Claude Haiku 4.5
Editor's pick: Cheap + structured output + vision for scanned docs
GPT-5.4 Mini is the best LLM for data extraction / classification in April 2026, followed by Claude Haiku 4.5 and Gemini 3.1 Flash-Lite. Rankings reflect real benchmarks, pricing, and compliance for a typical data extraction / classification workload; see the breakdown below or take the quiz for a pick tailored to your volume and constraints. Last verified 2026-04-19.
Editor's pick: Cheap + structured output + vision for scanned docs
Editor's pick: Cheapest option when accuracy floor is modest
Strong quality profile (87/100)
Top-tier benchmarks for this use case (93/100)
Expand any question for the full answer. Last reviewed 2026-04-19.
GPT-5.4 Mini is the best LLM for data extraction / classification in April 2026, followed by Claude Haiku 4.5 and Gemini 3.1 Flash-Lite. The ranking is based on benchmarks relevant to data extraction / classification — instruction following, reasoning, tool use where applicable — combined with cost at a typical production volume and caching behavior. All picks are verified against arena.ai/leaderboard and the provider's published pricing as of 2026-04-19.
Gemini 3.1 Flash-Lite is the cheapest credible option for data extraction / classification at $0.25 / $1.5 per 1M, coming in at roughly $975.00/month at typical volume. This model does not support prompt caching, so list price is the full cost.
Yes — Gemini 3.1 Flash-Lite, Gemini 3 Flash all offer a free tier usable for prototyping data extraction / classification workloads. Free tiers have rate limits and daily quotas, so they're fine for validation but not production. See the model pages for exact quotas.
Claude Haiku 4.5 is the top Anthropic pick, GPT-5.4 Mini is the top OpenAI pick, Gemini 3.1 Flash-Lite is the top Google pick. For data extraction / classification workloads in April 2026, GPT-5.4 Mini ranks first overall in our picker. The gap between top picks is small — you should pick primarily on API ergonomics, deployment region, and caching behavior rather than raw benchmark score.
Rankings combine (1) benchmark scores weighted by what matters for data extraction / classification — for example coding benchmarks dominate for coding, long-context retrieval dominates for RAG and long documents, (2) cost at a typical production volume, (3) speed and latency tier, (4) ergonomics like prompt caching and structured output, (5) recency of release, and (6) a curated editorial boost for provider-specific strengths that generic benchmarks miss (e.g. Gemini's advantage on maps and geospatial tasks). Every rank shows its exact score breakdown on the quiz result page.