Qwen 3.8 Max and DeepSeek V4 Pro Clash in a Financial Reasoning Showdown

Overview

The competition in the field of artificial intelligence on the financial reasoning track is entering a fierce phase. Recently, the two cutting-edge models Qwen 3.8 Max and DeepSeek V4 Pro have been engaged in an intense rivalry on the latest released Trading IQ evaluation leaderboard. The data shows that the scores of the two models differ by only 0.1 points, which can be described as a narrow margin, fully demonstrating the top-tier strength of current domestic large models in processing complex financial logic.

This is a "mirror showdown" taking place at the technological peak, where two models, with almost identical scores, have reached a similar realm of excellence through different technical paths.

Core Data Comparison and Analysis

In terms of overall scores, the two sides are closely matched, but subtle details reveal the truth. Qwen 3.8 Max ranks first with a slightly superior overall score, while DeepSeek V4 Pro follows closely behind, with the gap between them being almost negligible, reflecting a high degree of technical maturity.

Return Performance and Decision Quality

In terms of actual return performance, Qwen 3.8 Max performed more brilliantly, achieving a return of +6.10%, significantly higher than the +5.36% of DeepSeek V4 Pro. Although this gap is not large, in the microscopic environment of high-frequency trading, every basis point of lead is highly valuable.

Regarding the density of trading decisions, Qwen 3.8 Max completed a total of 88 settlements, compared to DeepSeek V4 Pro's 51 settlements. This indicates that Qwen, while maintaining high precision, possesses higher trading activity and response frequency.

Settlement Share and Strategic Differences

It is worth noting that DeepSeek V4 Pro performed excellently in the quality of single decisions, with its occupied settlement share reaching 47%, slightly better than Qwen 3.8 Max's 43%. This "less but refined" strategy forms a sharp contrast with Qwen's "more but stable" style. The two paths, though different, ultimately point to similar high-score results.

Industry Impact and Prospects

This showdown is not just a simple performance test, but also marks that the deep implementation of artificial intelligence in the vertical financial field has entered a stage of refined comparison. For financial institutions, whether choosing a trading assistant that pursues high-frequency activity or a robust model that focuses on the quality of single decisions, both products provide reliable solutions.

With the acceleration of model iteration, the boundaries of financial reasoning will be further expanded in the future, and technological dividends are expected to be converted into broader market efficiency.