| # Results — 2026-08-16 (v2, four models) |
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| Ten arithmetic problems, four Anthropic models, no tools, no MCP, single-shot |
| answers. Expected values computed in code, never by a model. |
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| v1 (2026-08-12) ran only the two ends of the range, claude-opus-5 and |
| claude-haiku-4-5. This run adds claude-sonnet-5 and claude-fable-5 and repeats |
| all four in one sitting, so "same questions, same prompt, same isolation, only |
| the model changed" still holds across the whole table. The v1 headline numbers |
| reproduced: opus 10/10, haiku 3/10. |
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| **Note on claude-fable-5:** it is the model that ran this session. That does not |
| affect the score, because the ground truth is computed in `run.ts` and the model |
| never grades itself; it only submits an answer that the script checks. |
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| | | opus-5 | sonnet-5 | haiku-4-5 | fable-5 | |
| |---|---|---|---|---| |
| | answered | 10 / 10 | 10 / 10 | 10 / 10 | 10 / 10 | |
| | exact | **10 / 10** | 9 / 10 | **3 / 10** | 9 / 10 | |
| | within 1% | 10 / 10 | 10 / 10 | 10 / 10 | 10 / 10 | |
| | worst error | 0.00% | 0.02% | **0.145%** | 0.00% | |
| | reasoning tokens (total) | 7,771 | 34,520 | **58,027** | 8,274 | |
| | reasoning tokens (median) | **936** | 3,281 | 7,051 | 1,148 | |
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| "Exact" means the answer differs from the computed value by no more than display |
| rounding (relative error ≤ 1e-6). |
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| ## Per task |
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| | task | expected | opus-5 | sonnet-5 | haiku-4-5 | fable-5 | |
| |---|---|---|---|---|---| |
| | `mrr-0` growth then churn, 36 mo | 38,115.31 | 38,115.31 | 38,115.31 | 38,115 (0.001%) | 38,115.34 | |
| | `cash-1` revenue vs fixed burn, 60 mo | 4,864,506.92 | 4,864,506.92 | 4,864,506.96 | 4,864,544.82 (0.001%) | 4,864,506.92 | |
| | `ltv-2` one division | 7,800.00 | 7,800 | 7,800 | 7,800 | 7,800 | |
| | `two-phase-3` growth switches at m6, 60 mo | 18,578.75 | 18,578.75 | 18,578.73 | 18,553 (0.139%) | 18,578.75 | |
| | `runway-4` stepped burn, month number | 5 | 5 | 5 | 5 | 5 | |
| | `mrr-5` growth then churn, 12 mo | 14,887.45 | 14,887.45 | 14,887.45 | 14,887.54 (0.001%) | 14,887.46 | |
| | `cash-6` revenue vs fixed burn, 60 mo | 5,822,959.30 | 5,822,959 | 5,822,959.31 | 5,831,410 (0.145%) | 5,822,959 | |
| | `ltv-7` one division | 2,346.84 | 2,346.84 | 2,347.37 (0.022%) | 2,346.32 (0.022%) | 2,346.84 | |
| | `two-phase-8` growth switches at m6, 60 mo | 21,262.90 | 21,262.90 | 21,262.91 | 21,271 (0.038%) | 21,263 | |
| | `runway-9` stepped burn, month number | 11 | 11 | 11 | 11 | 11 | |
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| ## What the numbers say |
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| **Nobody refuses and nobody collapses.** All four answered every question and |
| every answer landed within 0.145% of the truth, which is already at odds with |
| the shorthand that language models cannot do arithmetic. |
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| **"To the cent" is where they separate.** Exact matches: opus 10, sonnet 9, |
| fable 9, haiku 3. The difference is drift, not failure. haiku was never wildly |
| wrong, it was slightly wrong seven times out of ten. On `cash-6` that slight |
| wrongness is **$8,451 off** a five-year cash forecast, and nothing in the answer |
| signals it happened. The number simply looks right. |
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| **More thinking did not buy accuracy, it went the other way.** haiku spent the |
| most reasoning of any model, 58,027 tokens, roughly 7.5x opus's 7,771, and |
| finished last at 3/10. opus was both the most accurate and the cheapest to |
| think. fable landed 9/10 on 8,274 tokens; sonnet landed the same 9/10 but spent |
| 34,520 getting there. |
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| **Difficulty concentrates where the rule changes.** The growth-switches-at- |
| month-6 problems and the long cash forecasts produced haiku's largest errors. |
| Straight compounding and single divisions were handled cleanly by all four. |
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| ## Scope |
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| Ten problems, four models, one sitting, single-shot answers. This compares four |
| models on identical tasks. It does not establish a failure rate for any of them, |
| and says nothing about behaviour with tools enabled, which is how anyone would |
| actually run these in production. It measures the model alone, on purpose. |
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