Claude Haiku 4.5 vs Devstral Medium for Long Context
Winner: Claude Haiku 4.5. In our testing Claude Haiku 4.5 scores 5/5 on Long Context vs Devstral Medium's 4/5 (taskRank 1 of 52 vs 36 of 52). Haiku's 200,000-token context window, 64,000 max output token cap, and higher internal scores for tool_calling (5 vs 3) and faithfulness (5 vs 4) explain the margin. Devstral Medium is a solid, lower-cost alternative (input/output costs 0.4/2 vs Haiku's 1/5 per mTok) but trails on retrieval accuracy and tool-driven workflows in our benchmarks.
anthropic
Claude Haiku 4.5
Benchmark Scores
External Benchmarks
Pricing
Input
$1.00/MTok
Output
$5.00/MTok
modelpicker.net
mistral
Devstral Medium
Benchmark Scores
External Benchmarks
Pricing
Input
$0.400/MTok
Output
$2.00/MTok
modelpicker.net
Task Analysis
What Long Context demands: retrieval accuracy at 30K+ tokens, stable reference resolution across long documents, chunking and reassembly, faithful quoting, structured outputs for downstream parsing, and reliable tool selection when external retrieval or agents are used. Primary signal: our Long Context task score (taskScoreA = 5 for Claude Haiku 4.5; taskScoreB = 4 for Devstral Medium). Supporting signals from our 12-test suite: Claude Haiku 4.5 posts stronger tool_calling (5 vs 3), faithfulness (5 vs 4), agentic_planning (5 vs 4), and persona_consistency (5 vs 3) — all relevant when maintaining coherent state across long inputs. Context windows in the data matter directly: Claude Haiku 4.5 = 200,000 tokens and max_output_tokens = 64,000; Devstral Medium = 131,072 tokens and max_output_tokens = null. Structured_output is tied (4 vs 4), so both handle schemaed returns similarly in our tests. Because externalBenchmark is null, our internal Long Context score is the primary basis for the verdict.
Practical Examples
Where Claude Haiku 4.5 shines (based on score differences):
- Multi-year enterprise chat retrieval: Haiku's 200,000-token window and 5/5 long_context help retrieve precise snippets across very large archives where retrieval accuracy matters most. Tool_calling 5/5 supports orchestrated lookups and citations.
- Book-scale Q&A and guided summarization: Haiku's 64,000 max_output_tokens and faithfulness 5/5 reduce hallucinated summaries when synthesizing long source material.
- Agentic pipelines that combine retrieval + tools: Haiku's tool_calling 5 vs Devstral's 3 means fewer argument/selection errors when invoking external search or DB tools in our tests. Where Devstral Medium is the practical pick:
- Cost-sensitive batch processing: Devstral Medium has lower input/output costs (0.4/2 per mTok vs Haiku 1/5), so at scale you pay less while retaining a good long_context score (4/5).
- Reasonably long documents and codebases up to ~131k tokens: Devstral's 131,072 window and taskScore 4/5 make it suitable for many book-length or large-codebase tasks where absolute top retrieval fidelity is noncritical.
- Simpler structured extraction pipelines: both models score 4/5 on structured_output, so Devstral can be cost-effective for schemaed extraction from long text.
Bottom Line
For Long Context, choose Claude Haiku 4.5 if you need top retrieval accuracy at very large context sizes (200k tokens), stronger tool-calling, and higher faithfulness in our testing, and you can accept higher per-mTok costs. Choose Devstral Medium if you need a capable long-context AI at lower per-mTok cost (input/output 0.4/2) and your workloads fit within a 131k-token window where a 4/5 retrieval score is acceptable.
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.