ClawBenchOpen-weight is not the same as open-source
Many downloadable models are open-weight, but their licenses, training data, and source code may not be fully open. Read the model card and license before using a model commercially or redistributing a derivative.
Start with the work, not the leaderboard
Define a small task set from real work. Coding teams might include bug fixes, test writing, code review, and repository navigation. Support teams might test retrieval, summarization, and structured responses.
Keep the prompt, tools, context, attempt policy, and evaluator fixed. A model comparison is useful only when the surrounding system stays comparable.
Compare the constraints that affect production
- License: confirm commercial use, redistribution, derivative, and attribution terms.
- Quality: score task completion and inspect representative failures.
- Hardware: measure memory use, throughput, cold starts, and concurrency on your target system.
- Latency: report median and tail latency for the same task mix.
- Context: test the documents and repositories you expect to provide in practice.
- Operations: include serving, monitoring, upgrades, safety controls, and engineering time.
Evaluate model families by role
General-purpose models suit mixed workloads. Coding models may be better for editing and repository tasks. Reasoning models may help on multi-step problems but can add latency. Small models can be useful for routing and repetitive work.
Multimodal models add image or document understanding. Test that capability only when it matters to the product; otherwise it can distract from the text and tool-use quality you need.
Measure total cost, not just token price
Self-hosting shifts cost into hardware, capacity planning, monitoring, and maintenance. Hosted inference shifts more cost into usage. Compare both at your expected traffic, latency target, and reliability level.
A smaller model that needs retries or human cleanup can cost more than a larger model that completes the task once. Record attempts, tool calls, latency, review time, and successful outcomes together.
Use a repeatable selection loop
- Choose representative tasks and define the pass condition.
- Shortlist models whose licenses and deployment options fit.
- Run each model under the same harness and resource limits.
- Inspect traces and artifacts from both passes and failures.
- Repeat the strongest candidates on held-out tasks.
- Re-run the evaluation before changing the production default.
Use benchmark scores as evidence, not a verdict
Public benchmarks are useful for shortlisting, but they do not reproduce your data, tools, prompts, or failure costs. Learn where benchmark scores stop predicting production behavior.
For an improvement loop that preserves evidence, see how to move from benchmark failures to production changes.