Which hosted API fits your coding and tool workflow?
This page compares the older editions named in its title. For current GPT-6, Claude 5.5 and Gemini 3.8 comparisons, open the current comparison library. The historical model identities are preserved for migration and reproducibility.
A hosted model family introduced for coding and instruction following.
Use a dated model snapshot for reproducible API comparisons. Responses and Chat Completions have different request shapes.
Official source ↗A hosted Claude model introduced for coding and agent workflows.
Native Messages requests differ from OpenAI Chat Completions. A compatibility layer must map tool and streaming behavior correctly.
Official source ↗Replay a small repository task set and inspect the actual patches and test results. Native endpoint differences matter. A benchmark snapshot does not prove success in your own agent harness.
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.
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.
Estimate token costs with the API calculator →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.
Enter all four values. Attempted tasks must be at least one; accepted tasks cannot exceed attempted tasks.
Enter all four values. Attempted tasks must be at least one; accepted tasks cannot exceed attempted tasks.
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.
For the API currently offered here, explore Qwen3.8 Flash 27B and the production evaluation guide.
How do coding and multimodal workflows change the comparison?
What should an agent or multimodal evaluation measure?
When should you choose open-weight serving over a hosted API?
