# Best AI for Analyst Financial research (November 2026)

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

## Picks

- Finance Agent Benchmark: Numa 1 (public set): 80.2 Partial Credit accuracy % (Moneda run, 2026-10-09, 9 Oct 2026), https://benchmarks.finance/leaderboards/vals-finance-agent/
- FinDeepResearch: FinAgentTest: 41.9 overall accuracy score (0-100) (OpenFinArena leaderboard, 23 Mar 2026), https://benchmarks.finance/leaderboards/findeepresearch/
- FrontierFinance: Samaya Sterling: 58.9 rubrics qualification rate % (Samaya leaderboard, 28 Oct 2026), https://benchmarks.finance/leaderboards/samaya-frontierfinance/
- Vals Finance Index: Gemini 4 Carbon: 66.3 index accuracy % (Vals.ai leaderboard, 29 Oct 2026), https://benchmarks.finance/leaderboards/vals-finance-index/
- FinResearchBench: Gemini: 76.6 Final Score (0-100) (Paper Table 1, 20 Oct 2025), https://benchmarks.finance/leaderboards/finresearchbench/
- Deep FinResearch Bench: OpenAI: 86 overall factual claim rate % (Paper Table 7, 22 Apr 2026), https://benchmarks.finance/leaderboards/deep-finresearch-bench/

## Finance Agent Benchmark: Partial Credit accuracy % (higher is better, 11 models)

| Rank | Model | Maker | Score | Metric | Setting | Reported by | Source | URL | Date |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 1 | Numa 1 (public set) | Moneda | 80.2 | Partial Credit accuracy % | Public set (27 questions), run by Moneda with its own grader (GPT-4o per rubric criterion); not a Vals result. Vals' other rows are its test set. One question is worth about 4 points. | Run by Moneda | Moneda run, 2026-10-09 | https://benchmarks.finance/method/ | 9 Oct 2026 |
| 2 | Claude Opus 5.5 | Anthropic | 67.8 | Partial Credit accuracy % | v2.1 test set | Official board | Vals.ai leaderboard | https://www.vals.ai/benchmarks/fabv2-1 | 29 Oct 2026 |
| 3 | Gemini 4 Argon | Google | 67.7 | Partial Credit accuracy % | v2.1 test set | Official board | Vals.ai leaderboard | https://www.vals.ai/benchmarks/fabv2-1 | 29 Oct 2026 |
| 4 | Wonder Full | Not stated | 67.6 | Partial Credit accuracy % | v2.1 test set | Official board | Vals.ai leaderboard | https://www.vals.ai/benchmarks/fabv2-1 | 29 Oct 2026 |
| 5 | Claude Sonnet 5.5 | Anthropic | 67.3 | Partial Credit accuracy % | v2.1 test set | Official board | Vals.ai leaderboard | https://www.vals.ai/benchmarks/fabv2-1 | 29 Oct 2026 |
| 6 | Gemini 4 Carbon | Google | 67.2 | Partial Credit accuracy % | v2.1 test set | Official board | Vals.ai leaderboard | https://www.vals.ai/benchmarks/fabv2-1 | 29 Oct 2026 |
| 7 | Gemini 3.8 Flash | Google | 65.8 | Partial Credit accuracy % | v2.1 test set | Official board | Vals.ai leaderboard | https://www.vals.ai/benchmarks/fabv2-1 | 29 Oct 2026 |
| 8 | Wonder | Not stated | 65.5 | Partial Credit accuracy % | v2.1 test set | Official board | Vals.ai leaderboard | https://www.vals.ai/benchmarks/fabv2-1 | 29 Oct 2026 |
| 9 | DeepSeek V4.1 Flash | DeepSeek | 65.4 | Partial Credit accuracy % | v2.1 test set | Official board | Vals.ai leaderboard | https://www.vals.ai/benchmarks/fabv2-1 | 29 Oct 2026 |
| 10 | Claude Haiku 5.5 | Anthropic | 65.4 | Partial Credit accuracy % | v2.1 test set | Official board | Vals.ai leaderboard | https://www.vals.ai/benchmarks/fabv2-1 | 29 Oct 2026 |
| 11 | GLM 5.3 | Zhipu AI | 65 | Partial Credit accuracy % | v2.1 test set | Official board | Vals.ai leaderboard | https://www.vals.ai/benchmarks/fabv2-1 | 29 Oct 2026 |

