Claude Haiku 4.5 vs Devstral 2 2512 for Coding
Winner: Claude Haiku 4.5. In our testing the two models split the Coding task's primary tests (tool_calling and structured_output), but Claude Haiku 4.5 holds a narrow overall edge across coding-relevant proxies: tool_calling 5 vs 4, faithfulness 5 vs 4, agentic_planning 5 vs 4 and safety_calibration 2 vs 1. Devstral 2 2512 wins structured_output 5 vs 4 and constrained_rewriting 5 vs 3 and is substantially cheaper (input/output costs 0.4/2 vs Haiku's 1/5 per mTok). With no SWE-bench Verified scores available for either model, our internal proxy scores determine the verdict and show Claude Haiku 4.5 is the better all-around coding assistant by a narrow margin (net +1 across seven coding-related proxies in our testing).
anthropic
Claude Haiku 4.5
Benchmark Scores
External Benchmarks
Pricing
Input
$1.00/MTok
Output
$5.00/MTok
modelpicker.net
mistral
Devstral 2 2512
Benchmark Scores
External Benchmarks
Pricing
Input
$0.400/MTok
Output
$2.00/MTok
modelpicker.net
Task Analysis
What Coding demands: code generation, debugging, and code review require (1) correct function selection and argument sequencing (tool_calling), (2) strict schema and format compliance (structured_output), (3) faithfulness to the codebase and minimal hallucination, (4) ability to plan multi-step fixes and recover from failures (agentic_planning), and (5) long-context retrieval for large codebases. Our Coding task uses two primary tests: structured_output (JSON/schema compliance) and tool_calling (function selection & sequencing). On those tests the models split results: Claude Haiku 4.5 leads tool_calling (5 vs 4) while Devstral 2 2512 leads structured_output (5 vs 4). Because there are no external SWE-bench Verified scores for either model in the payload, we rely on these internal proxies and related benchmarks (faithfulness, agentic_planning, long_context, constrained_rewriting, safety_calibration) to explain strengths and weaknesses rather than external validation.
Practical Examples
Where Claude Haiku 4.5 shines (based on score differences in our testing):
- CI/CD automation and tool orchestration: tool_calling 5 vs 4 — better at choosing functions, ordering calls, and composing arguments for multi-step automation scripts.
- Code review and refactoring that must stay faithful to an existing codebase: faithfulness 5 vs 4 and agentic_planning 5 vs 4 — fewer hallucinations and stronger stepwise plans for complex fixes.
- Large-repo debugging where safety matters: safety_calibration 2 vs 1 and long_context tied 5 — more conservative refusals on harmful prompts while handling long contexts. Where Devstral 2 2512 shines:
- Strict, schema-bound outputs and API stubs: structured_output 5 vs 4 — better at producing JSON/contract-compliant responses, useful for code generators that must match exact schemas.
- Tight character-limited transformations and compact rewrites: constrained_rewriting 5 vs 3 — superior when outputs must fit strict size limits (e.g., code golf, embedded devices).
- Cost-sensitive bulk generation: input/output costs are 0.4/2 per mTok for Devstral vs 1/5 per mTok for Haiku — Devstral is materially cheaper for high-volume code generation tasks.
Bottom Line
For Coding, choose Claude Haiku 4.5 if you prioritize reliable tool calling, faithfulness to the codebase, multi-step debugging and safer refusals (tool_calling 5 vs 4; faithfulness 5 vs 4; agentic_planning 5 vs 4). Choose Devstral 2 2512 if you need perfect schema/JSON output and constrained rewrites (structured_output 5 vs 4; constrained_rewriting 5 vs 3) or you must minimize token cost (Devstral input/output 0.4/2 per mTok vs Haiku 1/5 per mTok).
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.
For coding tasks, we supplement our benchmark suite with SWE-bench scores from Epoch AI, an independent research organization.