Claude Haiku 4.5 vs Claude Opus 4.7 for Data Analysis
In our testing, Claude Haiku 4.5 is the better choice for Data Analysis. Haiku posts a task score of 4.3333 vs Claude Opus 4.7's 4.00 (a 0.33-point advantage) and ranks 11th of 53 for Data Analysis compared with Opus at 25th. The decisive factors are Haiku's stronger classification (4 vs 3) and equal performance on strategic analysis (5 vs 5) and structured output (4 vs 4). Opus has advantages in creative problem solving, constrained rewriting, safety calibration, and an enormous 1,000,000-token context window, but those do not overcome Haiku's higher task score and much lower per-token cost in our benchmarks.
anthropic
Claude Haiku 4.5
Benchmark Scores
External Benchmarks
Pricing
Input
$1.00/MTok
Output
$5.00/MTok
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anthropic
Claude Opus 4.7
Benchmark Scores
External Benchmarks
Pricing
Input
$5.00/MTok
Output
$25.00/MTok
modelpicker.net
Task Analysis
Data Analysis requires three core capabilities: strategic analysis (nuanced tradeoff reasoning with numbers), reliable classification (accurate categorization and routing), and structured output (JSON/schema compliance). In our testing the task is measured by those three tests. Claude Haiku 4.5 scores: strategic analysis 5, classification 4, structured output 4 → task score 4.3333. Claude Opus 4.7 scores: strategic analysis 5, classification 3, structured output 4 → task score 4.0. Both models tie at high marks for tool calling (5), faithfulness (5), agentic planning (5) and long-context retrieval (5), which supports analytic workflows and multi-step data processing. Haiku is described as Anthropic’s fastest and most efficient model and also has far lower token costs ($1 per million input / $5 per million output) compared with Opus ($5 per million input / $25 per million output). Opus’s 1,000,000-token window and higher scores on creative problem solving (5 vs 4) and constrained rewriting (4 vs 3) matter for ultra-long datasets and compressed executive deliverables, but for the core Data Analysis tests in our suite Haiku holds the edge.
Practical Examples
When Claude Haiku 4.5 shines (based on our scores):
- Batch labeling and routing: classification 4 vs Opus 3 — Haiku is better for automated dataset tagging, label cleaning, and routing records to downstream pipelines.
- Cost-sensitive analytics at scale: Haiku’s token pricing ($1 in / $5 out per million tokens) reduces operating cost for repeated analyses and large-volume report generation.
- Standard analytical workflows: identical strategic analysis (5) and structured output (4) mean Haiku reliably produces numerical tradeoff reasoning and JSON-compliant summaries for dashboards. When Claude Opus 4.7 shines (based on our scores and specs):
- Ultra-long-document analysis: Opus’s 1,000,000-token context window is advantageous for multi-month logs, entire data lakes, or giant transcripts even though long-context accuracy is tied at 5 for both models.
- Creative, compressed insights: Opus scores higher on creative problem solving (5 vs 4) and constrained rewriting (4 vs 3), so it’s stronger at generating novel hypotheses, compact executive summaries under strict character limits, and inventive feature-engineering suggestions.
- Higher safety sensitivity: Opus scores 3 vs Haiku’s 2 on safety calibration in our testing, useful when analyses touch regulated or sensitive topics.
Bottom Line
For Data Analysis, choose Claude Haiku 4.5 if you need the best overall task score (4.33 vs 4.00), stronger classification, and far lower per-token cost for production analytics and bulk labeling. Choose Claude Opus 4.7 if you must analyze extremely long contexts (1,000,000 tokens), need superior creative problem solving or constrained rewriting, or require slightly stronger safety calibration despite higher cost.
How We Test
We test every model against our 12-benchmark suite covering tool calling, agentic planning, creative problem solving, safety calibration, and more. Each test is scored 1–5 by an LLM judge. Read our full methodology.