What are Open Source LLMs for Finance?
Open source Large Language Models for finance are specialized AI systems designed to understand, analyze, and generate financial content with exceptional accuracy. These models excel at processing complex financial documents, performing quantitative analysis, regulatory compliance checking, and risk assessment. They leverage advanced reasoning capabilities to interpret market data, financial statements, and economic trends. This technology enables financial professionals to automate analysis, enhance decision-making, and build sophisticated fintech applications while maintaining transparency, cost-effectiveness, and the flexibility to customize for specific financial use cases.
DeepSeek-R1
DeepSeek-R1-0528 is a reasoning model powered by reinforcement learning (RL) that addresses the issues of repetition and readability. Prior to RL, DeepSeek-R1 incorporated cold-start data to further optimize its reasoning performance. It achieves performance comparable to OpenAI-o1 across math, code, and reasoning tasks, and through carefully designed training methods, it has enhanced overall effectiveness.
DeepSeek-R1: Advanced Financial Reasoning Powerhouse
DeepSeek-R1-0528 is a reasoning model powered by reinforcement learning (RL) that excels at complex financial analysis and mathematical computations. With 671B parameters and MoE architecture, it delivers exceptional performance in quantitative finance, risk modeling, and financial forecasting. The model's enhanced reasoning capabilities make it ideal for analyzing complex financial instruments, regulatory compliance, and multi-step financial calculations. Its 164K context length allows for processing extensive financial documents and reports.
Pros
- Exceptional mathematical and reasoning capabilities for financial analysis.
- 671B parameters with MoE architecture for complex computations.
- 164K context length for processing large financial documents.
Cons
- Higher computational requirements due to large parameter count.
- Premium pricing tier for enterprise financial applications.
Why We Love It
- It delivers unmatched reasoning power for complex financial analysis, making it the gold standard for quantitative finance and risk assessment applications.
Qwen3-235B-A22B
Qwen3-235B-A22B is the latest large language model in the Qwen series, featuring a Mixture-of-Experts (MoE) architecture with 235B total parameters and 22B activated parameters. This model uniquely supports seamless switching between thinking mode (for complex logical reasoning, math, and coding) and non-thinking mode (for efficient, general-purpose dialogue). It demonstrates significantly enhanced reasoning capabilities and excels in agent capabilities for precise integration with external tools.
Qwen3-235B-A22B: Versatile Financial Intelligence
Qwen3-235B-A22B features a sophisticated MoE architecture with 235B total parameters and 22B activated parameters, making it exceptionally efficient for financial applications. The model's unique dual-mode capability allows switching between thinking mode for complex financial reasoning and non-thinking mode for routine financial queries. Its enhanced agent capabilities enable seamless integration with financial tools, APIs, and databases, making it perfect for automated trading systems, financial reporting, and regulatory compliance workflows.
Pros
- Dual-mode operation optimizes performance for different financial tasks.
- MoE architecture balances power with computational efficiency.
- Excellent agent capabilities for financial tool integration.
Cons
- Complex setup required for optimal financial tool integration.
- May require fine-tuning for highly specialized financial domains.
Why We Love It
- It offers the perfect balance of reasoning power and efficiency, with flexible modes that adapt to both complex financial analysis and routine financial operations.
Qwen/QwQ-32B
QwQ is the reasoning model of the Qwen series. Compared with conventional instruction-tuned models, QwQ, which is capable of thinking and reasoning, can achieve significantly enhanced performance in downstream tasks, especially hard problems. QwQ-32B is the medium-sized reasoning model, which is capable of achieving competitive performance against state-of-the-art reasoning models, e.g., DeepSeek-R1, o1-mini.
QwQ-32B: Efficient Financial Reasoning
QwQ-32B is a specialized reasoning model with 32B parameters that excels at financial problem-solving and analytical tasks. Despite its more compact size compared to larger models, it delivers competitive performance in mathematical computations, financial modeling, and risk assessment. The model's enhanced reasoning capabilities make it particularly effective for financial planning, investment analysis, and credit scoring applications. Its efficient architecture makes it ideal for organizations seeking powerful financial AI capabilities with moderate computational requirements.
Pros
- Efficient 32B parameter model with strong reasoning capabilities.
- Cost-effective solution for mid-scale financial applications.
- Competitive performance against larger reasoning models.
Cons
- Smaller context window compared to larger financial models.
- May require additional training for highly specialized financial domains.
Why We Love It
- It provides exceptional financial reasoning capabilities in a compact, cost-effective package, making advanced financial AI accessible to smaller financial institutions and fintech startups.
Financial AI Model Comparison
In this table, we compare 2025's leading open source LLMs for finance, each with unique strengths for financial applications. For enterprise-scale financial analysis, DeepSeek-R1 provides unmatched reasoning power. For versatile financial operations, Qwen3-235B-A22B offers flexible dual-mode capabilities, while QwQ-32B delivers efficient financial reasoning for cost-conscious implementations. This comparison helps you choose the right model for your specific financial AI requirements.
| Number | Model | Developer | Subtype | SiliconFlow Pricing | Core Financial Strength |
|---|---|---|---|---|---|
| 1 | DeepSeek-R1 | deepseek-ai | Reasoning Model | Input: $0.5/M, Output: $2.18/M | Advanced quantitative analysis |
| 2 | Qwen3-235B-A22B | Qwen3 | Reasoning Model | Input: $0.35/M, Output: $1.42/M | Dual-mode financial intelligence |
| 3 | QwQ-32B | QwQ | Reasoning Model | Input: $0.15/M, Output: $0.58/M | Efficient reasoning at scale |
Frequently Asked Questions
Our top three picks for finance in 2025 are DeepSeek-R1, Qwen3-235B-A22B, and QwQ-32B. Each of these models stood out for their exceptional reasoning capabilities, mathematical precision, and proven performance in financial analysis and quantitative computing tasks.
For enterprise-scale quantitative analysis and complex financial modeling, DeepSeek-R1 is the top choice. For versatile financial operations requiring both complex reasoning and routine tasks, Qwen3-235B-A22B excels with its dual-mode capabilities. For cost-effective financial AI implementations, QwQ-32B provides excellent reasoning performance at a more accessible price point.