The AI landscape is moving fast, and comparing models is tricky. But comparing agent harnesses—the scaffolding around a model—is even trickier. A single model can behave completely differently depending on the harness it runs in. The good news? There are now excellent benchmarks and visualization tools that make this comparison tractable. Here are the ones I keep coming back to.

1. FrontierHarness Eval

This is the benchmark I recommend to anyone who wants to understand how much the harness matters. Run the same model through different coding-agent harnesses on the same software-engineering tasks, and the results are striking. In one run with Kimi K3 across nine harnesses, pass rates were similar but costs varied by 17.5x—median cost per pass ranged from $1.05 to $18.34. The interactive report at frontierharness.org lets you drill into failed runs, cache behavior, speed, and task-level results. If you're evaluating coding agents, this is essential.

2. Real API Pricing (Pareto Charts)

Model comparisons often ignore the cost dimension. real-api-pricing.vercel.app fixes that by showing subscription fee ÷ usable tokens, along with allowance, unit-price, and leaderboard Pareto charts. The Pareto frontier visualization is particularly useful: it shows which models offer the best performance-to-cost ratio, making the trade-offs explicit. A model that looks great on accuracy alone might be dominated once you factor in price.

3. SWE-bench Verified

For pure software-engineering capability, SWE-bench Verified remains the gold standard. It uses real GitHub issues and tests, evaluating retrieval, patch application, and verification. The leaderboard is actively maintained, with top models like Claude Opus 5 reaching 96% as of late 2026. It's a great first filter when narrowing down coding models.

4. AgentBench

AgentBench tests whether harnesses generalize beyond a single task loop. It spans OS, databases, knowledge graphs, and web browsing, making it ideal for evaluating multi-environment agents. The leaderboard shows how different agent systems perform across diverse challenges, which is a much better signal than single-task benchmarks.

5. Terminal-Bench

If your agents work in the terminal, Terminal-Bench is the benchmark to watch. It consists of hard, realistic tasks in command-line environments—compiling code, training models, setting up servers—each with a unique environment and comprehensive tests. The continuous benchmark evolves over time, and frontier agent builders use it to track progress.

6. HarnessBench

HarnessBench takes a different approach: it holds the LLM constant and measures the harness itself. It captures wire-level telemetry, runs two-stage regression guards, analyzes repo pollution, and calculates true costs using provider rate cards. The core hypothesis—that the execution harness is 50–70% of the battle—is backed by measurable differences in correctness, cost, latency, and token efficiency.

7. PawBench

PawBench evaluates model × harness pairs across 150 tasks, 9 models, and 3 harnesses, with diagnostic traces that separate model failures from harness failures. One key finding: with the same model, harness scores varied by up to 11.5 points. This is the kind of granularity you need when debugging agent regressions.

The Takeaway

No single benchmark tells the whole story. My workflow is:

1. Start with a Pareto chart (like real-api-pricing) to filter by cost-performance.
2. Use SWE-bench Verified for coding capability.
3. Run FrontierHarness Eval to understand harness-specific behavior.
4. Check AgentBench, Terminal-Bench, HarnessBench, and PawBench for specialized dimensions.

The tools are here. The graphs are here. Stop guessing—start measuring.

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Related Links:

• FrontierHarness Eval
• Real API Pricing
• SWE-bench Leaderboard
• Terminal-Bench
• PawBench
• HarnessBench