Understanding a difficult paper
Reasoning and math results can be supporting evidence, but do not measure citation accuracy or whether the model read the entire paper.
DECISION GUIDE
Choose an AI for research
Choose for the research steps you need: finding sources, reading documents, checking claims or synthesizing an answer.
Start with the job you need done
Synthesizing several documents
Check documented context limits and test retrieval across your own documents. A large context window is capacity, not proof of reliable recall.
Finding and verifying sources
Browsing and retrieval depend on the application and tools around the model. Open every important citation and check that it supports the associated claim.
Read the numbers without overreading them
Reasoning is supporting evidence
Strong reasoning results do not establish research quality. They can help identify candidates for an actual source-checking trial.
REVAL™ status
REVAL is our research evaluation framework. Only completed and reviewed runs receive a score; not tested does not mean poor performance.
Context and retrieval
Documents may fit in the advertised window yet still be retrieved incorrectly. Test your actual document length and application setup.
Inspect reasoning results and sources → · How scoring works → · Evidence gaps → · REVAL methodology and status →
Models with comparable reasoning evidence
These are starting points from our current comparison pool. Category scores are relative to covered tests; they are not a universal recommendation. Reasoning evidence is a proxy for one part of research, not a reviewed research evaluation.
Claude Sonnet 5.5
reasoning: 100/100 · Moderate confidence · 73% overall category coverage.
Read the practical guide → Follow updates →GPT-6 Astra
reasoning: 98/100 · Moderate confidence · 85% overall category coverage.
Read the practical guide → Follow updates →Claude Opus 5.5
reasoning: 97/100 · Moderate confidence · 85% overall category coverage.
Read the practical guide → Follow updates →DeepSeek V4 Pro
reasoning: 95/100 · Limited evidence · 73% overall category coverage.
Read the practical guide → Follow updates →Run a small, useful trial
- Use a question whose answer you can verify against primary sources.
- Check citations for existence, relevance and whether they support the claim.
- Separate model knowledge from information retrieved by external tools.
- Record unsupported claims and missed contrary evidence, not just fluent prose.
Keep the prompts, model version and settings fixed across candidates. Include failures in your notes. Choose using your task results alongside the published evidence.
Keep your decision current
Save your comparison and follow the shortlisted models. Return when independent results, prices or capabilities change; a new announcement alone does not establish better performance.
What changed for my models? ↗ This week’s verified changes →Explore another workflow: Choose an AI for coding →Choose an AI for automation →