--- title: "GSM-IC vs ai-berkshire" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/google-research-datasets-gsm-ic-vs-xbtlin-ai-berkshire" tools: ["google-research-datasets-gsm-ic", "xbtlin-ai-berkshire"] --- # GSM-IC vs ai-berkshire *GraphCanon updated Sep 20, 2026* ## Verdict Pick GSM-IC if gSM-IC is a benchmark dataset that evaluates the distractibility of language models by introducing irrelevant context into grade-school math problems; pick ai-berkshire if ai-berkshire implements a unique approach to value investing research through AI agents powered by Claude Code/Codex, inspired by the methodologies of Warren Buffett and Charlie Munger amongst other investors. The tool's. [GSM-IC](https://github.com/google-research-datasets/GSM-IC) reports 67 GitHub stars, 2 forks, and 1 open issues, last pushed Feb 13, 2023. [ai-berkshire](https://github.com/xbtlin/ai-berkshire#readme) has 16k stars, 2.5k forks, and 39 open issues, last pushed Sep 19, 2026. Figures are from public GitHub metadata via [GSM-IC's repository](https://github.com/google-research-datasets/GSM-IC) and [ai-berkshire's repository](https://github.com/xbtlin/ai-berkshire). | | [GSM-IC](/tools/google-research-datasets-gsm-ic.md) | [ai-berkshire](/tools/xbtlin-ai-berkshire.md) | | --- | --- | --- | | Tagline | GSM-IC benchmark evaluates distractibility of language models with grade-school math problems and irrelevant context. | AI-era Berkshire: a value investing research framework utilizing Claude Code / Codex with methodologies from Warren Buffett, Charlie Munger among others and multi-Agent adversarial analysis. | | Stars | 67 | 16,451 | | Forks | 2 | 2,456 | | Open issues | 1 | 39 | | Language | - | HTML | | Adopt for | GSM-IC is a benchmark dataset that evaluates the distractibility of language models by introducing irrelevant context into grade-school math problems. | ai-berkshire implements a unique approach to value investing research through AI agents powered by Claude Code/Codex, inspired by the methodologies of Warren Buffett and Charlie Munger amongst other investors. The tool's | | Persona | - | - | | Runtime | - | - | | License | License information is not provided in the repository. | MIT | | Categories | Evaluation & Observability | AI Agents, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [GSM-IC](/tools/google-research-datasets-gsm-ic.md) | [ai-berkshire](/tools/xbtlin-ai-berkshire.md) | | --- | --- | --- | | Maintenance | Archived (8%) | Very active (96%) | | Days since push | 1312d | 0d | | Archived on GitHub | Yes | No | | Open issues (now) | 1 | 39 | | Stars delta | 0 (30d) | +2.3k (30d) | | Open issues delta | 0 (30d) | +10 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/google-research-datasets-gsm-ic/trust.md) | [trust report](/tools/xbtlin-ai-berkshire/trust.md) | ## Decision facts: GSM-IC - **Pricing:** unknown - Pricing details are not available. - **Requirements:** The dataset is derived from GSM8K and includes irrelevant context in problem descriptions to test the distractibility of language models. - **Adopt for:** GSM-IC is a benchmark dataset that evaluates the distractibility of language models by introducing irrelevant context into grade-school math problems. - **License detail:** License information is not provided in the repository. ## Decision facts: ai-berkshire - **Adopt for:** ai-berkshire implements a unique approach to value investing research through AI agents powered by Claude Code/Codex, inspired by the methodologies of Warren Buffett and Charlie Munger amongst other investors. The tool's ## Choose when ### Choose GSM-IC if… - Pricing: Pricing details are not available.. - Requirements: The dataset is derived from GSM8K and includes irrelevant context in problem descriptions to test the distractibility of language models.. - Tags unique to GSM-IC: arithmetic reasoning, benchmark, distractibility, grade-school math. - Use GSM-IC when you need to assess how well a language model can focus on relevant information in arithmetic reasoning tasks, especially when faced with distracting irrelevant context. ### Choose ai-berkshire if… - Tags unique to ai-berkshire: ai, financial-analysis, investment-research, portfolio-management. - Also