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๐œยณ-Banking Benchmark Leaderboard

A fintech customer-support benchmark from the ๐œ-Knowledge framework that tests whether agents can navigate a large unstructured knowledge base and execute multi-step tool calls to resolve realistic banking workflows.
See example tasks

๐œยณ-Banking is the fintech customer-support domain of the ๐œ-Knowledge framework โ€” the third ๐œ-series benchmark from Sierra Research, extending ๐œ-Bench to settings where success requires both retrieving the right policy from a large unstructured knowledge base and executing the right multi-step sequence of tool calls.
The banking corpus spans ~700 interconnected policy documents (โ‰ˆ195K tokens, 21 product categories). Tasks include disputes, account freezes, provisional credits, and product changes โ€” workflows where resolving a single customer request often requires locating the relevant policy, reasoning over it, and executing several tool calls in sequence, sometimes including tools referenced only in the documentation rather than explicitly listed.
Outcomes are graded against backend database state โ€” whether a dispute was filed, a credit issued โ€” not conversational quality. We evaluate the full ๐œยณ-Banking suite (97 tasks) with 3 repeats per task and report pass@1 averaged across the repeats. Even frontier models with high reasoning budgets reach only ~25.5% pass@1 on the benchmark, with reliability degrading sharply across repeats.

All evaluations are conducted independently by Artificial Analysis. More information can be found on our Intelligence Benchmarking Methodology page.

Publication

View on arXiv

๐œ-Knowledge: Evaluating Conversational Agents over Unstructured Knowledge

Quan Shi, Alexandra Zytek, Pedram Razavi, Karthik Narasimhan, Victor Barres.

Conversational agents are increasingly deployed in knowledge-intensive settings, where correct behavior depends on retrieving and applying domain-specific knowledge from large, proprietary, and unstructured corpora during live interactions with users. Yet most existing benchmarks evaluate retrieval or tool use independently of each other, creating a gap in realistic, fully agentic evaluation over unstructured data in long-horizon interactions. We introduce ๐œ-Knowledge, an extension of ๐œ-Bench for evaluating agents in environments where success depends on coordinating external, natural-language knowledge with tool outputs to produce verifiable, policy-compliant state changes. Our new domain, ๐œ-Banking, models realistic fintech customer support workflows in which agents must navigate roughly 700 interconnected knowledge documents while executing tool-mediated account updates. Across embedding-based retrieval and terminal-based search, even frontier models with high reasoning budgets achieve only ~25.5% pass^1, with reliability degrading sharply over repeated trials. Agents struggle to retrieve the correct documents from densely interlinked knowledge bases and to reason accurately over complex internal policies. Overall, ๐œ-Knowledge provides a realistic testbed for developing agents that integrate unstructured knowledge in human-facing deployments.

๐œยณ-Banking

GPT-5.5 (xhigh) scores the highest on ๐œยณ-Banking with a score of 31.3%, followed by Claude Sonnet 4.6 (Adaptive Reasoning, Max Effort) with a score of 30.5%, and GPT-5.4 (xhigh) with a score of 30.3%

Score

๐œยณ-Banking Benchmark Leaderboard: Score

Benchmark developed by Sierra Research ยท Independently benchmarked by Artificial Analysis
Reasoning models are indicated by a lightbulb icon

Token Usage

๐œยณ-Banking Benchmark Leaderboard: Output Tokens per Task

Output tokens used to run one task, broken down by reasoning and answer tokens
Reasoning models are indicated by a lightbulb icon

The average number of answer and reasoning tokens produced per benchmark task in this evaluation.

CostUpdated

๐œยณ-Banking Benchmark Leaderboard: Cost per Task

Average cost per task (USD), broken down by input, cache hit, cache write, reasoning, and answer tokens
Reasoning models are indicated by a lightbulb icon

Average cost per task in the evaluation. Costs are split by input, cache hit, cache write, reasoning, and answer token pricing where canonical token counts are available.

SpeedUpdated

๐œยณ-Banking Benchmark Leaderboard: Time per Task

Weighted average wall clock time (minutes) per task; excludes TTFT and execution time ยท Lower is better
Reasoning models are indicated by a lightbulb icon

The weighted average time (seconds) per evaluation task. This is calculated by dividing output tokens per task by output speed, weighted by the relative weights of each benchmark in the evaluation.

Score vs. Release Date

๐œยณ-Banking Benchmark Leaderboard: Score vs. Release Date

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