Claude Haiku 4.5 vs Devstral 2 2512 for Long Context

Long Context (retrieval accuracy at 30K+ tokens) requires: 1) stable long-range attention and indexing so the model finds relevant passages across 30K+ tokens; 2) faithfulness to source material to avoid hallucinations when summarizing or extracting facts; 3) tool calling and agentic coordination when workflows must query search indexes, run retrieval, or call functions across chunks; 4) structured_output when you need strict JSON/schema outputs from long documents; and 5) usable context window length and practical output capacity (max_output_tokens). In our testing both Claude Haiku 4.5 and Devstral 2 2512 score 5/5 on long_context and are tied for 1st (tied with 36 others). To break the tie, we examine supporting proxy scores: Claude scores higher on tool_calling (5 vs 4), faithfulness (5 vs 4), and agentic_planning (5 vs 4), indicating stronger orchestration and source adherence across long inputs. Devstral scores higher on structured_output (5 vs 4) and has a larger documented context_window (262,144 vs 200,000) and lower input/output costs (0.4/2 vs 1/5 per mTok), which matter for schema extraction and cost-sensitive bulk processing. Max output: Claude lists max_output_tokens 64,000; Devstral’s max_output_tokens is not provided. Choose based on which supporting capability is most important for your long-context task.

1. Multi-document legal review with citation fidelity: Claude Haiku 4.5 is preferable — it scores faithfulness 5 vs Devstral’s 4 and tool_calling 5 vs 4, so in our testing Claude better preserves source facts and coordinates retrieval steps across 30K+ tokens. 2) Massive codebase extraction into strict JSON (API spec generation from 200K+ tokens): Devstral 2 2512 shines — structured_output 5 vs Claude’s 4, larger context_window (262,144 vs 200,000), and lower output cost (2 vs 5 per mTok) make it cheaper and more accurate for schema-constrained extracts. 3) Multimodal long reports with images embedded across a long file: Claude Haiku 4.5 supports text+image->text and scored 5 on long_context and multilingual, so it’s the clear pick when images must be ingested together with long text. 4) Cost-sensitive bulk retrieval pipelines that produce many short structured records: Devstral’s input/output cost per mTok (0.4/2) reduces billable spend compared to Claude (1/5), while preserving a 5/5 long_context score in our testing.

For Long Context, choose Claude Haiku 4.5 if you prioritize reliable function orchestration, stronger faithfulness, multimodal inputs, or long outputs (scores: tool_calling 5, faithfulness 5, max_output_tokens 64,000). Choose Devstral 2 2512 if you prioritize the largest raw context window (262,144), strict structured JSON/schema extraction (structured_output 5), or lower input/output cost per mTok (0.4/2 vs 1/5). Both scored 5/5 on Long Context in our tests and are tied for top rank; pick the one whose supporting strengths match your workflow.