GPT-5.4 vs Claude Opus 4.6: Which Flagship Wins?
These are the two models everyone’s comparing in 2026. OpenAI’s GPT-5.4 and Anthropic’s Claude Opus 4.6 represent the current ceiling of commercial AI capability, and picking between them isn’t straightforward.
Benchmark Performance
On paper, GPT-5.4 holds a narrow lead. It scores 93.1 on MMLU versus Claude’s 92.3, and 92.8 on HumanEval versus 91.5. The gap on GPQA is a bit wider: 76.2 for GPT-5.4 against 74.8 for Claude Opus. But at this level, benchmark differences of a point or two rarely translate into noticeable quality gaps in production.
Where things diverge is in how the models handle tasks. GPT-5.4 tends to be more concise and direct. Claude Opus 4.6 tends to be more thorough and careful about edge cases, especially in long-form output. Developers who’ve tested both extensively often report that Claude handles complex, multi-step instructions with fewer “drift” issues.
Pricing: The Real Difference
This is where the comparison gets interesting. GPT-5.4 charges $10/$30 per million tokens (input/output). Claude Opus 4.6 charges $15/$75. That means Claude’s output tokens cost 2.5x more than GPT-5.4’s.
For a chatbot generating thousands of long responses daily, this difference adds up fast. For an internal tool that processes documents and returns short analyses, the gap is smaller because you’re spending more on input tokens.
Context and Output Limits
GPT-5.4 gives you 256K tokens of context and 32K tokens of max output. Claude Opus 4.6 offers 200K context and the same 32K output. Both are generous by historical standards. The 56K token difference in context won’t matter for most applications, but if you’re right at the edge, GPT-5.4 has more room.
When to Pick Each
Choose GPT-5.4 when: Cost matters, you need slightly higher raw benchmark performance, or your workload is output-heavy and the 2.5x output price difference is a deal-breaker.
Choose Claude Opus 4.6 when: You need careful instruction-following, long-form writing quality, or you’ve found it performs better on your specific prompts. Many developers also prefer Claude’s more predictable behavior on safety-sensitive tasks.
The honest answer: Try both on your actual use case. The benchmark gap is small enough that prompt engineering and task fit matter more than raw scores.