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19/08/2026 3:43 pm
TL;DR: Model choice changes more by task family than by headline benchmark scores, with Sol and Terra nearly tied at about 83% while symbolic rules sharply separate them, according to Braintrust’s evaluation of GPT-5.6, Anthropic Fable, Opus 4.8, and Sonnet 5 on 225 machine-checkable tasks. The practical lesson is that exactness, latency, and retry tolerance now matter as much as capability when routing agent substeps.
NHIMG editorial — based on content published by Braintrust: Evaluating the GPT-5.6 family
By the numbers:
- Sol and Terra were both around 83% overall, while Luna trailed at about 68% in the GPT-5.6 family.
Questions worth separating out
Q: How should teams route models for exact, machine-checked tasks?
A: Route by task family, difficulty, and acceptable error cost.
Q: Why do model rankings change so much across task types?
A: Because model strengths are not uniform across all work.
Q: What do security teams get wrong about AI model evaluation?
A: They often collapse quality into a single score and ignore output format, refusals, and latency.
Practitioner guidance
- Define routing rules by task family Separate exact, structured work such as arithmetic, rule following, and data transforms from open-ended tasks, then assign acceptance thresholds per family rather than per vendor model.
- Track refusals separately from wrong answers Measure refusal rate, malformed output rate, and incorrect-answer rate as distinct control signals so you can tell whether a model is failing safely or simply failing to produce usable output.
- Build retry logic around hard cells Assume some task and difficulty combinations will not clear your reliability bar on the first pass.
What's in the full report
Braintrust's full blog post covers the operational detail this post intentionally leaves for the source:
- The per-cell decision map showing which model clears the reliability bar for each task family and difficulty level.
- The latency and cost breakdown behind the routing recommendations, including the trade-off between Sol and Terra for time-sensitive paths.
- The statistical model used to test whether Terra is meaningfully worse than Sol, which matters if you need evidence for model selection policy.
- The refusal analysis that separates non-answers from incorrect answers, useful when building acceptance criteria for automated workflows.
👉 Read Braintrust's evaluation of GPT-5.6 family routing for exact tasks →
GPT-5.6 routing decisions: when do exact tasks need a flagship model?
Explore further
19/08/2026 4:21 pm
Task-level model routing is becoming an identity-adjacent governance problem. As AI systems move into operational workflows, the model is no longer just a text generator. It becomes a decision component inside chains that may touch access reviews, policy triage, and automated evidence handling. That means the control question is not only which model is best, but which model is acceptable for each step of a governed workflow. The practitioner conclusion is that routing policy now belongs in the control plane.
A question worth separating out:
Q: How should teams decide whether a cheaper model is good enough?
A: Compare unique vulnerabilities found per dollar, not just per-run recall. If a mid-tier model repeated three times matches or beats a flagship once, it may be the better operational choice. The real decision is about stable yield, analyst effort, and how much variance your programme can absorb.
👉 Read our full editorial: Braintrust's GPT-5.6 eval shows routing matters more than raw model size
