OpenAI directs tool-calling workflows to the Responses API. Record the service tier and reasoning setting; Ultrafast mode was added on October 8, 2026.
Always-on adaptive thinking affects output usage and latency. Announced model limits and standard prices do not describe every cloud platform or inference configuration.
Use tasks that require multiple edits, tests and recovery. GPT-6.1 Sol launched September 29, 2026, and Opus 5.5 launched September 22. Measure task completion and total work, including unsuccessful attempts.
Set a verifiable stopping condition
Specify the expected change, tests and files before execution. Stop at a fixed task or budget boundary. Report incomplete work as incomplete even when the agent produces a polished explanation.
Account for always-on thinking
Opus 5.5 has always-on adaptive thinking. Record the full usage response and time to a final usable artifact. Do not compare only visible answer tokens against a configuration that reports reasoning separately.
Three criteria for this comparison
Finished changes that pass the complete test suite
Recovery from tool errors and partial progress
Total accepted-task cost including reasoning
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 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.
