Benchmark — Prove it holds up
The Benchmark proves which agent and model actually hold up on the work your teams really do, on your own hardware, before you commit. It evaluates complete agent systems, not individual models in isolation.
Before a single euro goes into GPUs, the Benchmark turns guesswork into evidence: it measures candidate models on the work your teams really do, not on a public leaderboard a vendor may have trained against.
Measure candidate models on your own coding challenges and see which one earns a place on your infrastructure.
See why a model shines for one team and frustrates another by testing each team's real scenarios and stacks.
Define your scenarios once and replay them automatically whenever a new model appears, so an upgrade becomes a measured decision.
When a model proves itself, switch it straight into the Workbench.
Flexible by design
Choose where the Benchmark runs and which codebase it runs against — two decisions, four ways to turn questions about AI into evidence.
Where it runs
Test candidate models against your own business domain in the cloud — see which ones deliver, what hardware each would require, and whether a sovereign on-premises deployment is a credible alternative. Measure your real challenges before committing budget.
Keep your AI stack at peak performance on your own infrastructure. When a new model is released, decide with evidence: does it run efficiently on your hardware, and can it handle your business challenges — before you switch?
What you benchmark on
Benchmark against greenfield challenges defined in a public pool, and gain insight from benchmarks shared by other users. As the same challenges are replayed over time, you also see how models' performance shifts — for instance when a cloud provider's update quietly changes results on the tasks you depend on.
Find out whether an agent can truly handle the problems your engineering teams face. Run the Benchmark against your own repositories, entirely within your environment — on-premises or in your own cloud tenant — to measure real performance on real projects. Generic tests only go so far; benchmarking on your real projects gives far more insight.
Capabilities
Every run pairs a coding agent with a model, because the system as a whole is what matters, not the model alone.
Mechanical checks (build, tests, static analysis, security scans) are kept separate from LLM-as-a-judge review, never blended into one misleading number.
Run the same use case at rising levels of difficulty and abstraction, to see where a model starts to struggle.
Run a model with no, some, or maximum context. The spread shows how much hand-holding it needs, which explains why a model suits one team and not another.
Bring your own repositories and coding challenges. Testing on your private stack sidesteps the contamination of public leaderboards.
Pinned commits and a saved configuration snapshot make every run reproducible, so you can re-test on each new model release.
Observe shared GPUs without disturbing them, or deploy a model to local or cloud compute just for a run.
Compare models and agents side by side and track how they change over time, with results you can automate via an agent interface.
What it measures
Every agent run is examined across three score axes — hardware, build and task — so a result is far more than just "did it run". What you see here is only a selection; each axis carries many more signals.
Tokens/min, latency, runtime …
Type safety, dead code, duplication …
Requirement coverage, tests, pass@k …
Security · Measured, not assumed
In independent testing, 45 % of AI-generated code samples failed security checks. Whether that holds for your agent, your model and your stack is a question the Benchmark can answer. Every run is scanned the way your own pipeline would scan it, and the findings become a score of their own. Alongside regular tasks we run attack scenarios to see what an agent can be talked into. And where an AI reviewer grades quality, we protect the grader too – so the numbers you base decisions on can’t be gamed.
01
Fast code that opens a hole is not good code.
02
Test what an agent can be talked into – before production does.
03
When an AI grades AI, the grader needs protecting too.
Security · Sovereignty
Self-hosted models and benchmarks on your own repositories are what make the setup sovereign – and both bring their own risks. Model files can carry executable code, be swapped on a mirror or be trained to misbehave on a trigger; your code becomes test data. We treat a model like any other release artifact, with verified origin and the Benchmark as the gate to production. And we handle your code accordingly: on-premises, nothing leaves your network; in the cloud mode, runs happen in your tenant, under your keys, and only scores ever reach the shared pool.
01
A self-hosted model is only sovereign if you know where it came from.
02
Your code is the test set – handled like your most sensitive asset.
Step 1
Pick generic tasks, or representative coding tasks from an internal software development project.
Step 2
Pick from a collection of models that run on your own infrastructure or in the cloud of your choosing.
Step 3
Run agents in isolated workspaces.
Step 4
Score with state-of-the-art metrics to compare model performance and cost on the given tasks.
Step 5
Compare results across models, over time.
You don't need a blast furnace to make toast.
Prof. Dr. Maja Göpel FSAS Technology Summit 2026
Not every task needs the biggest, most expensive model. Often a smaller, leaner one handles your concrete challenges just as well — and saves cost and resources along the way.
Decision support
A few of the calls teams make with it — alongside many more that are specific to your own domain.
Which model is worth hosting on your own infrastructure.
Weigh hosting cost against the quality you actually get.
How a model handles your own coding challenges before you commit.
Test on your real repositories, not a public leaderboard.
Why a model works for one team but not for another.
See how much context and hand-holding each team needs.
Whether a new release is actually better, tested the same way each time.
Re-run the identical, pinned benchmark on every release.
When a model is ready to promote into the Workbench.
Move it from evaluation to production once it clears your bar.
…and the questions that matter most to your own teams.
Bring your own criteria — the benchmark adapts to them.
