Claude Haiku 4.5 vs Devstral 2 2512 for Classification

What Classification demands: precise mapping of inputs to labels or routes, consistent adherence to output schemas, reliable tool selection for downstream routing, and resistance to hallucination when labels depend on source content. In our testing on the Classification task, Claude Haiku 4.5 scores 4/5 and ranks 1 of 52 for classification; Devstral 2 2512 scores 3/5 and ranks 31 of 52. Key capability differences that explain the gap: Haiku’s tool_calling is 5 vs Devstral’s 4 (helps in multi-step routing and accurate function argument selection), faithfulness is 5 vs 4 (Haiku sticks closer to source material), and safety_calibration is 2 vs 1 (Haiku is likelier to refuse or correctly handle risky classification prompts). Devstral 2 2512 scores higher on structured_output (5 vs Haiku’s 4), so it’s stronger when strict JSON/CSV format compliance is the primary constraint. Context windows: Haiku supports 200,000 tokens vs Devstral’s 262,144 tokens — Devstral offers a larger window if you need more context for classification. Cost matters for production: Haiku charges $1.00 input / $5.00 output per mTok; Devstral charges $0.40 input / $2.00 output per mTok, so Devstral is more cost-efficient at scale.

Where Claude Haiku 4.5 shines (based on score differences):

• Multi-step routing: High tool_calling (5 vs 4) makes Haiku better at choosing which downstream service to call and populating arguments for automated ticket routing or triage flows.
• Faithful label assignment: Faithfulness 5 vs 4 reduces label-hallucination in content-moderation or legal-classification pipelines where sticking to source text is critical.
• Safer refusals: Better safety_calibration (2 vs 1) helps avoid unsafe categorization decisions on edge-case inputs. Where Devstral 2 2512 shines (based on score differences and costs):
• Strict schema exports: Structured_output 5 vs 4 makes Devstral preferable when every classification must be emitted in an exact JSON schema for downstream parsers.
• Large-context classification: With a 262,144-token window vs Haiku’s 200,000, Devstral can inspect more context when labels depend on long transcripts or documents.
• Cost-sensitive high-volume inference: At $0.40 input / $2.00 output per mTok vs Haiku’s $1.00 / $5.00, Devstral reduces operational costs for massive batch classification even though its accuracy is lower in our tests.

For Classification, choose Claude Haiku 4.5 if accurate routing, higher faithfulness, and stronger tool-calling matter (it scores 4/5 vs Devstral’s 3/5 and ranks 1 of 52). Choose Devstral 2 2512 if strict schema compliance, a larger context window (262,144 tokens), or lower inference cost ($0.40 input / $2.00 output per mTok) are your top priorities.