FinSearchComp T2&T3

FinSearchComp T2&T3 is a combined benchmark for evaluating financial search and reasoning capabilities on Tier 2 and Tier 3 tasks, testing models' ability to retrieve and analyze complex financial information using tools.

Kimi K2.5 from Moonshot AI currently leads the FinSearchComp T2&T3 leaderboard with a score of 0.678 across 1 evaluated AI models.

Moonshot AIKimi K2.5 leads with 67.8%.

Progress Over Time

Interactive timeline showing model performance evolution on FinSearchComp T2&T3

State-of-the-art frontier
Open
Proprietary

FinSearchComp T2&T3 Leaderboard

1 models
ContextCostLicense
1
Moonshot AI
Moonshot AI
1.0T262K$0.60 / $3.00
Notice missing or incorrect data?

FAQ

Common questions about FinSearchComp T2&T3.

What is the FinSearchComp T2&T3 benchmark?

FinSearchComp T2&T3 is a combined benchmark for evaluating financial search and reasoning capabilities on Tier 2 and Tier 3 tasks, testing models' ability to retrieve and analyze complex financial information using tools.

What is the FinSearchComp T2&T3 leaderboard?

The FinSearchComp T2&T3 leaderboard ranks 1 AI models based on their performance on this benchmark. Currently, Kimi K2.5 by Moonshot AI leads with a score of 0.678. The average score across all models is 0.678.

What is the highest FinSearchComp T2&T3 score?

The highest FinSearchComp T2&T3 score is 0.678, achieved by Kimi K2.5 from Moonshot AI.

How many models are evaluated on FinSearchComp T2&T3?

1 models have been evaluated on the FinSearchComp T2&T3 benchmark, with 0 verified results and 1 self-reported results.

What categories does FinSearchComp T2&T3 cover?

FinSearchComp T2&T3 is categorized under economics, finance, reasoning, and search. The benchmark evaluates text models.

More evaluations to explore

Related benchmarks in the same category

View all economics
GPQA

A challenging dataset of 448 multiple-choice questions written by domain experts in biology, physics, and chemistry. Questions are Google-proof and extremely difficult, with PhD experts reaching 65% accuracy.

reasoning
213 models
MMLU-Pro

A more robust and challenging multi-task language understanding benchmark that extends MMLU by expanding multiple-choice options from 4 to 10, eliminating trivial questions, and focusing on reasoning-intensive tasks. Features over 12,000 curated questions across 14 domains and causes a 16-33% accuracy drop compared to original MMLU.

finance
119 models
AIME 2025

All 30 problems from the 2025 American Invitational Mathematics Examination (AIME I and AIME II), testing olympiad-level mathematical reasoning with integer answers from 000-999. Used as an AI benchmark to evaluate large language models' ability to solve complex mathematical problems requiring multi-step logical deductions and structured symbolic reasoning.

reasoning
107 models
MMLU

Massive Multitask Language Understanding benchmark testing knowledge across 57 diverse subjects including STEM, humanities, social sciences, and professional domains

finance
99 models
SWE-Bench Verified

A verified subset of 500 software engineering problems from real GitHub issues, validated by human annotators for evaluating language models' ability to resolve real-world coding issues by generating patches for Python codebases.

reasoning
89 models
Humanity's Last Exam

Humanity's Last Exam (HLE) is a multi-modal academic benchmark with 2,500 questions across mathematics, humanities, and natural sciences, designed to test LLM capabilities at the frontier of human knowledge with unambiguous, verifiable solutions

reasoningmultimodal
74 models