GPT-5.6 sol vs terra

Choose between gpt-5.6-sol and gpt-5.6-terra with an explicit API test plan. Compare observed results without assuming performance from model names.

Sol and terra are separate published request identifiers. Do not assume that either suffix guarantees a particular speed, reasoning budget or quality tier.

Run a controlled variant trial

Send the same small prompt first to gpt-5.6-sol, then to gpt-5.6-terra. Verify returned identity and completed output. Repeat on your real evaluation set, including edge cases.

curl --fail-with-body https://api.useinfergate.com/v1/responses \
  -H "Authorization: Bearer $INFERGATE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"gpt-5.6-sol","input":"Reply with one sentence about API design.","max_output_tokens":256}'

Decision record

Variant details

Prepaid usage and costs

API usage consumes available account credit. Review current input and output rates in application pricing before running a workload. This page does not promise free requests or a fixed discount. See how prepaid pricing works and compare actual usage logs after a small test.

Evaluation worksheet

MeasureHow to collect itInterpretation
Task correctnessScore outputs against a fixed rubricPrefer repeatable task success over a single impressive answer
CompletionRecord status and useful textCount incomplete responses separately
LatencyRecord first-text and completion timesUse a distribution, not one fastest sample
CostReconcile completed requests with usage recordsCompare cost per successful task

Runnable integration example

curl --fail-with-body https://api.useinfergate.com/v1/responses \
  -H "Authorization: Bearer $INFERGATE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"gpt-5.6-terra","input":"Return a short release note for an API client update.","max_output_tokens":256}'

Frequently asked questions

Which model should I choose?

Choose from your task results, current account access and observed cost. The page provides a test method, not unsupported performance rankings.

Can I compare models using only one prompt?

One request is useful for checking connectivity and identity. It is insufficient for a quality or latency ranking. Include representative tasks and repeated runs.

Should I use identical settings?

Keep inputs and output budgets comparable, and record any settings that differ. Do not force optional parameters onto a model before confirming support.

Continue your integration

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