Claude Haiku 4.5 vs Codestral 2508 for Structured Output
Codestral 2508 is the winner for Structured Output. In our testing Codestral scores 5/5 on the Structured Output benchmark vs Claude Haiku 4.5's 4/5, and Codestral is ranked 1 of 52 for this task while Claude Haiku 4.5 ranks 26 of 52. That 1-point advantage reflects more reliable JSON schema compliance and format adherence in our suite. Codestral is also substantially cheaper per token (input/output costs of $0.30/$0.90 per mTok) and has a larger context window (256,000 tokens vs Claude Haiku 4.5's 200,000), making it better for high-volume, cost-sensitive structured-output pipelines. Claude Haiku 4.5 remains a strong alternative when multimodal (image→text) structured extraction or stronger strengths in strategic analysis, persona consistency, and agentic planning matter.
anthropic
Claude Haiku 4.5
Benchmark Scores
External Benchmarks
Pricing
Input
$1.00/MTok
Output
$5.00/MTok
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mistral
Codestral 2508
Benchmark Scores
External Benchmarks
Pricing
Input
$0.300/MTok
Output
$0.900/MTok
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Task Analysis
Structured Output requires strict JSON schema compliance, predictable formatting, and deterministic adherence to response_format/structured_outputs parameters. Key capabilities: strong adherence to schema (format correctness), fine-grained control over tokens and max_tokens, reliable response_format/structured_outputs support, tool selection when outputs feed downstream systems, and sufficient context window for large schemas or reference data. In our testing the primary signal is the Structured Output test itself (Codestral 2508: 5, Claude Haiku 4.5: 4). Supporting proxies: both models score 5 on tool_calling and 5 on long_context in our tests (ties), indicating both can sequence function calls and handle large schemas. Differences emerge in modality and cost: Claude Haiku 4.5 supports text+image→text (useful when extracting structured data from images) and includes parameters like include_reasoning, while Codestral 2508 is text→text and is cheaper per mTok. Use these internal scores and parameter support to explain why Codestral achieved the top Structured Output score in our suite.
Practical Examples
- API payload generation for billing systems: Codestral 2508 (5 vs 4) — higher schema adherence and rank 1/52 in our tests means fewer rejection loops and cheaper per-token runs (input $0.30 / output $0.90 per mTok). 2) Large schema with many embedded references (30K+ context): both models tie on long_context (5), but Codestral's larger window (256k) reduces context chopping risk. 3) Image→text forms (structured extraction from receipts/photos): choose Claude Haiku 4.5 — it supports text+image→text modality, and still scores 4 on Structured Output, so it’s preferable when the source is visual. 4) Developer workflows requiring response_format and structured_outputs parameters: both support structured_outputs and response_format; Codestral's superior structured_output score (5 vs 4) reduces post-processing. 5) Cost-sensitive batch processing (thousands of requests): Codestral is materially cheaper — priceRatio in our data favors Codestral (Claude Haiku 4.5 input/output $1/$5 per mTok vs Codestral $0.30/$0.90).
Bottom Line
For Structured Output, choose Codestral 2508 if you need the most reliable JSON/schema adherence, lowest per-token cost, and the top-ranked Structured Output model in our tests. Choose Claude Haiku 4.5 if your workflow requires multimodal (image→text) extraction or the model’s other strengths (strategic analysis, persona consistency, agentic planning) outweigh a small drop in schema adherence.
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.