Claude Sonnet 4.6 vs GPT-5.4 for Research
Winner: GPT-5.4 (narrow). Both models score 5/5 on our Research task (strategic_analysis, faithfulness, long_context), but GPT-5.4 pulls ahead on measurable precision and tooling for research workflows. In our testing GPT-5.4 has a higher SWE-bench Verified score (76.9 vs 75.2) and a substantially higher AIME 2025 score (95.3 vs 85.8) — supplementary external signals (Epoch AI) that indicate stronger exact reasoning and problem-solving on those third-party tasks. Internally, GPT-5.4 also scores 5/5 for structured_output (vs 4/5 for Claude Sonnet 4.6) and supports file input modality, which matters for ingesting papers and datasets. Claude Sonnet 4.6 is still excellent for Research (ties on core Research dimensions, and it wins tool_calling 5 vs 4 and creative_problem_solving 5 vs 4), but overall GPT-5.4 offers a small edge for rigorous, reproducible research workflows.
anthropic
Claude Sonnet 4.6
Benchmark Scores
External Benchmarks
Pricing
Input
$3.00/MTok
Output
$15.00/MTok
modelpicker.net
openai
GPT-5.4
Benchmark Scores
External Benchmarks
Pricing
Input
$2.50/MTok
Output
$15.00/MTok
modelpicker.net
Task Analysis
What Research demands: deep long-context handling (30K+ token retrieval), strict faithfulness to sources, nuanced strategic analysis, reproducible structured outputs (JSON/tables), reliable tool orchestration, and the ability to ingest varied inputs (documents, datasets). In our Research test suite (strategic_analysis, faithfulness, long_context) both Claude Sonnet 4.6 and GPT-5.4 score 5/5, tying at the task level. Supplementary external benchmarks show differences: on SWE-bench Verified (Epoch AI) GPT-5.4 scores 76.9 vs Sonnet 75.2, and on AIME 2025 (Epoch AI) GPT-5.4 scores 95.3 vs Sonnet 85.8 — these external results point to stronger performance on precise code/issue resolution and competition-level math tasks, which correlate with rigorous quantitative reasoning in research. Internally, GPT-5.4's 5/5 structured_output (vs 4/5) and support for file input help produce machine-readable deliverables and ingest PDFs/datasets; Claude Sonnet 4.6's strengths (tool_calling 5/5, creative_problem_solving 5/5, classification 4/5) support exploratory workflows, rapid iteration, and literature triage. Cost and IO: GPT-5.4 has slightly cheaper input cost per mTok (2.5 vs 3) and supports text+image+file->text; Claude Sonnet 4.6 supports text+image->text and matches output cost per mTok.
Practical Examples
Where GPT-5.4 shines (based on scores):
- Producing reproducible deliverables: GPT-5.4 scores 5/5 on structured_output vs Sonnet's 4/5 — use GPT-5.4 when you need strict JSON schemas or tables for downstream analysis.
- Math-heavy or exact-reasoning research: GPT-5.4's higher AIME 2025 (95.3 vs 85.8, Epoch AI) indicates stronger performance on competition-level quantitative problems useful for formal proofs, algorithm validation, or statistical derivations.
- Ingesting paper archives and datasets: GPT-5.4 supports file input (text+image+file->text), making batch literature ingestion easier. Where Claude Sonnet 4.6 shines (based on scores):
- Iterative tool-driven workflows: Sonnet 4.6 scores 5/5 on tool_calling vs GPT-5.4's 4/5 — better for multi-step data collection, API orchestration, or agentic experimentation during a literature review.
- Ideation and hypothesis generation: Sonnet's creative_problem_solving 5/5 vs GPT-5.4's 4/5 favors brainstorming novel experimental directions and non-obvious insights.
- Fast triage and classification: Sonnet's classification 4/5 vs GPT-5.4's 3/5 helps when routing papers into categories or labeling large corpora. Shared strengths: both score 5/5 on strategic_analysis, faithfulness, long_context and tie on persona_consistency, safety_calibration, and multilingual — both are solid for deep synthesis and long-form literature reviews.
Bottom Line
For Research, choose Claude Sonnet 4.6 if you prioritize iterative, tool-driven literature workflows, exploratory hypothesis generation, or better built-in tool calling (tool_calling 5/5) during active research. Choose GPT-5.4 if you need stricter machine-readable outputs (structured_output 5/5), file ingestion for large corpora, and stronger third-party signals on exact reasoning (SWE-bench Verified 76.9 vs 75.2; AIME 95.3 vs 85.8, Epoch AI) for quantitative or reproducible research.
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.