Research Assistant
Summarize papers and extract key findings
Ingest academic papers, industry reports, or technical documentation and extract hypotheses, methodologies, key findings, and limitations in a structured format — enabling researchers to process 10x more literature in the same time.
GPT-6.1 Sol
for 2M tokens a month · 80% input / 20% output
Why this model
For this workload — 2M tokens a month, 80% of them input — it comes to about $7.2 a month, the lowest of the models suggested here. Its 1M-token context window holds long inputs in a single call, which matters for this kind of work.
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Implementation tips
- 1
Use a standardized extraction schema for every paper: problem statement, methodology, key results (with numbers), limitations, and related work — this creates a machine-readable research database, not just readable summaries.
- 2
Chain extraction with a comparison step: after processing a batch of papers, ask the model to identify consensus findings, contradictions, and open questions across the set — this meta-synthesis is where the real research value lies.
- 3
Flag papers where the model's confidence in extraction is low (ambiguous methodology sections, preprints without clear results) and prioritize those for manual human review rather than trusting the extraction blindly.