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.
Always-on adaptive thinking affects output usage and latency. Announced model limits and standard prices do not describe every cloud platform or inference configuration.
Separate routine work from difficult repository or knowledge tasks. Opus 5.5 launched September 22, 2026; Sonnet 5.5 launched September 28. A family name does not establish the amount of additional quality your application receives.
Find the tasks that change the decision
Start with cases Sonnet fails or needs manual repair. Evaluate both models on those cases and on a held-out routine sample. This reveals whether an escalation path is useful without treating every task as equally difficult.
Use current cache and output accounting
Sonnet cache-read pricing changed October 7. Opus's launch notes describe always-on adaptive thinking. Use current provider rates and reported usage, including reasoning, cache operations and retries.
Three criteria for this comparison
Success on the difficult task subset
Long-running tool-loop reliability
Additional spend per additional accepted task
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 Sonnet 5.5
Enter all four values. Attempted tasks must be at least one; accepted tasks cannot exceed attempted tasks.
Claude Opus 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.
