Claude Sonnet 4.6 vs GPT-5.4

Choose Claude Sonnet 4.6 if you want…

• ✓A comparable all-rounder — they trade blows on the headline metrics.

Choose GPT-5.4 if you want…

• ✓Higher intelligence (Artificial Analysis index 57)
• ✓Faster output (~163.4 tokens/sec)
• ✓Lower price ($5.63 / 1M blended)
• ✓Larger context window (1.1M)

Claude Sonnet 4.6

Sonnet 4.6 sits at the sweet spot where coding and agentic work get done without paying Opus prices. On SWE-bench Verified it scores 79.6% — within one point of Opus 4.6 (80.8%) — at roughly a third of the cost, which is why developers running automated pipelines tend to reach for it first. The self-correction training is the headline improvement: when a tool call fails, the model recognizes and recovers rather than cycling through the same error. Users also praise the 1M-token context window for swallowing entire codebases or large document sets in a single pass. The honest caveat is that this context window has edges — retrieval quality degrades on adversarial tests beyond about 700K tokens, so vector-based RAG is still the safer bet for critical long-context searches. Speed is also a known tension: at 44 tokens per second, it runs slower than the median for its tier, which can feel noticeable in real-time applications. Still, for teams that need high-quality code generation, browser automation, and multi-step agentic workflows without Opus-level spend, Sonnet 4.6 is the practical default.

GPT-5.4

GPT-5.4 was built for the actual work that happens inside offices — financial modeling, legal analysis, complex codebases, and multi-step document workflows — rather than for chasing narrow benchmarks. That strategic shift shows in the numbers: it matched or outperformed human professionals in 83% of head-to-head comparisons, and developers have called its coding output "flawless," with some declaring it the definitive choice for complex software engineering work. Native computer-use capabilities let it operate browsers and desktop apps directly, and it scored above the human baseline on UI interaction tasks. The 1.05 million token context window handles large codebases and lengthy legal documents in a single pass, though you need to configure it explicitly — the default is 272K. Where GPT-5.4 falls short is nuance: it tends to interpret requests too literally, missing the intent behind ambiguous prompts in ways that Claude handles more naturally. Writing personality is another common frustration, with verbose follow-up suggestions that can feel mechanical. For structured professional tasks where thoroughness and tool integration matter more than prose feel, it is the strongest model in the GPT-5 line prior to the release of GPT-5.5.