Use the exact Claude endpoint and current model documentation for limits and prices. Cache-read pricing changed on October 7, 2026; a launch-era estimate can be stale.
Start with routine classification and extraction, then add the cases that require escalation. Haiku 5.5 launched October 7, 2026, and Sonnet 5.5 launched September 28. Haiku is positioned for high volume, but your measured error and retry rates decide whether it can handle the workload.
Treat Haiku migration as an API change
Haiku 5.5 uses adaptive thinking with effort control. The official notes warn that manual budget_tokens thinking, temperature, top_p, top_k and assistant prefill can return 400 errors. Fix the request adapter before drawing a latency or quality conclusion.
Design an escalation rule from errors
Label failures by task and difficulty. A routing rule can use schema validation or uncertainty signals, but it should be evaluated on a held-out set. Include both the first attempt and any Sonnet retry in cost and latency.
Three criteria for this comparison
Incorrect fields on labeled extraction cases
Tail latency at the same queue and effort settings
Retries and escalation per accepted result
Run a fair comparison
Use the same representative inputs and a written answer rubric. Pin the model version and record the endpoint, thinking configuration, quantization when known, context length, output allowance and concurrency. Preserve native request differences in an adapter rather than silently dropping fields.
Measure full request time, error rate and reported usage. For tools, validate arguments and score the result after execution. For images, preserve resolution and preprocessing. A valid response format alone does not establish a correct answer.
Compare cost per accepted result
Calculate input cost plus all billed output, including reasoning where the provider counts it. Add retries and discarded answers. Divide the total by the number of results that pass your quality criteria. Keep API usage cost separate from deployment and engineering cost.
Latest recorded measurements aligned by exact artifact, benchmark version, source and methodology. These are source results, not tests of Flonno's deployment.
Artificial Analysis observations are refreshed by the integrated Radar. Differences in artifact, benchmark version or method prevent a score comparison. Read the evidence methodology →
YOUR WORKLOAD
Compare cost per accepted task.
Enter results from your own evaluation. A task should count once it meets your written quality criteria. Include failed attempts and retries in total spend. These inputs stay in this browser and are not a Flonno benchmark.
Claude Haiku 5.5
Enter all four values. Attempted tasks must be at least one; accepted tasks cannot exceed attempted tasks.
Claude Sonnet 5.5
Enter all four values. Attempted tasks must be at least one; accepted tasks cannot exceed attempted tasks.
Turn evidence into a deployment decision
A source benchmark can inform your shortlist, while your evaluation shows whether a model finishes the work at an acceptable cost. Keep the chosen endpoint, processing terms and output limits in the decision record. Inclusion in the Radar does not mean the model is served by Flonno.
