Claude Sonnet 4.6 vs GPT-5.4 for Writing

Winner: Claude Sonnet 4.6. In our testing both models earn a 4/5 task score and tie at rank 6 of 52 for Writing, but Claude Sonnet 4.6 edges GPT-5.4 on creative idea generation (creative_problem_solving 5 vs 4), which matters most for blog posts, marketing campaigns, and concept work. GPT-5.4 wins where strict format and compression matter (structured_output 5 vs 4; constrained_rewriting 4 vs 3). Consider Sonnet 4.6 the better choice for idea-first content; choose GPT-5.4 when exact formatting, short ad copy, or schema output is primary.

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Claude Sonnet 4.6

Overall
4.67/5Strong

Benchmark Scores

Faithfulness
5/5
Long Context
5/5
Multilingual
5/5
Tool Calling
5/5
Classification
4/5
Agentic Planning
5/5
Structured Output
4/5
Safety Calibration
5/5
Strategic Analysis
5/5
Persona Consistency
5/5
Constrained Rewriting
3/5
Creative Problem Solving
5/5

External Benchmarks

SWE-bench Verified
75.2%
MATH Level 5
N/A
AIME 2025
85.8%

Pricing

Input

$3.00/MTok

Output

$15.00/MTok

Context Window1000K

modelpicker.net

openai

GPT-5.4

Overall
4.58/5Strong

Benchmark Scores

Faithfulness
5/5
Long Context
5/5
Multilingual
5/5
Tool Calling
4/5
Classification
3/5
Agentic Planning
5/5
Structured Output
5/5
Safety Calibration
5/5
Strategic Analysis
5/5
Persona Consistency
5/5
Constrained Rewriting
4/5
Creative Problem Solving
4/5

External Benchmarks

SWE-bench Verified
76.9%
MATH Level 5
N/A
AIME 2025
95.3%

Pricing

Input

$2.50/MTok

Output

$15.00/MTok

Context Window1050K

modelpicker.net

Task Analysis

What Writing demands: idea generation, voice/persona control, concise rewriting to length limits, adherence to formats (CMS blocks, JSON), long-context coherence for drafts and research, and faithfulness to source. On our Writing test suite (creative_problem_solving and constrained_rewriting), creative_problem_solving is the primary signal for ideation-heavy workflows; constrained_rewriting measures tight-length editing. In our testing: Claude Sonnet 4.6 scores 5 on creative_problem_solving and 3 on constrained_rewriting; GPT-5.4 scores 4 on creative_problem_solving and 4 on constrained_rewriting. Both models score 5 on long_context and faithfulness, and both maintain persona_consistency (5) and multilingual quality (5). Structured output favors GPT-5.4 (5 vs Sonnet’s 4), explaining why GPT-5.4 is superior for strict schema or CMS-ready content.

Practical Examples

Where Claude Sonnet 4.6 shines (use Sonnet when you need stronger ideation):

  • Multi-concept campaign kickoff: Sonnet 4.6 (creative_problem_solving 5) generates more non-obvious, feasible concepts and headline variants than GPT-5.4 (4).
  • Long-form thought leadership that needs creative hooks across sections: both models hold long-context (5), but Sonnet’s higher ideation score speeds concept iteration.
  • Multilingual marketing drafts: Sonnet 4.6 multilingual 5 matches GPT-5.4 (5) while offering stronger idea variety.

Where GPT-5.4 shines (use GPT-5.4 when format and tight constraints matter):

  • Short ad copy or SMS where exact character caps matter: GPT-5.4 constrained_rewriting 4 vs Sonnet’s 3 produces tighter, more reliable compressed rewrites.
  • CMS or API-driven content requiring JSON or schema compliance: GPT-5.4 structured_output 5 vs Sonnet 4 reduces post-processing.
  • Controlled template output (snippets, meta descriptions): GPT-5.4’s structured_output advantage yields fewer formatting fixes.

Cost/context notes: output cost per mTok is equal (15) for both; input cost per mTok is 3 for Claude Sonnet 4.6 vs 2.5 for GPT-5.4. Context windows are similar and large for both, supporting long drafts.

Bottom Line

For Writing, choose Claude Sonnet 4.6 if your priority is ideation, campaign concepts, headlines, and creative variety (creative_problem_solving 5 vs 4). Choose GPT-5.4 if you need strict format compliance, tight character-limited rewrites, or CMS-ready structured output (structured_output 5 and constrained_rewriting 4 vs Sonnet’s 4 and 3); note GPT-5.4 has a slightly lower input cost per mTok (2.5 vs 3).

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.

Frequently Asked Questions