covers AI Agents. - You need to leverage multi-Agent adversarial analysis for deep fundamental stock market assessment aligned with renowned investor philosophies. ## When NOT to use GSM-IC - Avoid GSM-IC if your evaluation focuses on the model's ability to solve math problems without any form of distraction or if the context is always assumed to be relevant. - Do not use GSM-IC if your primary interest is in evaluating the model's performance on pure arithmetic tasks without the added complexity of irrelevant context. ## When NOT to use ai-berkshire - If your investment research requires real-time trading data or dynamic algorithmic trading strategies which are not the tool's expertise. - When you prefer a more manual or traditional approach to value investing that does not integrate AI-driven adversarial agent methodologies. ## Common questions ### What is the difference between GSM-IC and ai-berkshire? GSM-IC: GSM-IC benchmark evaluates distractibility of language models with grade-school math problems and irrelevant context.. ai-berkshire: AI-era Berkshire: a value investing research framework utilizing Claude Code / Codex with methodologies from Warren Buffett, Charlie Munger among others and multi-Agent adversarial analysis.. See the comparison table for live GitHub stats and shared categories. ### When should I choose GSM-IC over ai-berkshire? Choose GSM-IC over ai-berkshire when Pricing: Pricing details are not available.; Requirements: The dataset is derived from GSM8K and includes irrelevant context in problem descriptions to test the distractibility of language models.; Tags unique to GSM-IC: arithmetic reasoning, benchmark, distractibility, grade-school math; Use GSM-IC when you need to assess how well a language model can focus on relevant information in arithmetic reasoning tasks, especially when faced with distracting irrelevant context. ### When should I choose ai-berkshire over GSM-IC? Choose ai-berkshire over GSM-IC when Tags unique to ai-berkshire: ai, financial-analysis, investment-research, portfolio-management; Also covers AI Agents; You need to leverage multi-Agent adversarial analysis for deep fundamental stock market assessment aligned with renowned investor philosophies. ### When should I avoid GSM-IC? Avoid GSM-IC if your evaluation focuses on the model's ability to solve math problems without any form of distraction or if the context is always assumed to be relevant. Do not use GSM-IC if your primary interest is in evaluating the model's performance on pure arithmetic tasks without the added complexity of irrelevant context. ### When should I avoid ai-berkshire? If your investment research requires real-time trading data or dynamic algorithmic trading strategies which are not the tool's expertise. When you prefer a more manual or traditional approach to value investing that does not integrate AI-driven adversarial agent methodologies. ### Is GSM-IC or ai-berkshire more popular on GitHub? ai-berkshire has more GitHub stars (16,451 vs 67). Stars measure visibility, not whether either tool fits your constraints. ### Are GSM-IC and ai-berkshire open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to GSM-IC or ai-berkshire? GraphCanon lists graph-backed alternatives at [GSM-IC alternatives](/tools/google-research-datasets-gsm-ic/alternatives) and [ai-berkshire alternatives](/tools/xbtlin-ai-berkshire/alternatives) ([GSM-IC markdown twin](/tools/google-research-datasets-gsm-ic/alternatives.md), [ai-berkshire markdown twin](/tools/xbtlin-ai-berkshire/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/google-research-datasets-gsm-ic-vs-xbtlin-ai-berkshire.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, GSM-IC or ai-berkshire? GSM-IC: Archived. ai-berkshire: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for GSM-IC and ai-berkshire? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [GSM-IC trust report](/tools/google-research-datasets-gsm-ic/trust); [ai-berkshire trust report](/tools/xbtlin-ai-berkshire/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=google-research-datasets-gsm-ic`](/api/graphcanon/graph?tool=google-research-datasets-gsm-ic) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_