Claude Haiku 4.5 vs R1 0528 for Research
Winner: Claude Haiku 4.5. In our testing Claude Haiku 4.5 scores 5.00 on Research vs R1 0528’s 4.67 (a 0.33 gap) and ranks 1 of 52 for this task. Haiku 4.5 posts top marks on the Research subtests we use (strategic_analysis, faithfulness, long_context) and matches or leads R1 on tool calling and agentic planning. R1 0528 is competitive on faithfulness and long-context but scores lower on strategic analysis (4 vs 5) and ranks 20 of 52, though it is materially cheaper (see costs) and shows superior safety_calibration in our scores.
anthropic
Claude Haiku 4.5
Benchmark Scores
External Benchmarks
Pricing
Input
$1.00/MTok
Output
$5.00/MTok
modelpicker.net
deepseek
R1 0528
Benchmark Scores
External Benchmarks
Pricing
Input
$0.500/MTok
Output
$2.15/MTok
modelpicker.net
Task Analysis
What Research demands: deep literature synthesis requires nuanced strategic analysis, strict faithfulness to sources, and robust long-context handling for multi-document inputs; tool calling and well-formed structured output matter for citation extraction, data tables, and reproducible notes. In our testing (12-test suite focused on strategic_analysis, faithfulness, long_context) Claude Haiku 4.5 scores 5.00 and R1 0528 scores 4.6667. Claude Haiku 4.5 scored 5 on strategic_analysis, faithfulness, and long_context in our tests, supporting high-quality tradeoff reasoning, source-faithful summaries, and reliable retrieval across 30K+ tokens. R1 0528 scored 4 on strategic_analysis but 5 on faithfulness and long_context, and also posts a stronger safety_calibration (4 vs Haiku’s 2). Note: R1 0528 includes external math benchmarks in the payload (MATH Level 5 96.6% and AIME 2025 66.4% on MATH benchmarks, Epoch AI), while Claude Haiku 4.5 has no external math scores in the provided data. Use our internal scores as the primary signal here because no single external benchmark is flagged as primary for this task.
Practical Examples
Where Claude Haiku 4.5 shines (based on score deltas):
- Multi-paper literature review: Haiku’s 5/5 long_context and 5/5 faithfulness help synthesize and cite findings across >30K tokens of source material with fewer omissions or hallucinations.
- Policy tradeoff memos and methods comparison: Haiku’s 5/5 strategic_analysis produces clearer, numbered tradeoffs and numeric reasoning for experimental design.
- Tool-driven extraction pipelines: Haiku’s 5/5 tool_calling supports more reliable function selection and sequencing for citation retrieval and structured export.
Where R1 0528 shines (based on score deltas and quirks):
- Cost-sensitive, safe research assistants: R1’s safety_calibration is 4 vs Haiku’s 2, making it better at refusing harmful queries or conservatively flagging sensitive content in our tests.
- Math-heavy research subproblems: R1 posts 96.6% on MATH Level 5 and 66.4% on AIME 2025 (Epoch AI), which is relevant if your research involves contest-level math or formal reasoning (Epoch AI). Caveats for R1: payload notes R1 may return empty responses for structured_output, constrained_rewriting, and short agentic_planning runs unless given a high max_completion_tokens budget — plan for longer completions or post-processing.
Bottom Line
For Research, choose Claude Haiku 4.5 if you need the strongest end-to-end synthesis: it scores 5.00 on our Research tests, leads on strategic analysis, long-context, faithfulness, and tool calling. Choose R1 0528 if you need a lower-cost option with better safety calibration (4 vs 2 in our tests), strong long-context faithfulness, or superior contest-math results (MATH Level 5 96.6% and AIME 2025 66.4% on Epoch AI), and you can accommodate its structured_output quirks.
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.