# Best AI for Live financial market data (November 2026)

The leader on each benchmark in this use case, never combined. Data refreshed 2 Nov 2026.

## Picks

- FinSearchComp: grok-4: 68.9 Global subset average accuracy % (Paper Table 5, 16 Sept 2025), https://benchmarks.finance/leaderboards/finsearchcomp/
- CAIA (Crypto AI Agent Benchmark): gpt-5: 67.4 accuracy % (majority vote, 5 runs) (Paper Tables 2-3, 17 Jan 2026), https://benchmarks.finance/leaderboards/caia/

## FinSearchComp: Global subset average accuracy % (higher is better, 11 models)

| Rank | Model | Maker | Score | Metric | Setting | Reported by | Source | URL | Date |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 1 | grok-4 | xAI | 68.9 | Global subset average accuracy % | web product | Reported by the authors | Paper Table 5 | https://arxiv.org/html/2509.13160v1 | 16 Sept 2025 |
| 2 | GPT-5-Thinking | OpenAI | 63.9 | Global subset average accuracy % | web product | Reported by the authors | Paper Table 5 | https://arxiv.org/html/2509.13160v1 | 16 Sept 2025 |
| 3 | Numa 1 | Moneda | 59.4 | Global subset average accuracy % | Product with tools; historical-lookup tiers only (time-sensitive tier not run); two-judge panel | Run by Moneda | Moneda run, 2026-11-01 | https://benchmarks.finance/method/ | 1 Nov 2026 |
| 4 | Gemini2.5-pro | Google | 42.6 | Global subset average accuracy % | web product | Reported by the authors | Paper Table 5 | https://arxiv.org/html/2509.13160v1 | 16 Sept 2025 |
| 5 | DouBao | ByteDance | 39.1 | Global subset average accuracy % | web product | Reported by the authors | Paper Table 5 | https://arxiv.org/html/2509.13160v1 | 16 Sept 2025 |
| 6 | Gemini2.5-pro (Google Search) | Google | 38.2 | Global subset average accuracy % | API with search | Reported by the authors | Paper Table 5 | https://arxiv.org/html/2509.13160v1 | 16 Sept 2025 |
| 7 | Qwen3-235B-A22B-2507 | Qwen | 37.4 | Global subset average accuracy % | web product | Reported by the authors | Paper Table 5 | https://arxiv.org/html/2509.13160v1 | 16 Sept 2025 |
| 8 | YuanBao-DeepSeek-V3 | Not stated | 30.5 | Global subset average accuracy % | web product | Reported by the authors | Paper Table 5 | https://arxiv.org/html/2509.13160v1 | 16 Sept 2025 |
| 9 | DouBao-Thinking | ByteDance | 29.8 | Global subset average accuracy % | web product | Reported by the authors | Paper Table 5 | https://arxiv.org/html/2509.13160v1 | 16 Sept 2025 |
| 9 | HunYuan-T1-Thinking | Tencent | 29.8 | Global subset average accuracy % | web product | Reported by the authors | Paper Table 5 | https://arxiv.org/html/2509.13160v1 | 16 Sept 2025 |
| 9 | YuanBao-DeepSeek-R1 | Not stated | 29.8 | Global subset average accuracy % | web product | Reported by the authors | Paper Table 5 | https://arxiv.org/html/2509.13160v1 | 16 Sept 2025 |

## CAIA (Crypto AI Agent Benchmark): accuracy % (majority vote, 5 runs) (higher is better, 10 models)

| Rank | Model | Maker | Score | Metric | Setting | Reported by | Source | URL | Date |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 1 | gpt-5 | OpenAI | 67.4 | accuracy % (majority vote, 5 runs) | with tools | Reported by the authors | Paper Tables 2-3 | https://arxiv.org/html/2510.00332v2 | 17 Jan 2026 |
| 2 | GPT-OSS 120B | OpenAI | 62.9 | accuracy % (majority vote, 5 runs) | with tools | Reported by the authors | Paper Tables 2-3 | https://arxiv.org/html/2510.00332v2 | 17 Jan 2026 |
| 3 | Grok 4 Fast Reasoning | xAI | 61.2 | accuracy % (majority vote, 5 runs) | with tools | Reported by the authors | Paper Tables 2-3 | https://arxiv.org/html/2510.00332v2 | 17 Jan 2026 |
| 4 | Claude Opus 4 | Anthropic | 57.3 | accuracy % (majority vote, 5 runs) | with tools | Reported by the authors | Paper Tables 2-3 | https://arxiv.org/html/2510.00332v2 | 17 Jan 2026 |
| 5 | Claude Sonnet 4 | Anthropic | 56.7 | accuracy % (majority vote, 5 runs) | with tools | Reported by the authors | Paper Tables 2-3 | https://arxiv.org/html/2510.00332v2 | 17 Jan 2026 |
| 6 | Claude Opus 4.1 | Anthropic | 56.3 | accuracy % (majority vote, 5 runs) | with tools | Reported by the authors | Paper Tables 2-3 | https://arxiv.org/html/2510.00332v2 | 17 Jan 2026 |
| 7 | GPT-o3 | OpenAI | 53.4 | accuracy % (majority vote, 5 runs) | with tools | Reported by the authors | Paper Tables 2-3 | https://arxiv.org/html/2510.00332v2 | 17 Jan 2026 |
| 8 | DeepSeek V3.1 | DeepSeek | 49.2 | accuracy % (majority vote, 5 runs) | with tools | Reported by the authors | Paper Tables 2-3 | https://arxiv.org/html/2510.00332v2 | 17 Jan 2026 |
| 9 | Kimi K2 Instruct | Moonshot AI | 47.2 | accuracy % (majority vote, 5 runs) | with tools | Reported by the authors | Paper Tables 2-3 | https://arxiv.org/html/2510.00332v2 | 17 Jan 2026 |
| 10 | GPT-4.1 | OpenAI | 46.6 | accuracy % (majority vote, 5 runs) | with tools | Reported by the authors | Paper Tables 2-3 | https://arxiv.org/html/2510.00332v2 | 17 Jan 2026 |

## Also in this use case (no dated scores yet)

- Moneda GroundBench (Moneda): https://benchmarks.finance/benchmarks/moneda-groundbench/
- Moneda MarketBench (Moneda): https://benchmarks.finance/benchmarks/moneda-marketbench/

Source: https://benchmarks.finance/best/live-market-data/

Created and maintained by Moneda.