arXiv:2512.14717v1 Announce Type: new 
Abstract: The rapid adoption of large language models in financial services necessitates rigorous evaluation frameworks to assess their performance, efficiency, and practical applicability. This paper conducts a comprehensive evaluation of the GPT-OSS model family alongside contemporary LLMs across ten diverse financial NLP tasks. Through extensive experimentation on 120B and 20B parameter variants of GPT-OSS, we reveal a counterintuitive finding: the smaller GPT-OSS-20B model achieves comparable accuracy (65.1% vs 66.5%) while demonstrating superior computational efficiency with 198.4 Token Efficiency Score and 159.80 tokens per second processing speed [1]. Our evaluation encompasses sentiment analysis, question answering, and entity recognition tasks using real-world financial datasets including Financial PhraseBank, FiQA-SA, and FLARE FINERORD. We introduce novel efficiency metrics that capture the trade-off between model performance and resource utilization, providing critical insights for deployment decisions in production environments. The benchmark reveals that GPT-OSS models consistently outperform larger competitors including Qwen3-235B, challenging the prevailing assumption that model scale directly correlates with task performance [2]. Our findings demonstrate that architectural innovations and training strategies in GPT-OSS enable smaller models to achieve competitive performance with significantly reduced computational overhead, offering a pathway toward sustainable and cost-effective deployment of LLMs in financial applications.

أدت السرعة في اعتماد نماذج اللغة الكبيرة (LLMs) في الخدمات المالية إلى إجراء تقييم شامل لعائلة نماذج GPT-OSS مقارنةً بنماذج LLM المعاصرة الأخرى عبر عشر مهام NLP المالية. تكشف الدراسة أن النموذج الأصغر GPT-OSS-20B يحقق دقة قابلة للمقارنة مع النماذج الأكبر بينما يظهر كفاءة حسابية متفوقة، مما يبرز مفارقة كفاءة مفاجئة في أداء النموذج.

La rápida adopción de grandes modelos de lenguaje (LLMs) en los servicios financieros ha llevado a una evaluación exhaustiva de la familia de modelos GPT-OSS en comparación con otros LLM contemporáneos en diez tareas de NLP financiero. El estudio revela que el modelo más pequeño GPT-OSS-20B logra una precisión comparable a la de modelos más grandes, al tiempo que demuestra una superior eficiencia computacional, destacando una sorprendente paradoja de eficiencia en el rendimiento del modelo.

L'adoption rapide des grands modèles de langage (LLMs) dans les services financiers a conduit à une évaluation complète de la famille de modèles GPT-OSS par rapport à d'autres LLMs contemporains sur dix tâches NLP financières. L'étude révèle que le modèle plus petit GPT-OSS-20B atteint une précision comparable à celle des modèles plus grands tout en montrant une efficacité computationnelle supérieure, mettant en lumière un paradoxe d'efficacité surprenant dans la performance des modèles.

The rapid adoption of large language models (LLMs) in financial services has prompted a comprehensive evaluation of the GPT-OSS model family against other contemporary LLMs across ten financial NLP tasks. The study reveals that the smaller GPT-OSS-20B model achieves comparable accuracy to larger models while demonstrating superior computational efficiency, highlighting a surprising efficiency paradox in model performance.

Is GPT-OSS All You Need? Benchmarking Large Language Models for Financial Intelligence and the Surprising Efficiency Paradox

One More Thing in AI – Your Shortcut to AI Mastery

Is GPT-OSS All You Need? Benchmarking Large Language Models for Financial Intelligence and the Surprising Efficiency Paradox

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