SWE-Bench is not YOUR codebase
SWE-Bench and other public benchmarks are better than vibes but they don't tell you how these models will perform on YOUR codebase. Here's why you need a benchmark for your codebase and what it can do for you.
The landscape shifted again. On Feb 5, 2026, Anthropic released Claude Opus 4.6 and OpenAI released GPT-5.3-Codex. Engineers are scrambling to upgrade their agents to get access to these new models ASAP. Of course, each company’s blog post talks about how great their model is and presents their performance in terms of public benchmarks including SWE-Bench* and Terminal-Bench. (Note: they don’t even use the same version of the benchmark.)
Benchmarks are better than vibes
These benchmarks exist to provide a consistent way to measure models against each other which is better than relying on vibes alone. Yet, these benchmarks are measuring model performance on a set of coding problems that are probably very different than your company’s codebase. So, yes, SWE-bench is better than no measurement, but it doesn’t really tell you how these models will perform on YOUR codebase.
Your codebase is unique
Every company’s codebase is a unique beast. It reflects the history, strategy, and business model of your company, the industry you’re in, the products you sell, your partnerships, and the technology choices made over time: languages, frameworks, integrations, and architectural patterns. Even within a single company, there might be dozens or hundreds of different projects each with their own respective history and unique choices.
Does the performance of Opus 4.6 or GPT-5.3-Codex (or any other model) on SWE-bench actually help you decide which one your engineers should use day to day? The answer is no. It doesn’t really tell you much about how to differentiate performance on your codebase.
You wouldn’t hire a senior engineer based solely on how well they solve LeetCode problems; you’d hire them based on how they navigate your specific stack, your conventions, and your business logic. Yet, that is essentially what we’re doing when we choose AI coding tools based on generic benchmarks.
Benchmark for YOUR codebase?
What if we could measure how these new models (along with Gemini, DeepSeek, Kimi, Qwen, etc.) performed against your codebase? That would enable you to answer the basic question of which model performs best on your codebase. But there is a lot more that having a benchmark for your codebase gets you.
Tool & Model Independence
Consider what happened yesterday: two major releases in 24 hours. This is the new normal. The frontier is shifting constantly, and the model that’s best for your codebase today may not be best tomorrow. Vendor lock-in turns a competitor’s breakthrough into your disadvantage. A codebase-specific benchmark gives you the data to switch confidently, not reactively.
Token & Cost Optimization
A benchmark can also reveal how differently priced models work across different types of tasks for your codebase. Can you say for certain that an expensive model like Opus 4.6 is necessary for an extra-small task vs something like Gemini-3-flash? Unless you have a benchmark that tells you how different models perform on these different sized tasks, you can’t know. And if you believe that these model provider companies are going to stop subsidizing inference costs eventually, you’ll want to understand the token and cost tradeoffs for different types of coding tasks on your codebase.
Context Engineering Optimization (performance)
Most importantly, having a benchmark for your codebase is a requirement to continually measure and optimize how your context engineering efforts affect model performance on your specific tasks. The only real lever developers have to drive successful outcomes from AI coding tools is the context engineering (prompts, tools, rules, documentation, examples, etc.) we provide.
For example, if one of your engineers changes what is in a context file (like AGENTS.md or CLAUDE.md) in a repository, how do you know if that change is harmful, helpful, or neutral? You can’t. Today, changes like this are based on anecdotal evidence at best. With a benchmark, you can actually measure the impact. That makes a benchmark critically important for improving model and agent performance on your codebase.
What if we’re locked into a single vendor?
Let’s say you’re already using Claude Code (or Codex, Cursor, Gemini, Copilot, etc). Is measuring model performance on your codebase important? Even if you’re locked into a specific vendor, you still want to be able to optimize your context engineering. In fact, without a benchmark for your specific codebase, all of your context engineering is subjective guesswork. In other words, you have no reliable way to tell how changes to agent instructions, markdown docs, rules, prompts, etc. are affecting the performance of your AI coding tools.
Ready to get started?
We help companies move beyond guesswork. Our platform builds custom benchmarks for your unique codebase, enabling you to optimize context engineering and model selection systematically. The result is compounding velocity gains driven by data, not by throwing tool subscriptions at engineers.
Don’t settle for generic metrics.
| Model | Public Benchmark Score | YOUR Codebase Score |
|---|---|---|
| Claude Opus 4.6 | 80.8% (SWE-Verified) | ???? |
| GPT-5.3-Codex | 57% (SWE-Pro) | ???? |
| Gemini 3 Pro | 76.2% (SWE-Verified) | ???? |
Stop guessing. Start measuring. Get in touch or book a demo.
* Opus 4.6 reports on SWE-bench Verified, while GPT-5.3-Codex reports on SWE-bench Pro (Public).