DAYJOB: Tax A benchmark for AI tax agents

Frontier Research Mathematics

Riemann-bench

We evaluate AI models on advanced mathematical problems requiring deep reasoning and novel synthesis. Our benchmark features cutting-edge problems sourced from leading mathematicians, including Ivy League professors, PhD IMO medalists, and graduate students at the top of their field.

RL Environments and the Hierarchy of Agentic Capabilities
Our RL environment run on 9 models revealed the core capabilities all agents need to master: tool use, planning, adaptability, groundedness, and common sense.
Riemann-bench Leaderboard
1
GPT
5.6 Sol (Max reasoning)
83.2
%
1
GPT
6 Astra (Max reasoning)
81.6
%
1
Gemini
4 Carbon (High reasoning)
80
%
1
Claude
Opus 5.5 (Adaptive/Max)
80
%
1
GPT
6.1 Sol (Max reasoning)
79.2
%
1
Claude
Sonnet 5.5 (Adaptive/Max)
76.8
%
1
Gemini
4 Argon (High reasoning)
74.4
%
1
Claude
Fable 5.1 (Adaptive/Max)
71.2
%
1
Muse
Wonder Full (Max reasoning)
63.2
%
1
Kimi
K3.1 Pro (Max reasoning)
60.8
%
1
Muse
Wonder 1.0 (Max reasoning)
59.2
%
1
Grok
4.7 (xHigh reasoning)
57.6
%
1
Gemini
3.8 Flash (High reasoning)
56.8
%
1
Muse
Spark 1.3 (xHigh reasoning)
53.6
%
1
Qwen
3.8 Max (xHigh reasoning)
48
%
1
DeepSeek
V4.1 Flash 1017 (Max reasoning)
38.4
%
1
Beam
1.0 (Max reasoning)
37.6
%
1
Kimi
K3 (Max reasoning)
36.8
%
1
Hy
Hy4 Preview (High reasoning)
24
%
1
GLM
5.3 (Max reasoning)
20.8
%
1
Mistral
Large 4 (High reasoning)
20
%

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