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.
ComplexConstraints Leaderboard
1
GPT
6 Astra (Max reasoning)
57.7
%
1
Gemini
4 Argon (High reasoning)
53.3
%
1
Muse
Spark 1.3 (xHigh reasoning)
51.9
%
1
GPT
6 Sol (Max reasoning)
50.8
%
1
GPT
5.6 Sol (Max reasoning)
50.5
%
1
Claude
Opus 5.5 (Adaptive/Max)
49.9
%
1
DeepSeek
V4.1 Flash (Max reasoning)
49.7
%
1
GPT
5.5 (xHigh reasoning)
49.5
%
1
Grok
4.7 (xHigh reasoning)
48.9
%
1
Gemini
3.8 Flash (High reasoning)
48.4
%
1
Hy
Hy4 Preview (High reasoning)
46
%
1
GPT
5.4 (xHigh reasoning)
46
%
1
Qwen
3.8 Max (xHigh reasoning)
45.5
%
1
Claude
Fable 5.1 (Adaptive/Max)
45.1
%
1
Gemini
3.1 Pro (High reasoning)
43.7
%
1
Qwen
3.8 Flash (xHigh reasoning)
43.3
%
1
Gemini
3.7 Flash (High reasoning)
42.4
%
1
DeepSeek
V4 Pro (Max reasoning)
42
%
1
Gemini
3.6 Flash (High reasoning)
40
%
1
DeepSeek
V4 Flash Vision (experimental) (Max reasoning)
39.9
%
1
Muse
Spark 1.2 (xHigh reasoning)
39.9
%
1
Muse
Spark 1.1 (xHigh reasoning)
38.1
%
1
Claude
Fable 5 (Max reasoning)
38.1
%
1
Kimi
K3 (Max reasoning)
37.9
%
1
Claude
Opus 5 (Adaptive/Max)
37.3
%
1
Grok
4.6 (xHigh reasoning)
36.5
%
1
Grok
4.5 (High reasoning)
35.9
%
1
Claude
Opus 4.8 (Max reasoning)
35.6
%
1
Hy
Hy3 (High reasoning)
34.3
%
1
Qwen
3.7 Max (Thinking on)
33.5
%
1
GLM
5.3 (Max reasoning)
30.5
%
1
Kimi
K2.6 (Thinking on)
30.1
%
1
GLM
5.2 (Max reasoning)
29.3
%
1
GLM
5.3 Flash (Max reasoning)
27.6
%
1
Muse
Glimmer 30B (xHigh reasoning)
23.9
%
1
Gemini
3.5 Flash-Lite (High reasoning)
23.2
%
1
Kimi
K2.5 (Thinking on)
18.2
%
1
Nemotron
3 Ultra
17.8
%
1
Qwen
3.5 Plus (Thinking on)
17.8
%
1
Grok
4.20 Beta
16.4
%
1
Ernie
5.1
14.7
%
1
Nemotron
Lightning 3.5 30B A3B (Thinking on)
7.1
%
1
Mistral
Large 3
0.4
%
1
Ernie
4.5 300B
0
%
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Agentic Coding
sudo L7
Coding agents are getting very good at being L3s. But do they have the judgment to become L7s? Across 60 tasks, agents must navigate unfamiliar systems, choose architectures, spot hidden risks, and know when technically correct code is still the wrong thing to ship.
Rank
Model
Score
41.7
%
Claude Sonnet 5.5 (Adaptive/Max)
Anthropic
41.7
%
Claude Opus 5.5 (Adaptive/Max)
Anthropic
33.9
%
GPT 6 Astra (Max)
OpenAI
31.1
%
Claude Fable 5.1 (Adaptive/Max)
Anthropic
28.9
%
GPT 6.1 Sol (Max)
OpenAI
20.6
%
Qwen 3.8 Max
Alibaba Cloud
Agentic Finance
DAYJOB: Finance
DAYJOB: Finance measures long-horizon finance agents across corporate finance, banking, credit, investing, and real assets. It's built around the messy reality of knowledge work: realistic requests, complex environments, and end-to-end professional judgment.
Rank
Model
Score
23.9
%
Claude Opus 5.5 (Adaptive/Max)
Anthropic
21.5
%
GPT 6 Astra (Max)
OpenAI
20.3
%
Gemini 4 Argon (High)
Google
19.8
%
Claude Fable 5.1 (Adaptive/Max)
Anthropic
14.8
%
Muse Spark 1.3 (Max)
Meta
14.5
%
Grok 4.7 (xHigh)
xAI