This page compares the two models on one job: reading a pasted A/B test results table and judging whether the lift is real or just noise. It ends with a fair way to test both on your own experiments.
Sep 8, 2026 · 9 min read
GPT-5.6 Terra is the safer first choice for turning a pasted A/B test table into a verdict, based on stronger performance reading and calculating from quantitative material. That evidence is indirect, since no benchmark tests either exact model on A/B-test significance itself. Claude Sonnet 5 stays cheaper and holds up better against harmless table changes, but independent testing found it keeps answering even when a table's evidence is missing or contradictory.
That split shows up in three places: a chart-reading benchmark where Terra scored far higher1, a finance benchmark where the two are close enough to call a tie2, and a structured-data reliability test that found Sonnet 5 rarely stopped when a table couldn't support an answer3. None of them tests A/B-test significance directly, which is the real gap in the public evidence.
For a live workflow, the safer split is stage by stage: use Terra for the first parsing pass on a messy or dense table, let either model write the plain-language explanation once the numbers are set, and always run the actual significance calculation through a deterministic statistics function rather than trusting either model's arithmetic. Save analyst review for any launch, medical, financial or regulatory decision.
You paste a variant's results table and need a first read fast. Terra tested stronger at extracting and calculating from dense quantitative material, so it is the safer opening pass before a deterministic check.
You run this at volume. Give either model a validated statistics function to call instead of trusting its own arithmetic, and keep the model's job to reading and explaining, not calculating.
Your reporting template changes often but the underlying fields stay complete. Sonnet 5 held its answers steadiest when valid data was reordered or reformatted in independent testing.
A ship or no-ship call rides on the result. Neither model has a published record proving it knows when to abstain, so an analyst should check the extracted counts and the test before anyone acts.
This page compares the two models through their API in one neutral setup, not one model wrapped inside one app against the other wrapped inside a different one.
The parts that matter for this job are reading the counts and rates correctly, picking a defensible test, calculating the lift and the interval right, telling a real effect from a chance blip, and knowing when the table is simply missing a field the analysis needs. Official docs come first, then independent benchmarks that test extraction, arithmetic and evidence handling under matched conditions.
Tool rows are left out of the spec table on purpose. A spreadsheet add-on, a code sandbox or a file-upload feature belongs to the app built around the model, not to the model itself, and the same model can behave differently depending on which one it sits inside. The one exception is tool or function calling, since handing extracted counts to a validated statistics function is a model-level capability both APIs support9, 5.
Both context windows are far bigger than one pasted table needs. Accuracy and evidence handling matter more than context size here.
Figures from Anthropic and OpenAI documentation, checked September 8, 2026. The two vendors price and tokenize differently, so treat any cross-model cost comparison as directional, not exact.
This is the main analysis: which model has the edge on each part of reading a results table and deciding if a lift is real, and what backs it up.
Better-choice calls map to what the sources actually tested. No public benchmark grades either exact model on A/B-test significance itself, so the first three rows are the closest available evidence, not a direct test of this task.
Build a small set from three to five real experiments, then add adversarial variants: a clear lift, a small-sample lift, a borderline result, a table missing a field, and a multi-metric test that needs a multiple-comparison warning.
Cover a large clearly significant lift, the same apparent lift with a small sample and a wide interval, a borderline result near your threshold, a table missing denominators or unit definitions, and a multi-variant test.
Give both models identical source text and ask for the metric type, the absolute lift, the relative lift, the confidence interval, the p-value and a plain-language caveat about what the result does and does not prove.
Set the same reasoning effort for both, and hand each model the same deterministic statistics function to call rather than letting it do the arithmetic alone. Calculate the reference answers yourself first, in R, Python or your experimentation platform.
Do not edit an output before scoring it. Check the extracted counts, the chosen test, whether it separated failure to reject from proof of no effect, and whether it flagged missing information. For a real launch call, use blind review by at least two analysts.
No published benchmark grades these two exact models on A/B-test significance. Here is what each nearby source actually measures, and how much weight to put on it.
Single-tester and community write-ups are a secondary signal only. None of the sources reviewed for this page treats A/B-test significance as its own benchmark task.
