Claude Haiku 4.5 vs Codestral 2508 for Long Context

Long Context is defined in our benchmarks as retrieval accuracy at 30K+ tokens. Key capabilities that matter: (1) a large context window to hold source documents, (2) faithful retrieval and low hallucination (faithfulness), (3) stable tool calling and indexing support for multi-step retrieval (tool_calling), (4) structured-output fidelity when you need machine-readable extracts, (5) throughput and cost per token for practical scaling, and (6) modality support if documents include images. In our testing both models score 5/5 on the long_context test and tie for 1st out of 52. Supporting signals: both have faithfulness 5 and tool_calling 5, indicating reliable retrieval and function selection in our suite. Differences that break the tie in practice are hardware/policy-visible: Codestral 2508 has a larger context_window (256000 vs Claude Haiku 4.5's 200000) and lower input/output cost numbers (0.3/0.9 vs 1/5 per mTok). Claude Haiku 4.5 adds multimodal input (text+image->text) and an explicit max_output_tokens of 64000, plus higher scores on strategic_analysis, agentic_planning, persona_consistency and multilingual in our tests — useful when long-context retrieval is one part of a broader reasoning or multimodal pipeline. Use the equal 5/5 task scores as the primary evidence that both models handle 30K+ retrieval well, then choose based on window, cost, and modality tradeoffs.

Large legal archive search: Both score 5/5 for retrieval accuracy. Prefer Codestral 2508 for cost and capacity — context_window 256000 vs Haiku 4.5's 200000, and much lower token costs (input 0.3 / output 0.9 vs 1 / 5). Multimodal compliance review: Prefer Claude Haiku 4.5 (modality text+image->text, max_output_tokens 64000) when you must extract references from scanned diagrams or screenshots inside long documents. Haiku also scored higher on strategic_analysis and agentic_planning in our testing, aiding complex reasoning over the long context. Structured-data extraction at scale: Codestral 2508 scored structured_output 5 versus Haiku 4 in our tests, so for high-fidelity JSON schema extraction from long transcripts Codestral reduces post-processing work. Cost-sensitive bulk ingestion: Choose Codestral 2508 — same 5/5 long_context score but far lower input/output costs in our data (0.3 / 0.9 vs 1 / 5 per mTok), which matters when processing many long records. Single very-long generated reports: If your workflow demands generating very large outputs in one shot, Claude Haiku 4.5 shows an explicit max_output_tokens of 64000 in our data (Codestral has no max_output_tokens listed), which can be an advantage for huge single-response generation.

For Long Context, choose Claude Haiku 4.5 if you need multimodal ingestion (text+image->text), large single-response generation with an explicit max_output_tokens (64,000), or stronger strategic/agentic signals in downstream tasks. Choose Codestral 2508 if you want the same top long-context retrieval accuracy (both score 5/5 in our tests) but need a larger raw window (256,000 vs 200,000) and much lower token costs (input_cost_per_mtok 0.3 / output_cost_per_mtok 0.9 vs 1 / 5), or if you prioritize structured-output fidelity (Codestral structured_output 5 vs Haiku 4).