Claude Haiku 4.5 vs Claude Opus 4.7 for Classification
Winner: Claude Haiku 4.5. In our testing Haiku scores 4/5 on Classification vs Opus 3/5 and ranks 1st vs Opus 31st out of 53 models. Haiku is described as Anthropic’s fastest, most efficient model and also costs much less ($1 per million input tokens, $5 per million output tokens versus Opus at $5 input / $25 output), making it the definitive choice for high-throughput, accurate categorization and routing. Opus 4.7 is stronger in constrained rewriting (4 vs 3) and safety calibration (3 vs 2), so it can be preferable when you must enforce tight output constraints or stricter refusal behavior, but it loses on raw classification accuracy and cost-efficiency in our benchmarks.
anthropic
Claude Haiku 4.5
Benchmark Scores
External Benchmarks
Pricing
Input
$1.00/MTok
Output
$5.00/MTok
modelpicker.net
anthropic
Claude Opus 4.7
Benchmark Scores
External Benchmarks
Pricing
Input
$5.00/MTok
Output
$25.00/MTok
modelpicker.net
Task Analysis
What Classification demands: precise label assignment, consistent schema-conformant outputs for downstream routing, multilingual parity for global data, and predictable refusal behavior when inputs are unsafe. Our classification benchmark measures accurate categorization and routing. In our testing Haiku scores 4/5 for classification while Opus scores 3/5; Haiku also ranks 1st for this task versus Opus at rank 31. Supporting signals: both models tie on tool calling (5/5) and structured output (4/5), so both can follow JSON schemas and call functions reliably. Haiku’s advantages for this task come from higher multilingual (5 vs 4) and parity in faithfulness and long-context handling (both 5/5), which reduce mislabeling across languages and long inputs. Opus’s strengths (constrained rewriting 4 vs 3, creative problem solving 5 vs 4, and safety calibration 3 vs 2) explain why Opus may better handle extremely tight output-length constraints or safety gating, but those do not overcome Haiku’s higher classification accuracy in our tests.
Practical Examples
When to pick Claude Haiku 4.5:
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High-volume email or support ticket triage across languages: Haiku scores 4 vs Opus 3 on classification and 5 vs 4 on multilingual, plus much lower cost ($1 input / $5 output), making it cheaper for large-scale routing.
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Long-document categorization (30K+ context): Haiku ties Opus on long-context (5/5) and faithfulness (5/5), so it delivers accurate labels for long inputs at lower latency and cost. When to pick Claude Opus 4.7:
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Safety-sensitive routing where stricter refusal rules matter: Opus scores 3 vs Haiku 2 on safety calibration, so it better distinguishes harmful vs legitimate requests in our tests.
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Very tight formatted outputs or aggressive compression into hard limits: Opus 4 vs Haiku 3 on constrained rewriting, so Opus is preferable when labels must be packed into strict character-limited fields. Numerical summary from our tests: Classification 4 (Haiku) vs 3 (Opus); structured output both 4; tool calling both 5; multilingual 5 vs 4; safety calibration 2 vs 3; cost Haiku $1/$5 vs Opus $5/$25 (input/output per million tokens).
Bottom Line
For Classification, choose Claude Haiku 4.5 if you need higher routing accuracy, multilingual parity, and much lower cost ($1 per million input tokens, $5 per million output tokens). Choose Claude Opus 4.7 if you prioritize stricter safety refusals or superior constrained-rewriting (tight-format outputs) despite higher cost ($5 input, $25 output) and a 1-point lower classification score in our tests.
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.