MatrixBench
Benchmark testing AI
MatrixBench is KZN's AI agent benchmark suite for measuring real-world task completion across models, tools, memory systems, and multi-step workflows. It tests whether an agent can actually do the work — not just answer a prompt.
Latest results
MatrixBench score comparison
Scores are shown on a 0–100 scale from Harness AI benchmark runs.
| Rank | Agent setup | Score |
|---|---|---|
| #1 | Claude Code + Opus 4.8 Max Highest score |
74/100
|
| #2 | Hermes + GPT-5.5 xhigh 3 points behind |
71/100
|
Why MatrixBench
Measure agents by completed work
Model leaderboards rarely show whether an AI agent can use tools, recover from errors, preserve context, pass tests, and ship a correct result. MatrixBench turns those realities into repeatable tasks and verifiable scores.
Real-world tasks
Coding, debugging, shell work, file operations, tests, and multi-step problem solving — the kind of work agents are actually expected to do.
Tool-aware evaluation
Measure the full loop: plan, execute, inspect outputs, fix failures, and validate with deterministic checks rather than subjective impressions.
Comparable runs
Run the same task suite across Claude Code, Codex, Hermes, local models, or custom agents to see where each setup is strong or brittle.
Evidence-backed reports
Capture task outcomes, verifier results, and report artifacts so teams can improve agents based on reproducible evidence.
How it works
From quick smoke to full benchmark
Select the agent
Choose the model, CLI, or autonomous coding setup you want to evaluate.
Run benchmark tasks
Start with a small pilot or run the full native and Harness AI benchmark suites.
Verify outcomes
Each task is checked by tests, scripts, or oracle logic so success means the work was actually completed.
Compare and improve
Use the report to tune prompts, tools, memory, and execution workflows before deploying agents into production work.
Want to benchmark your AI agents?
KZN can help design, run, and interpret agent evaluations.
Talk to us