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Benchmark
Jan 26, 2026
Terminal X Outperforms Claude, GPT, and Gemini on Retail Investment Queries
Retail investors need AI that delivers accurate, timely, and actionable insights. While out of the box LLMs like ChatGPT, Claude, and Gemini have shown impressive gains in general intelligence, coding proficiency, and even competition math and physics, our research finds that these same models fall short when answering financial queries. General-purpose LLMs frequently misidentify tickers, cite outdated information, and give vague responses when analyzing US market exposure or providing investment guidance. These errors directly impact investment decisions and leave the retail cohort worse off.
Our findings present Terminal X as a highly accurate, context-aware solution to the shortcomings of general purpose LLMs. We benchmarked three AI platforms (ChatGPT 5.0, Claude 4.5 Sonnet, and Gemini 2.5 Pro) across 500 natural language financial queries from real retail investors and compared their performance to Terminal X across 7 key categories.
Terminal X achieved a 89.9% composite accuracy score, outperforming the next best general purpose chatbot by +19.3 percentage points. The largest gaps emerged in Tier 1 source authority (49% vs 5.8%) and temporal relevance (99.8% vs 72.4%). See our full analysis and findings below.
The results show that general-purpose chatbots—despite their strengths across various domains—fall short when it comes to financial research. They default to public and secondary sources available to all parties, miss temporal nuances in queries, and are designed to intentionally hedge their responses, leading to unclear and confusing financial insights.
Terminal X was designed specifically for investment analysis, giving retail users access to information previously only available to institutions and professional investors. From our backend data indexing, to the thousands of source-specific prompts and retrieval systems tuned to prioritize authoritative financial data, we provide the context that retail investors actually need. To demo our full institutional service suite, request an enterprise trial at Terminal X AI.
In the financial sector, an answer is only as valuable as the data supporting it. Source authority evaluates the credibility and hierarchy of sources used, focusing on whether the core information originates from high-trust institutions, primary data, or top-tier research, even if accessed via reputable aggregators. We scored responses using a three tier system:
Terminal X drew from Tier 1 sources in 49% of responses. General purpose chatbots relied heavily on Tier 2 and Tier 3 sources, with Claude showing the weakest source discipline at just 0.4% Tier 1 citations, followed by Gemini at 2.4% and ChatGPT at 5.8%.
General purpose LLMs are, by design, limited in their ability to source and utilize institutional grade research and information. Models are trained on publicly accessible (and often outdated) information, or source their information from the open internet, often resulting in untrustworthy and biased information. The fundamental advantage of Terminal X is our access to the same institutional grade information that real analysts use in their workflows. This ensures that every answer is grounded in verified, auditable, and professional information.
Temporal relevance measures whether the model correctly addresses the specific time horizon mentioned in the query. Retail investors often ask about short term time frames, temporally bound questions (“December”, “today”), or long term, multi-year outlooks. Providing generic analysis when the user specified a timeframe constituted a failure. Additionally, most models are released with a specific training cutoff, making them prone to hallucinations or losing their sense of the “current moment”, misrepresenting facts that were only true during their last update.
Terminal X achieved 99.8% temporal relevance, a result of our strict, multi-stage prompting and response auditing methodology. Gemini failed to properly frame temporal context in over half of responses (56.2%), while ChatGPT scored 46.6% and Claude scored relatively higher at 72.4%.
Investment queries require the model to link specific stocks to broader market drivers like inflation data, interest rates, or sector trends mentioned in the prompt. Terminal X maintained a 99.8% accuracy rate in this category. General models often struggled to connect these dots, with Claude achieving 89.2% accuracy. Other models such as Gemini dropped to 49.2%. This performance gap indicates that general models frequently "tunnel vision" on a single company name while ignoring critical macroeconomic context, leading to technically correct but irrelevant answers.
Further, Terminal X achieved a significant advantage over base models in actionability (95% vs. ChatGPT’s 60.4%), which was measured as the ability for a retail user to make an informed decision based on the information and guidance provided in the response. The general purpose models frequently defaulted to "hedging", which delivered a balanced but vague summary of the source information, requiring the user to perform more analytical work. This was most evident in Gemini’s 25.2% score, where the model consistently returned “non-committal” responses and data that lacked analytical depth and rigor.
Across the remaining evaluation dimensions, Terminal X maintained strong performance. Ticker identification accuracy reached 88.4% versus 79.8% for ChatGPT and 79.6% for Claude. Subject identification was near-perfect at 99.4%, and query intent classification hit 98.2%.
We constructed a benchmark of 500 natural language retail investment queries spanning multiple intent types: Fact Request, Explanation, Analysis/Outlook, Comparison, Calculation, and Buy/Sell intent. Each query was submitted to all four platforms via their web interfaces with default settings. Responses were evaluated across seven dimensions by a team of independent human evaluators.
Terminal X demonstrated consistent advantages over general purpose LLMs in every dimension relevant to investment research. The 89.9% composite accuracy versus 70.6% for the next best performing model (Claude 4.5 Sonnet) across a sample of 500 queries represents a meaningful difference in reliability. We find that base LLM models are not optimized for accurate financial research and analysis. They treat financial information and data with the same flexibility and interchangeability as any other low-stakes conversation, which leads to the high failure rates present across the 7 key evaluation metrics.
These findings validate why the Terminal X team prioritizes retrieval accuracy and primary data sourcing as the foundation for a strong answer. When models are provided with the correct, ground-truth data paired with finance-aware prompting, they will provide meaningful analysis rather than the confident guess work we saw from the base model cohort.