Terra does best with an explicit analysis contract and a higher effort setting for a one-off audit. Sonnet 5 needs the abstention rule stated as loudly as the calculation itself.
GPT-5.6 Terra works best with an explicit analysis contract at a higher reasoning effort for a one-off audit, or medium effort plus the shared statistics tool for volume work. Ask it to identify the metric type, numerator, denominator, unit of analysis and assignment design before it calculates anything, stop if a required field is missing, then call the supplied function and report the lift, interval, p-value and a plain caveat5.
Claude Sonnet 5 needs the abstention rule stated as clearly as the calculation itself. Tell it directly not to infer a missing sample size, event count, independence assumption or metric definition, and to output only a fixed marker plus the missing fields when the table cannot support one unique analysis. That instruction targets the exact unsupported-path behavior an independent benchmark documented for Sonnet 53.
A Claude Sonnet 5 prompt: a loud abstention rule
Before calculating anything, run an evidence-sufficiency check.
You must not infer sample sizes, event counts, independence,
test duration or metric definition.
If the table cannot support one unique analysis, output only
INSUFFICIENT_DATA and the missing fields.
If it can, call the supplied statistics function and explain
the result in cautious, non-technical language.A GPT-5.6 Terra prompt: an explicit analysis contract
Read the table without assuming missing values. Identify metric
type, numerator, denominator, unit of analysis and assignment
design.
If any required field is absent, stop and ask for it.
Otherwise call compare_proportions, then report absolute lift,
relative lift, 95% CI, two-sided p-value, practical
interpretation and caveats. Do not describe p>0.05 as proof of
no effect.Neither model is safe to trust unattended. The useful question is where each one adds cleanup work, and what to change in the prompt or the workflow.
One question first. Will a deterministic statistics function calculate the result, or is the model reasoning and calculating alone? Then follow the branch that matches your situation.
A starting point, not a rule. Test on your own experiments before you commit.
If a validated statistics function will do the actual calculating, the model choice comes down to parsing and price. Pick Claude Sonnet 5 when you need the lowest output-token cost or you are running many short explanations, and when the table's format changes often but the underlying fields stay complete. Pick GPT-5.6 Terra when the incoming tables are messy, dense or hard to parse1, 4, 5.
If the model has to reason and calculate without a shared tool, GPT-5.6 Terra is the safer general default on the adjacent evidence, and it should run at high or max effort. Missing-data detection stays the open question either way, so give Terra a hard abstention checklist rather than trusting it to flag a gap on its own, since no public result shows it will3, 7.
For a launch, medical, financial or regulatory decision, neither model replaces analyst review. Use whichever model reads faster as a first pass, then check the extracted counts, the chosen test and the interval against an independent calculation before anyone acts on it6, 7.
One case sits outside all of this. A solo analyst running occasional checks with no team to share results with gets little from Playgram's shared workspace, built around team memory and multi-person usage controls, so a single model subscription is the simpler and cheaper choice for that one person. The setup below earns its keep once more than one person needs the same comparison and the same context.
GPT-5.6 Terra is the better provisional choice for reading a pasted A/B test table and judging whether a lift is real. Its strongest advantage is quantitative extraction. Claude Sonnet 5 costs less and holds up better against harmless table changes, but it has the clearer documented tendency to keep answering when the table's evidence is not enough.
The important limit is that no direct public benchmark compares these exact models on A/B-test significance, and Terra has no published, exact-version result proving it knows when to stop. The benchmarks behind this page also differ in prompts, reasoning effort, available tools, graders and input type, and prices and behavior can change quickly, so treat every figure here as a starting point rather than a permanent fact1, 2, 3.
The safest final step is to test the shape of your own experiment data, not a generic prompt from the internet. A fair test needs the same setup for both models: the same table, the same prompt and the same place to run them, so the result reflects the models and not the tool around them. In practice that is harder than it sounds, since most teams end up running one model in one app and the other in a different one, on two separate subscriptions, which tilts the comparison before the first answer comes back. The cleaner the setup, the more the difference you see is really Claude Sonnet 5 vs GPT-5.6 Terra, and not just which one happened to be easier to reach that day.
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