Ebisu - Overall: leaderboard

Evaluates Japanese financial implicit commitment recognition (JF-ICR) and terminology extraction (JF-TE). This is the paper v1 result table, whose stated generation limit is 1,024; current repository task defaults differ. JF-ICR uses accuracy, JF-TE uses maximal-term F1 and hit rates at 1/5/10, and the mean combines five metrics. Published fractions are converted to percentages. Explicit zero scores are retained; malformed predictions can receive zero under the public evaluator. Source-specified dated checkpoints are retained. This series reports Ebisu - Overall using Mean Task Score (%).

Metric: Mean Task Score (%). Source: arxiv.org. Saturation forecast: Not forecast. 22 models tracked.

Top models

#ModelScoreOverall rank
1Llama 3.3 70B Instruct40.11#541
2Llama-3.3-Swallow-70B-Instruct-v0.438.51#540
3Llama 4 Scout37.78#648
4Claude Sonnet 4.536.18#148
5Kimi K235.9#252
6Qwen 3 235B A22B 2507 Instruct FP834.51#234
7Qwen 2.5 32B Instruct34.39#509
8DeepSeek V3.132.67#275
9Ministral-3-14B-Instruct-251228.87#625
10GPT-4o24.61#357
11Qwen 3 32B15.75#449
12Qwen 3 14B11.79#542
13GPT-511.06#105
14Qwen-14B8.57#1153
15Gemini 3 Flash7.87#103

No result here: #3 Claude Opus 5.5, #6 GPT-6 Astra, #7 GPT-6.1 Sol.

Interactive version: theaggregate.ai/benchmark?slug=ebisu-overall · How It Works · Data refreshed daily, snapshot 2026-11-05.