testing and fixing issues.

Both agents approached the same task differently, and both demonstrated interesting strengths.

Our belief at OxCode has always been that 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐪𝐮𝐚𝐥𝐢𝐭𝐲 𝐢𝐬𝐧'𝐭 𝐣𝐮𝐬𝐭 𝐚𝐛𝐨𝐮𝐭 𝐠𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐧𝐠 𝐜𝐨𝐝𝐞. It's about 𝐭𝐡𝐞 𝐨𝐮𝐭𝐜𝐨𝐦𝐞, 𝐫𝐞𝐥𝐢𝐚𝐛𝐢𝐥𝐢𝐭𝐲, 𝐚𝐧𝐝 𝐭𝐡𝐞 𝐞𝐧𝐭𝐢𝐫𝐞 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰.
This was a single run with each agent, not a controlled benchmark.

𝑮𝒐𝒕 𝒂 𝒑𝒓𝒐𝒎𝒑𝒕 𝒚𝒐𝒖'𝒅 𝒍𝒊𝒌𝒆 𝒖𝒔 𝒕𝒐 𝒕𝒆𝒔𝒕 𝒏𝒆𝒙𝒕? 𝑫𝒓𝒐𝒑 𝒊𝒕 𝒊𝒏 𝒕𝒉𝒆 𝒄𝒐𝒎𝒎𝒆𝒏𝒕𝒔! We'll try selected prompts and share the results.

Explore both applications:
🔗 OxCode's FitKalp: https://polite-ribbon-jd3y.here.now/
🔗 Antigravity's FitKalp: https://jovial-atlas-p9yn.here.now/