AI for Pharma
241 models ranked for pharmaceutical and life sciences. Scored with bonuses for reasoning (research analysis), large context (papers/patents), JSON mode (structured data), web search (literature), and function calling.
Pharma AI - Ranked by Life Sciences Score
| # | Model | Score |
|---|---|---|
| 1 | Claude Fable 5Anthropic | 97 |
| 2 | Claude Fable 5 (batch)Anthropic | 97 |
| 3 | Claude Opus 5 (Fast)Anthropic | 95 |
| 4 | Claude Opus 5Anthropic | 95 |
| 5 | Claude Opus 4.8 (Fast)Anthropic | 95 |
| 6 | Claude Opus 4.8Anthropic | 95 |
| 7 | Claude Opus 4.7 (Fast)Anthropic | 95 |
| 8 | Claude Opus 4.7Anthropic | 95 |
| 9 | Claude Opus 4.7 (batch)Anthropic | 95 |
| 10 | Claude Opus 4.8 (batch)Anthropic | 95 |
| 11 | GPT-5.5 ProOpenAI | 93 |
| 12 | GPT-5.5 Pro (batch)OpenAI | 93 |
| 13 | GPT-5.5OpenAI | 93 |
| 14 | GPT-5.5 (batch)OpenAI | 93 |
| 15 | Gemini 3.1 Pro Preview Custom ToolsGoogle | 92 |
| 16 | Gemini 3.1 Pro PreviewGoogle | 92 |
| 17 | Gemini 3.1 Pro Preview (batch)Google | 92 |
| 18 | GPT-5.4 ProOpenAI | 92 |
| 19 | GPT-5.4 Pro (batch)OpenAI | 92 |
| 20 | GPT-5.4OpenAI | 92 |
| 21 | GPT-5.4 (batch)OpenAI | 92 |
| 22 | GPT-5.3-CodexOpenAI | 91 |
| 23 | GPT-5.2-CodexOpenAI | 91 |
| 24 | GPT-5.2 ProOpenAI | 91 |
| 25 | GPT-5.2 Pro (batch)OpenAI | 91 |
| 26 | GPT-5.2OpenAI | 91 |
| 27 | GPT-5.2 (batch)OpenAI | 91 |
| 28 | Claude Opus 4.6Anthropic | 90 |
| 29 | Claude Opus 4.6 (batch)Anthropic | 90 |
| 30 | GPT-5.6 Luna ProOpenAI | 89 |
AI for Pharma & Life Sciences
Drug Discovery
Analyze molecular structures, predict drug-target interactions, and generate candidate compounds. Reasoning models evaluate pharmacokinetic properties and safety profiles.
Clinical Trial Analysis
Process trial data, generate statistical summaries, and identify adverse events. Large context handles full study protocols and regulatory submissions.
Literature Review
Search and synthesize scientific publications, patents, and clinical guidelines. Web search models access the latest research for up-to-date evidence summaries.
Regulatory Submissions
Draft IND applications, NDA sections, and compliance documents. JSON mode produces structured data for eCTD submissions and FDA databases.
Reasoning models analyze molecular structures, predict drug-target interactions, and review clinical trial literature. They help with study design, statistical analysis plans, and regulatory submission drafts. Large context processes lengthy FDA guidance documents and ICH guidelines.
Models with web search access current FDA, EMA, and PMDA regulations. Reasoning handles complex compliance requirements across multiple jurisdictions. They draft CTD sections, assess regulatory risk, and prepare responses to agency questions. Always validate with regulatory affairs professionals.
Large output (16K+ tokens) for complete clinical study reports. Reasoning for accurate medical/scientific content. Large context for processing study data and source documents simultaneously. Web search for current medical literature and guidelines.
Models assist with ICSR processing, signal detection, and aggregate safety report drafting. Reasoning identifies potential safety signals from case narratives. JSON mode outputs structured MedDRA-coded data. Human pharmacovigilance professionals must review all safety assessments.