OpenAI directs tool-calling workflows to the Responses API. Record the service tier and reasoning setting; Ultrafast mode was added on October 8, 2026.
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.
Use the same repository snapshot and acceptance tests. GPT-6.1 Sol launched September 29; Sonnet 5.5 launched September 28. Their release dates establish which editions are being compared, while the actual patch and test outcomes establish which one fits your workflow.
Map the native tool loop before comparing quality
OpenAI directs GPT-6.1 Sol tool calling to the Responses API. Claude uses the native Messages API. Preserve tool IDs, execution results and error recovery in separate adapters. A loop that silently drops a field can make the model comparison a test of adapter bugs.
Include the October lifecycle changes
GPT-6.1 Sol gained Ultrafast mode on October 8, 2026; Claude Sonnet 5.5 cache-read pricing changed on October 7. Record the selected service tier and actual reported cache usage. A September launch estimate is not a current bill.
Three criteria for this comparison
Patch correctness and tests after the complete tool loop
Compatibility of Responses and Messages adapters
Accepted-task cost, retries and service-tier latency
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.
GPT-6.1 Sol
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.
