OpenAI
Eligible: the provider-reported training data/knowledge cutoff is before the earliest decision we used to test; a month-only cutoff counts as the first day of that month.Cutoff: February 16, 2026 (knowledge cutoff)GPT-5.6 Luna
Micro BrierMicro Brier is the headline metric: squared error of each dismissal forecast, averaged over all claim-defendant units. Lower is better; always answering 50% scores 0.25.
0.1432
Rank 7 of 18 · On the observed cost/quality frontier: no other model is both cheaper and at least as good, and this model strictly improves over others on at least one dimension. (Note that the improvement does not have to be statistically significant.)
Equal-case Brier
0.1508
Rank 10 of 18 · On the observed cost/quality frontier: no other model is both cheaper and at least as good, and this model strictly improves over others on at least one dimension. (Note that the improvement does not have to be statistically significant.)
Unit accuracy
80.9%
Rank 3 of 18 · On the observed cost/quality frontier: no other model is both cheaper and at least as good, and this model strictly improves over others on at least one dimension. (Note that the improvement does not have to be statistically significant.)
Cost and quality
High-confidence forecasts
182 of 387 predictions (47.0%) were at least 90% confident either way. 20 were wrong, an error rate of 11.0%. Mean stated confidence in that subset was 94.9%.
- Gemini 3.1 Pro PreviewGemini 3.1 Pro Preview: mean stated confidence 95.4%, realized accuracy 82.2% on 320 predictions57 / 320 (83% of units)
- GPT-5.6 SolGPT-5.6 Sol: mean stated confidence 97.0%, realized accuracy 88.6% on 228 predictions26 / 228 (59% of units)
- Gemini 3.8 FlashGemini 3.8 Flash: mean stated confidence 95.2%, realized accuracy 87.3% on 276 predictions35 / 276 (71% of units)
- GPT-5.6 LunaGPT-5.6 Luna: mean stated confidence 94.9%, realized accuracy 89.0% on 182 predictions20 / 182 (47% of units)
- GPT-6 LunaGPT-6 Luna: mean stated confidence 95.4%, realized accuracy 89.6% on 183 predictions19 / 183 (47% of units)
- GPT-6 AstraGPT-6 Astra: mean stated confidence 95.5%, realized accuracy 89.9% on 207 predictions21 / 207 (53% of units)
- GPT-6.1 SolGPT-6.1 Sol: mean stated confidence 95.7%, realized accuracy 91.0% on 199 predictions18 / 199 (51% of units)
- Muse Spark 1.3Muse Spark 1.3: mean stated confidence 93.5%, realized accuracy 88.9% on 135 predictions15 / 135 (35% of units)
- Muse WonderMuse Wonder: mean stated confidence 93.4%, realized accuracy 91.5% on 118 predictions10 / 118 (30% of units)
- Claude Sonnet 5Claude Sonnet 5: mean stated confidence 92.6%, realized accuracy 91.7% on 60 predictions5 / 60 (16% of units)
- GPT-6 SolGPT-6 Sol: mean stated confidence 95.6%, realized accuracy 95.3% on 169 predictions8 / 169 (44% of units)
- Claude Sonnet 5.5Claude Sonnet 5.5: mean stated confidence 91.6%, realized accuracy 92.1% on 63 predictions5 / 63 (16% of units)
- Grok 4.7Grok 4.7: mean stated confidence 92.9%, realized accuracy 95.9% on 98 predictions4 / 98 (25% of units)
- Claude Opus 5Claude Opus 5: mean stated confidence 93.6%, realized accuracy 96.7% on 121 predictions4 / 121 (31% of units)
- Kimi K3Kimi K3: mean stated confidence 92.8%, realized accuracy 98.9% on 92 predictions1 / 92 (24% of units)
- Grok 4.6Grok 4.6: mean stated confidence 93.0%, realized accuracy 99.1% on 112 predictions1 / 112 (29% of units)
- Claude Fable 5.1Claude Fable 5.1: mean stated confidence 92.9%, realized accuracy 100.0% on 81 predictions0 / 81 (21% of units)
- Claude Opus 5.5Claude Opus 5.5: mean stated confidence 92.8%, realized accuracy 100.0% on 65 predictions0 / 65 (17% of units)
Statistical comparison coverage
Included in the paired significance analysis.
Analysis and reproducible inputs →Statistically distinguishable differences
- Better than GPT-4.1 on Micro Brier, Equal-case Brier, and Unit accuracy.