Claude Haiku 4.5 vs Devstral Small 1.1 for Research
Winner: Claude Haiku 4.5. In our testing for Research (deep analysis, literature review, synthesis) Haiku scores 5.0 vs Devstral Small 1.1's 3.33. Haiku outperforms on the three Research tests — strategic_analysis (5 vs 2), faithfulness (5 vs 4), and long_context (5 vs 4) — and ranks 1 of 52 for this task versus Devstral's rank 47. Those gaps indicate Haiku produces more reliable, context-aware research outputs. Devstral Small 1.1 is notably cheaper (input cost 0.1 vs 1; output cost 0.3 vs 5 per mTok) and remains useful for shorter, cost-sensitive workflows, but it loses on the core Research capabilities we measured.
anthropic
Claude Haiku 4.5
Benchmark Scores
External Benchmarks
Pricing
Input
$1.00/MTok
Output
$5.00/MTok
modelpicker.net
mistral
Devstral Small 1.1
Benchmark Scores
External Benchmarks
Pricing
Input
$0.100/MTok
Output
$0.300/MTok
modelpicker.net
Task Analysis
What Research demands: deep tradeoff reasoning, faithful use of sources, and accurate synthesis across very long contexts. The task tests are strategic_analysis, faithfulness, and long_context. With no external benchmark in the payload, we base the verdict on our internal task scores: Claude Haiku 4.5 scores 5 on all three Research test axes (strategic_analysis=5, faithfulness=5, long_context=5) while Devstral Small 1.1 scores (strategic_analysis=2, faithfulness=4, long_context=4). Supporting signals: Haiku also scores higher on tool_calling (5 vs 4), agentic_planning (5 vs 2) and persona_consistency (5 vs 2), which matter for running retrieval/agent pipelines, maintaining analytic voice, and decomposing research goals. Both models tie on structured_output (4), so neither has a systematic format advantage. Haiku’s context_window (200,000 tokens) and max_output_tokens (64,000) explicitly favor very long literature reviews and aggregated syntheses; Devstral’s context_window is 131,072 tokens with no max_output_tokens listed. Cost tradeoff: Haiku’s input/output costs (1 / 5 per mTok) are materially higher than Devstral’s (0.1 / 0.3), so budget and throughput matter when choosing.
Practical Examples
- Large-scale literature synthesis (200k+ tokens): Use Claude Haiku 4.5. In our tests Haiku’s long_context=5 vs Devstral=4 and context_window (200,000 vs 131,072) make Haiku better at retrieving and integrating long documents. 2) Methodological tradeoff write-ups and recommendations: Use Claude Haiku 4.5. Strategic_analysis is 5 for Haiku vs 2 for Devstral, so Haiku gives more nuanced numeric tradeoffs in our testing. 3) Citation-accurate summarization and source-faithful extraction: Use Claude Haiku 4.5. Faithfulness 5 vs 4 means Haiku better adheres to source material in our benchmarks. 4) High-volume, short research tasks (many short literature scans, classification, or JSON outputs): Use Devstral Small 1.1 to save cost. Devstral ties on structured_output (4) and matches classification (4), and its input/output cost (0.1 / 0.3 per mTok) is ~16.67x cheaper on output per-token than Haiku, making it the pragmatic choice for budget-limited, shorter-context pipelines. 5) Image-to-text research inputs: Use Claude Haiku 4.5 when you need image→text handling; Haiku’s modality is text+image->text while Devstral is text->text.
Bottom Line
For Research, choose Claude Haiku 4.5 if you need the strongest long-context synthesis, highest faithfulness, and advanced strategic analysis (scores 5 vs 3.33; task rank 1 of 52). Choose Devstral Small 1.1 if budget and throughput matter more than top-tier reasoning — it is far cheaper (input 0.1 vs 1; output 0.3 vs 5 per mTok), ties on structured output, and works well for shorter, repeatable research tasks.
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.