Can you recommend a large context window API for research synthesis across dozens of long reports?

Yes — for synthesizing dozens of long reports, you’ll want an API with a very large context window, strong reasoning, and decent throughput/cost balance.

Best current options

1) Google Gemini 1.5 Pro / Flash

• Context: up to 1M tokens (availability can vary by tier/product)
• Good for: ingesting many long documents at once, cross-document synthesis, retrieval-like workflows without heavy chunking
• Pros: huge context, strong at long-document tasks, good multimodal/document support
• Cons: quality can vary by use case; you may still want structured prompting and doc segmentation for reliability

2) Anthropic Claude 3.5 Sonnet

• Context: typically 200K tokens
• Good for: high-quality synthesis, nuanced writing, summarization across many sources
• Pros: excellent writing and analytical synthesis, strong instruction-following
• Cons: smaller context than Gemini; for dozens of long reports you’ll often need preprocessing/chunking

3) OpenAI GPT-4.1 / GPT-4o-class large-context offerings

• Context: commonly up to 128K tokens depending on model/API
• Good for: strong general-purpose reasoning, tool use, structured extraction
• Pros: very solid synthesis and coding ecosystem
• Cons: context smaller than Gemini, so you’ll likely need a retrieval pipeline for lots of long reports

Practical recommendation

If your main requirement is “put as much as possible into context and synthesize across many long reports”, I’d start with:

• Gemini 1.5 Pro for the largest single-context ingestion
• Claude 3.5 Sonnet if you prioritize best-quality synthesis and can work within 200K tokens
• OpenAI if you want the strongest overall platform/tooling and are fine with retrieval + chunking

What I’d do for your use case

For dozens of long reports, even very large context windows can get unwieldy. A robust workflow is:

1. Chunk reports by section
2. Extract structured notes per chunk:
   • key claims
   • evidence
   • methods
   • limitations
   • relevant quotes
3. Build per-document summaries
4. Do a second-pass synthesis across summaries and targeted excerpts
5. Use retrieval (RAG) for follow-up questions and citation grounding

This usually beats dumping everything into one prompt, even with huge contexts.

Quick decision guide

• Need the largest single context window: Gemini 1.5 Pro
• Need the best synthesis quality for long-form analysis: Claude 3.5 Sonnet
• Need best ecosystem + solid reasoning: OpenAI GPT-4.1 / GPT-4o-class

If you want, I can also give you a ranked comparison table with:

• context window
• pricing rough order
• API maturity
• citation/grounding support
• best-fit research workflows