Model Management and Inference
Model Benchmarking — Stack
Model benchmarking › Stack
Technology landscape as of 2026-10-10. Knowledge cutoff May 2026.
Selection criteria
A benchmarking stack has two halves that are usually bought separately: a correctness harness and a load generator. Few tools do both, and a comparison missing either is incomplete.
- Reproducibility of a run. Whether a result records the model version, prompt version, configuration, dataset version, and harness version together, so a rerun is a rerun.
- Custom scorer support. Whether domain-specific checks — schema validity, a numeric tolerance, a citation resolving to a source document — are first-class or bolted on.
- Concurrency realism. Whether the harness can issue requests at a controlled rate with a target concurrency and report latency percentiles, rather than looping serially.
- Cost capture. Whether token usage is recorded per case and converted to a cost per completed task, counting retries and failures.
- Statistical reporting. Confidence intervals or paired-comparison tests, not a single mean. A tool that only prints an average invites decisions on noise.
- Per-case inspection. The ability to read the cases that changed between two runs. Aggregate movement is a prompt to investigate, never a conclusion.
- Data handling. Where cases and outputs are stored, given that a benchmark built from production traffic is a copy of customer data with a long retention.
Options
| Option | Best for | Watch out for |
|---|---|---|
| Inspect AI (UK AI Safety Institute) | Structured evaluation with solvers, scorers, and a reviewable log format | Oriented to evaluation design rather than load testing; pair it with a separate latency harness |
| OpenAI Evals | Teams wanting a simple, well-known registry format for task definitions | Centred on one provider's ecosystem; custom scorers and multi-provider runs need work |
| LM Evaluation Harness (EleutherAI) | Reproducing standard academic benchmarks across many open-weight models | Academic task formats; it answers a different question than "is this better for my product" |
| HELM (Stanford CRFM) | A multi-metric framing that reports accuracy, cost, and efficiency together | Heavyweight to run end to end; most valuable as a model for how to report, not as a daily tool |
| Tracing-platform evaluation suites (Langfuse, LangSmith, Braintrust, Arize Phoenix) | Running the same scorers over production traces and over benchmark datasets | Designed around iterating on prompts; keeping the model as the only variable takes discipline |
| Load generators (k6, Locust, vegeta) against your own inference path | Latency percentiles and throughput ceilings under realistic concurrency | They measure your whole serving path, which is what you want — but attribute carefully before blaming a model |
vllm-style built-in serving benchmarks |
Measuring time-to-first-token and inter-token latency for self-hosted deployments | Engine-specific; comparable across configurations of that engine, not across hosted providers |
| A small purpose-built harness in your own repository | Full control of sampling, scoring, cost accounting, and storage | You maintain it, and it will be under-documented unless treated as a product |
Models
Model choice is the output of this capability rather than an input, with one exception: the judge. If correctness is scored by a model, that judge is itself a model decision and deserves the same scrutiny as the candidates. Pin its version, never draw it from the same family as a candidate without also reporting results from a second judge, and validate its labels against human judgements on a sample before trusting any ranking it produces. For the candidate set itself, include the incumbent as a control in every run — a comparison without the current production model has no baseline — and include at least one substantially cheaper model, because the most common actionable finding is not that a better model exists but that a cheaper one is sufficient for part of the traffic.