Best Models for Financial Analysis & Trading Decisions

For Financial Analysis & Trading Decisions, the practical quality bar is 8.0 out of 10. The evidence shows a trade-off rather than a universal winner: Grok-4.5 offers the strongest measured balance of trading-recommendation quality and synchronous cost, while GPT-5.5 and Gemini-3.5-flash are credible alternatives when filing-analysis quality or answer rates matter more than price. No single model dominates both the broad filing-analysis and the trading-recommendation workflows, and the category verdict changes once response behavior is included.

Where quality is highest. On trading recommendations, Grok-4.5 scores 8.63 out of 10 at $0.02 per synchronous task run [^1], ahead of GPT-5.5 at 8.61 out of 10 at $0.56 [^1], Kimi-k3 at 8.33 at $0.11 [^1], and GPT-5.4-nano at 7.84 at $0.0083 [^1]. On document-based financial analysis, Grok-4.5 reaches 9.19 on SEC filing chunks at $0.03 per task [^2] and 8.74 on SEC filing analysis at $0.02 [^3]. Gemini-3.5-flash scores 9.02 on filing analysis when it answers on 86% of attempts [^2], and GPT-5.5 scores 9.01 (±0.15) on filing analysis [^2][^3]. Within measurement uncertainty, the filing-analysis leaders are effectively tied: the ±0.15 interval around GPT-5.5’s 9.01 [^2][^3] overlaps both Gemini-3.5-flash’s 9.02 [^2] and Grok-4.5’s 9.19 [^2].

Cheapest option that clears the bar. Among the reported cost-quality pairs, Grok-4.5 at $0.02 per synchronous run [^1] is the cheapest model that exceeds 8.0 on trading recommendations. For filing chunks and filing analysis, Grok-4.5 costs $0.02–$0.03 [^3][^2]; no lower-cost result in the supplied evidence clears the bar at those task points. GPT-5.4-nano is cheaper at $0.0083 [^1] but scores 7.84, falling below the threshold; it is only viable when a lower quality level is acceptable.

Gap between the quality leader and the budget option. The spread depends on which tasks are compared. Within trading recommendations, Grok-4.5’s 8.63 [^1] at $0.02 is 0.79 points above GPT-5.4-nano’s 7.84 [^1] at $0.0083, with a cost gap of $0.0117 per task. Between Grok-4.5’s high filing-chunk score of 9.19 [^2] at $0.03 and the same budget option’s 7.84 [^1] at $0.0083, the quality gap is 1.35 points and the cost difference is $0.0217 per task. Because these figures come from different tasks—filing chunks versus trading recommendations—they identify the available frontier rather than a controlled paired comparison, and should be read directionally.

Task disagreement with the category-level impression. The broad finding is that Grok-4.5 is the best balance, but individual tasks revise that. For trading recommendations, Grok-4.5’s 8.63 [^1] is slightly ahead of GPT-5.5’s 8.61 [^1], yet GPT-5.5’s $0.56 cost [^1] makes it far more expensive per attempt, and Grok-4.5’s response behavior is not quantified in the supplied evidence. Meanwhile, Gemini-3.5-flash achieves 8.89 on trading recommendations with answers on 85% of attempts [^1], meaning roughly 15% of attempts produced no usable output; its filing-analysis result of 9.02 [^2] also carries an 86% answer rate [^2]. Thus on raw quality alone Gemini is competitive, but on reliable production delivery it is not equivalent to a model with a reported 1.00-style pipeline success rate—though the current evidence does not supply such a rate for Grok-4.5, either.

For long-horizon SEC-filing interpretation, the evidence points to a direct quality-versus-completion trade-off not fully captured by single scores: GPT-5.5’s 9.01 ±0.15 [^2][^3] and Grok-4.5’s 9.19 [^2] are close, but the cost structure differs and Gemini’s answer-rate ceiling limits effective throughput. The category-level preference for Grok-4.5 therefore holds for cost-sensitive combined workflows, yet filing-analysis steps that need high scores regardless of price could justify GPT-5.5 or Gemini-3.5-flash.

Which measured models are not competitive. GPT-5.4-nano is below the quality bar at 7.84 [^1]. Kimi-k3 clears it on trading recommendations at 8.33 [^1], but at $0.11 it is roughly five times more expensive per task than Grok-4.5 at $0.02 [^1] while delivering 0.30 fewer quality points. No reliability, confidence-interval, or success-rate evidence is supplied for Kimi-k3 or for GPT-5.4-nano, so neither can be recommended for production routes that require predictable output.

The table below summarizes the reported task-level evidence.

Reliability caveats and what the evidence cannot settle. The supplied results provide quality scores and some synchronous costs, but they do not provide a uniform reliability metric across all pairs: confidence intervals appear only for GPT-5.5 filing analysis [^2][^3]; answer-rate constraints appear only for Gemini-3.5-flash [^2][^1]; and no pipeline success rate, failure rate, or response-rate figure is stated for Grok-4.5, Kimi-k3, GPT-5.4-nano, or the remaining pairs. The benchmark is a standing measurement programme, and these published figures are not a one-off run; they represent the state of measurement at publication, with continuous updates ongoing. Because the underlying body of scored measurements for each result is not visible here, engineers should not treat the quality scores as guarantees of operational completion, and should weigh cost per successful usable output rather than cost per attempt alone.

The evidence also does not state how many tasks this category contains or how many models are measured across them, so category-wide counts cannot be reported. The figures should therefore guide routing decisions for the named workflows—trading recommendations, SEC filing chunks, filing analysis—rather than support a permanent overall league table.

Routing conclusion. For combined SEC-analysis and trading-recommendation workflows, default to Grok-4.5 when its $0.02–$0.03 synchronous cost [^3][^2][^1] fits the budget and its unreported response behavior has been validated in your environment. Use Gemini-3.5-flash when filing-analysis quality near 9.0 [^2] is required and you can handle unanswered requests at 14%–15% [^2][^1]. Reserve GPT-5.5 for filing-analysis steps where 9.01 ±0.15 [^2][^3] justifies its cost, but avoid it for high-volume trading recommendations because of its $0.56 per-task cost [^1]. Do not use GPT-5.4-nano for quality-critical routes because it scores 7.84 [^1], below the 8.0 bar, and do not select Kimi-k3 for trading recommendations unless its 8.33 [^1] at $0.11 is specifically required, since Grok-4.5 leads at 8.63 [^1] at lower cost.

The live benchmark is available at https://llm-bench.kapualabs.com/ for checking task-level quality, cost, and response rates as they change. Set the quality bar your production system actually needs there, and compare batch against synchronous pricing yourself.