OpenAI directs tool-calling workflows to the Responses API. Record the service tier and reasoning setting; Ultrafast mode was added on October 8, 2026.
GPT-6 Astra does not support none reasoning effort, custom temperature, top_p or logprobs. Check your request adapter before replaying prompts from earlier models.
Choose tasks where stronger multi-step execution could matter. Astra launched September 3, 2026, and GPT-6.1 Sol followed September 29. OpenAI positions Sol as lower-cost professional work, while your task results determine the trade-off.
Remove unsupported Astra request settings
Astra does not support none reasoning effort, custom temperature, top_p or logprobs. Validate each native request before evaluation so an unsupported parameter does not become a misleading quality or reliability result.
Evaluate the output artifact
For research, require source evidence. For code, require passing tests. For documents, require a complete deliverable. Record time, retries and usage through that stopping condition rather than through the first response.
Three criteria for this comparison
Task completion on complex multi-step work
Adapter compatibility with reasoning controls
Complete latency and additional cost per accepted artifact
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.
GPT-6 Astra
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.
