BonuslyBench · Model Recommendations · September 2026
Which Models Fit Which Work
Work-type recommendations drawn from measured performance: 95 fully-tested models × 40 real GTM tasks, deterministically scored. Match the model to the type of work you're doing — the data shows clear specialists.
📖 How to use this: find the category that matches your workload below, start with the #1 model for that work, and sanity-check against your own data before committing. Scores come from one company's GTM data distribution (redacted) — treat them as a strong prior, not a guarantee. Every model name links to its full answer-by-answer record.
Top 5 by Category of Work
The eight categories are the actual archetypes of GTM/RevOps work — each backed by five tests with planted real-world traps.
Coming from Anthropic or OpenAI? The First Five Open Models
If you're evaluating open-source models for production work for the first time, these five give you frontier-comparable output on operational work at 1–3% of frontier cost. Ranked by overall score with a breadth floor (the worst category must also be strong):
Heavy multi-table reconciliation (rollforwards, weighted forecasts, cross-system audits): the top reporting-analytics models separated from the pack by 5–10 points on these tests. Don't default to a chat-tuned model for scheduled pipeline jobs.
Executive communication (digests, Slack summaries, RFP answers): this is where cheap models punch hardest — the top five communication models include three under $1 for the entire suite.
Speed-sensitive embeds (CRM hooks, in-app enrichment): look at the speed map in the graphs folder — the fast-and-accurate quadrant (Qwen 3.8 Flash, MiMo, GLM 5.3 Flash, DeepSeek V4 Flash) delivers 0.93+ at median latencies under 30s.
Judgment calls on messy data (renewal risk with conflicting dates, churn-save eligibility): the "aha" tests showed even the best models miss 3–4 of 40 — keep a human review layer for high-stakes calls.
Cost discipline
The value kings (17 models ≥0.93 mean at ≤$2) make premium pricing hard to justify for operational workloads. Measure your own workload: the $/point spread in this study was 1,000×.
Reasoning-token-heavy "pro" models (5-pro/5.2-pro/5.4-pro class) cost $90–130 per suite here and did not outscore the top open models on these tasks — their premium buys latency consistency and vendor SLAs, not measurable accuracy on operational work.
Guardrails that matter regardless of model
Entity whitelist validation: 18 of ~100 models fabricated at least one deal/company alias across ~4,600 graded answers. A deterministic alias check caught every instance — it costs nothing to run.
Deterministic spot-audit: re-derive expected numbers from source data on a schedule; the biggest scores we saw from any model still contained subtle misses on the hardest tests.
"Cannot be determined" acceptance: reward models for saying the data doesn't support a conclusion — the best performers distinguished underdetermined questions; many mid models fabricated instead.
⚖️ These are recommendations derived from measured behavior on GTM work, per the study's framing: archetypes and category leaders are acknowledgements of performance, and your own data distribution may shift the order within the top tier. Verify with 20–40 tests of your own before committing spend.