# Behavioral Consistency and Transparency Analysis on Large Language Model API Gateways

arXiv: https://arxiv.org/abs/2604.21083 · HTML: https://mlsanity.com/paper/2604.21083/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 32 references that are in the map.
- First posted on arXiv 22 Apr 2026.

## Builds on (most cited first, 14 of 14)

- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Measuring Mathematical Problem Solving With the MATH Dataset](https://mlsanity.com/paper/2103.03874/) (2021)
- [AceMath: Advancing Frontier Math Reasoning with Post-Training and Reward Modeling](https://mlsanity.com/paper/2412.15084/) (2024)
- [Instructional Fingerprinting of Large Language Models](https://mlsanity.com/paper/2401.12255/) (2024)
- [Artificial Intelligence Index Report 2025](https://mlsanity.com/paper/2504.07139/) (2025)
- [METAL: Metamorphic Testing Framework for Analyzing Large-Language Model Qualities](https://mlsanity.com/paper/2312.06056/) (2023)
- [Auditing Prompt Caching in Language Model APIs](https://mlsanity.com/paper/2502.07776/) (2025)
- [SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation](https://mlsanity.com/paper/2506.12699/) (2025)
- [ManiFPT: Defining and Analyzing Fingerprints of Generative Models](https://mlsanity.com/paper/2402.10401/) (2024)
- [UTF:Undertrained Tokens as Fingerprints A Novel Approach to LLM Identification](https://mlsanity.com/paper/2410.12318/) (2024)
- [AuditLLM: A Tool for Auditing Large Language Models Using Multiprobe Approach](https://mlsanity.com/paper/2402.09334/) (2024)
- [Invisible Tokens, Visible Bills: The Urgent Need to Audit Hidden Operations in Opaque LLM Services](https://mlsanity.com/paper/2505.18471/) (2025)
- [Predictive Auditing of Hidden Tokens in LLM APIs via Reasoning Length Estimation](https://mlsanity.com/paper/2508.00912/) (2025)
- [FDLLM: A Dedicated Detector for Black-Box LLMs Fingerprinting](https://mlsanity.com/paper/2501.16029/) (2025)

## Built on by (most cited first, 1 of 1)

- [KBF: Knowledge Boundary as Fingerprint for Language Model and Black-Box API Auditing](https://mlsanity.com/paper/2605.29524/) (2026)

## Abstract (arXiv)

Third-party Large Language Model (LLM) API gateways are rapidly emerging as unified access points to models offered by multiple vendors. However, the internal routing, caching, and billing policies of these gateways are largely undisclosed, leaving users with limited visibility into whether requests are served by the advertised models, whether responses remain faithful to upstream APIs, or whether invoices accurately reflect public pricing policies. To address this gap, we introduce GateScope, a lightweight black-box measurement framework for evaluating behavioral consistency and operational transparency in commercial LLM gateways. GateScope is designed to detect key misbehaviors, including model downgrading or switching, silent truncation, billing inaccuracies, and instability in latency by auditing gateways along four critical dimensions: response content analysis, multi-turn conversation performance, billing accuracy, and latency characteristics. Our measurements across 10 real-world commercial LLM API gateways reveal frequent gaps between expected and actual behaviors, including silent model substitutions, degraded memory retention, deviations from announced pricing, and substantial variation in latency stability across platforms.