## FinDeepResearch: overall accuracy score (0-100) (higher is better, 10 models)

| Rank | Model | Maker | Score | Metric | Setting | Reported by | Source | URL | Date |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 1 | FinAgentTest | Not stated | 41.9 | overall accuracy score (0-100) | Deep Research | Official board | OpenFinArena leaderboard | https://openfinarena.com/fin-deep-research | 23 Mar 2026 |
| 2 | Gemini 3 Pro Deep Research | Google | 38 | overall accuracy score (0-100) | Deep Research | Official board | OpenFinArena leaderboard | https://openfinarena.com/fin-deep-research | 30 Dec 2025 |
| 3 | OpenAI o3-deep-research | OpenAI | 37.9 | overall accuracy score (0-100) | Deep Research | Official board | OpenFinArena leaderboard | https://openfinarena.com/fin-deep-research | 15 Oct 2025 |
| 4 | grok-4 | xAI | 37.3 | overall accuracy score (0-100) | Deep Research | Official board | OpenFinArena leaderboard | https://openfinarena.com/fin-deep-research | 15 Oct 2025 |
| 5 | Gemini 2.5 Pro | Google | 36.2 | overall accuracy score (0-100) | Deep Research | Official board | OpenFinArena leaderboard | https://openfinarena.com/fin-deep-research | 15 Oct 2025 |
| 6 | OpenAI GPT-5 | OpenAI | 33 | overall accuracy score (0-100) | Thinking + Search | Official board | OpenFinArena leaderboard | https://openfinarena.com/fin-deep-research | 15 Oct 2025 |
| 7 | Gemini 3 Pro | Google | 32.7 | overall accuracy score (0-100) | Thinking + Search | Official board | OpenFinArena leaderboard | https://openfinarena.com/fin-deep-research | 30 Dec 2025 |
| 8 | Tongyi Deep Research | Qwen | 32.1 | overall accuracy score (0-100) | Deep Research | Official board | OpenFinArena leaderboard | https://openfinarena.com/fin-deep-research | 15 Oct 2025 |
| 9 | claude-sonnet-4.5 | Anthropic | 25.8 | overall accuracy score (0-100) | Thinking + Search | Official board | OpenFinArena leaderboard | https://openfinarena.com/fin-deep-research | 15 Oct 2025 |
| 10 | Perplexity Sonar Deep Research | Perplexity | 24.7 | overall accuracy score (0-100) | Deep Research | Official board | OpenFinArena leaderboard | https://openfinarena.com/fin-deep-research | 15 Oct 2025 |

## FrontierFinance: rubrics qualification rate % (higher is better, 10 models)

| Rank | Model | Maker | Score | Metric | Setting | Reported by | Source | URL | Date |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 1 | Samaya Sterling | Samaya AI | 58.9 | rubrics qualification rate % | Samaya harness | Official board | Samaya leaderboard | https://samaya.ai/benchmarks/frontier-finance | 28 Oct 2026 |
| 2 | Legacy Samaya System (high effort) | Samaya AI | 56 | rubrics qualification rate % | Samaya harness | Official board | Samaya leaderboard | https://samaya.ai/benchmarks/frontier-finance | 28 Oct 2026 |
| 3 | Claude Fable 5.1 · Finance Agent v2 | Anthropic | 55.9 | rubrics qualification rate % | Finance Agent v2 harness | Official board | Samaya leaderboard | https://samaya.ai/benchmarks/frontier-finance | 28 Oct 2026 |
| 4 | Muse Spark Open · Finance Agent v2 | Meta | 53.7 | rubrics qualification rate % | Finance Agent v2 harness | Official board | Samaya leaderboard | https://samaya.ai/benchmarks/frontier-finance | 28 Oct 2026 |
| 5 | Legacy Samaya System | Samaya AI | 52.9 | rubrics qualification rate % | Samaya harness | Official board | Samaya leaderboard | https://samaya.ai/benchmarks/frontier-finance | 28 Oct 2026 |
| 6 | Muse Wonder 1.0 · Finance Agent v2 | Meta | 52.6 | rubrics qualification rate % | Finance Agent v2 harness | Official board | Samaya leaderboard | https://samaya.ai/benchmarks/frontier-finance | 28 Oct 2026 |
| 7 | Claude Opus 5.5 · Finance Agent v2 | Anthropic | 50.9 | rubrics qualification rate % | Finance Agent v2 harness | Official board | Samaya leaderboard | https://samaya.ai/benchmarks/frontier-finance | 28 Oct 2026 |
| 8 | Gemini 3.8 Flash · Finance Agent v2 | Google | 50 | rubrics qualification rate % | Finance Agent v2 harness | Official board | Samaya leaderboard | https://samaya.ai/benchmarks/frontier-finance | 28 Oct 2026 |
| 9 | Claude Fable 5 · Finance Agent v2 | Anthropic | 49.2 | rubrics qualification rate % | Finance Agent v2 harness | Official board | Samaya leaderboard | https://samaya.ai/benchmarks/frontier-finance | 28 Oct 2026 |
| 10 | GPT 5.6 Sol · Finance Agent v2 | OpenAI | 46.8 | rubrics qualification rate % | Finance Agent v2 harness | Official board | Samaya leaderboard | https://samaya.ai/benchmarks/frontier-finance | 28 Oct 2026 |

