Claude Haiku 4.5 vs DeepSeek V3.2 for Data Analysis
Tie. On our Data Analysis suite both Claude Haiku 4.5 and DeepSeek V3.2 score 4.333/5 (rank 11 of 52). Claude Haiku 4.5 is stronger at classification (4 vs 3) and tool calling (5 vs 3), which favors workflows that require function selection, API/SQL calls, or automated routing. DeepSeek V3.2 is stronger at structured output (5 vs 4), making it preferable for strict JSON/schema compliance and downstream ETL. Both tie on strategic analysis (5) and share top marks for long-context and faithfulness (5 each). Cost and modality differ sharply: Claude Haiku 4.5 costs 1 input / 5 output per mTok and supports text+image->text with a 200,000 token context; DeepSeek V3.2 costs 0.26 input / 0.38 output per mTok, is text->text with a 163,840 token context. Choose based on whether tool-calling/classification or schema compliance/cost is the priority.
anthropic
Claude Haiku 4.5
Benchmark Scores
External Benchmarks
Pricing
Input
$1.00/MTok
Output
$5.00/MTok
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deepseek
DeepSeek V3.2
Benchmark Scores
External Benchmarks
Pricing
Input
$0.260/MTok
Output
$0.380/MTok
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Task Analysis
What Data Analysis demands: precise strategic reasoning over numbers, reliable classification/routing, and strict structured outputs for downstream systems. Key capabilities: strategic_analysis to reason about tradeoffs and metrics, classification for labeling and routing, structured_output for JSON/schema compliance, tool_calling to execute queries or invoke analysis tools, long_context and faithfulness to handle large datasets without hallucination. On our tests (strategic_analysis, classification, structured_output) both models score the same overall (taskScore 4.333/5), but the component strengths differ: strategic_analysis is tied at 5 for both, classification favors Claude Haiku 4.5 (4 vs 3), and structured_output favors DeepSeek V3.2 (5 vs 4). Tool calling (5 vs 3) and modality/context specs (Haiku: text+image->text, 200k context, max output 64k; DeepSeek: text->text, 163,840 context) explain practical differences: Haiku better integrates with tool-driven analysis and multimodal inputs; DeepSeek enforces cleaner schema output at lower cost. Both models have top-tier faithfulness and long-context performance (5), so neither is likely to hallucinate on large inputs in our testing.
Practical Examples
Where Claude Haiku 4.5 shines (based on scores):
- Automated ETL that requires invoking SQL/analytics functions, calling plotting or API tools, and routing rows by category — tool_calling 5 vs 3 and classification 4 vs 3 make Haiku better at selecting and sequencing functions and labeling outputs.
- Multimodal data analysis that includes images (Haiku supports text+image->text) and very large synthesis tasks (200k context, 64k output) for long analytical reports. Where DeepSeek V3.2 shines (based on scores and cost):
- High-volume, schema-first reporting where strict JSON/CSV output is required downstream — structured_output 5 vs 4 yields tighter compliance and fewer format fixes.
- Budget-sensitive batch analysis: DeepSeek costs $0.26 input / $0.38 output per mTok versus Claude Haiku at $1 / $5 per mTok, so repeated schema generation or large-volume exports are far cheaper. Shared strengths and tie context: Both models score 5 on strategic_analysis and 5 on faithfulness and long_context in our tests, so for complex numerical reasoning over long datasets they perform equivalently; choose by integration needs (tooling vs schema/cost).
Bottom Line
For Data Analysis, choose Claude Haiku 4.5 if you need stronger classification and tool-calling (API/SQL/function orchestration) or multimodal inputs; choose DeepSeek V3.2 if you need stricter structured output compliance and much lower per-token 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.