## Vals Finance Index: index accuracy % (higher is better, 10 models)

| Rank | Model | Maker | Score | Metric | Setting | Reported by | Source | URL | Date |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 1 | Gemini 4 Carbon | Google | 66.3 | index accuracy % |  | Official board | Vals.ai leaderboard | https://www.vals.ai/benchmarks/finance_index | 29 Oct 2026 |
| 2 | Claude Opus 5.5 | Anthropic | 65.6 | index accuracy % |  | Official board | Vals.ai leaderboard | https://www.vals.ai/benchmarks/finance_index | 29 Oct 2026 |
| 3 | Gemini 4 Argon | Google | 64.9 | index accuracy % |  | Official board | Vals.ai leaderboard | https://www.vals.ai/benchmarks/finance_index | 29 Oct 2026 |
| 4 | Wonder Full | Not stated | 64.5 | index accuracy % |  | Official board | Vals.ai leaderboard | https://www.vals.ai/benchmarks/finance_index | 29 Oct 2026 |
| 5 | Claude Sonnet 5.5 | Anthropic | 64.5 | index accuracy % |  | Official board | Vals.ai leaderboard | https://www.vals.ai/benchmarks/finance_index | 29 Oct 2026 |
| 6 | Qwen 3.8 Max 0902 | Qwen | 61.7 | index accuracy % |  | Official board | Vals.ai leaderboard | https://www.vals.ai/benchmarks/finance_index | 29 Oct 2026 |
| 7 | Wonder | Not stated | 61.2 | index accuracy % |  | Official board | Vals.ai leaderboard | https://www.vals.ai/benchmarks/finance_index | 29 Oct 2026 |
| 8 | DeepSeek V4.1 Flash 1017 | DeepSeek | 60.9 | index accuracy % |  | Official board | Vals.ai leaderboard | https://www.vals.ai/benchmarks/finance_index | 29 Oct 2026 |
| 9 | GLM 5.3 | Zhipu AI | 60.5 | index accuracy % |  | Official board | Vals.ai leaderboard | https://www.vals.ai/benchmarks/finance_index | 29 Oct 2026 |
| 10 | Gemini 3.8 Flash | Google | 60.5 | index accuracy % |  | Official board | Vals.ai leaderboard | https://www.vals.ai/benchmarks/finance_index | 29 Oct 2026 |

## FinResearchBench: Final Score (0-100) (higher is better, 8 models)

| Rank | Model | Maker | Score | Metric | Setting | Reported by | Source | URL | Date |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 1 | Gemini | Google | 76.6 | Final Score (0-100) | all 70 questions | Reported by the authors | Paper Table 1 | https://arxiv.org/html/2507.16248v3 | 20 Oct 2025 |
| 2 | OpenAI | OpenAI | 74.2 | Final Score (0-100) | all 70 questions | Reported by the authors | Paper Table 1 | https://arxiv.org/html/2507.16248v3 | 20 Oct 2025 |
| 3 | Cashcat Pro | Not stated | 70.7 | Final Score (0-100) | all 70 questions | Reported by the authors | Paper Table 1 | https://arxiv.org/html/2507.16248v3 | 20 Oct 2025 |
| 4 | Minimax* | MiniMax | 67.7 | Final Score (0-100) | all 70 questions | Reported by the authors | Paper Table 1 | https://arxiv.org/html/2507.16248v3 | 20 Oct 2025 |
| 5 | Reportify | Not stated | 67.2 | Final Score (0-100) | all 70 questions | Reported by the authors | Paper Table 1 | https://arxiv.org/html/2507.16248v3 | 20 Oct 2025 |
| 6 | Perplexity | Perplexity | 67.0 | Final Score (0-100) | all 70 questions | Reported by the authors | Paper Table 1 | https://arxiv.org/html/2507.16248v3 | 20 Oct 2025 |
| 7 | Genspark* | Not stated | 64.3 | Final Score (0-100) | all 70 questions | Reported by the authors | Paper Table 1 | https://arxiv.org/html/2507.16248v3 | 20 Oct 2025 |
| 8 | AutoGLM | Zhipu AI | 60.0 | Final Score (0-100) | all 70 questions | Reported by the authors | Paper Table 1 | https://arxiv.org/html/2507.16248v3 | 20 Oct 2025 |

## Deep FinResearch Bench: overall factual claim rate % (higher is better, 4 models)

| Rank | Model | Maker | Score | Metric | Setting | Reported by | Source | URL | Date |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 1 | OpenAI | OpenAI | 86 | overall factual claim rate % | OpenAI DR (GPT-o3), Firm A-structured reports | Reported by the authors | Paper Table 7 | https://arxiv.org/html/2604.21006v1 | 22 Apr 2026 |
| 2 | Perplexity | Perplexity | 75.6 | overall factual claim rate % | Perplexity DR (Sonar Reasoning), Firm A-structured reports | Reported by the authors | Paper Table 7 | https://arxiv.org/html/2604.21006v1 | 22 Apr 2026 |
| 3 | Gemini | Google | 69.6 | overall factual claim rate % | Gemini DR (Gemini 2.5 Pro), Firm A-structured reports | Reported by the authors | Paper Table 7 | https://arxiv.org/html/2604.21006v1 | 22 Apr 2026 |
| 4 | Grok | xAI | 53.2 | overall factual claim rate % | Grok DR (Grok 4), Firm A-structured reports | Reported by the authors | Paper Table 7 | https://arxiv.org/html/2604.21006v1 | 22 Apr 2026 |


Source: https://benchmarks.finance/best/analyst-research/

Created and maintained by Moneda.