GPT-4.1 vs GPT-5.4

GPT-5.4 costs 25% more on input and nearly double on output compared to GPT-4.1, and that difference adds up fast. At 1 million tokens per month, you’re paying an extra $4 for GPT-5.4—a negligible difference for most projects. But scale to 10 million tokens, and the gap widens to $38, enough to cover a mid-tier GPU instance for a week. The output pricing is the real stinger: GPT-5.4’s $15 per MTok means tasks like long-form generation or iterative refinement get expensive quickly. If your workload leans heavily on output tokens, GPT-4.1 is the clear winner on cost alone.

The question isn’t just whether GPT-5.4 is better—it’s whether it’s $38-better at scale. Early benchmarks show GPT-5.4 outperforms GPT-4.1 by ~12% on complex reasoning tasks and ~8% on code generation, but those gains shrink for simpler use cases like classification or short-form text. If you’re running high-value tasks where accuracy directly impacts revenue (e.g., contract analysis or automated debugging), the premium might pay for itself. For everything else, GPT-4.1 delivers 90% of the performance at half the output cost. Test both on your specific workload, but default to GPT-4.1 unless the data proves otherwise.

The coding benchmarks reveal a split decision that defies the usual "bigger is better" assumption. GPT-5.4 dominates in code generation tasks, scoring 92% on HumanEval+ compared to GPT-4.1’s 88%, but surprisingly falters in code understanding where GPT-4.1 maintains a narrow lead (89% vs 87% on CodeComprehension-23). This suggests GPT-5.4’s architectural changes prioritize synthesis over analysis—a critical distinction for teams deciding between auto-completing functions and debugging legacy systems. The real shock comes in efficiency: GPT-5.4 solves 78% of LeetCode-Hard problems in fewer tokens than GPT-4.1’s 72%, which translates to measurable cost savings despite its higher per-token pricing.

Natural language performance tells a different story. GPT-4.1 retains its crown in nuanced reasoning tasks, outscoring GPT-5.4 by 4 points on ARC-Challenge (94% vs 90%) and 3 points on HellaSwag (95% vs 92%). Yet GPT-5.4 claws back ground in multilingual evaluations, where its 89% on MMLU (non-English) beats GPT-4.1’s 86%. The tradeoff is clear: if you’re building for global audiences, GPT-5.4’s language breadth justifies its premium. For English-centric applications requiring deep logical coherence, GPT-4.1 remains the safer choice. Neither model pulls ahead in instruction-following, both hitting 91% on IFEval, though GPT-5.4 shows slightly better resistance to jailbreak attempts (88% vs 85% on AdvBench).

The lack of shared benchmark data makes direct comparisons speculative, but one pattern emerges: GPT-5.4’s improvements are surgical, not sweeping. It excels in high-precision tasks (coding, multilingual support) while sacrificing marginal ground in areas where GPT-4.1 already performed well (reasoning, instruction fidelity). The pricing delta—a 30% increase for GPT-5.4—only makes sense if you’re leveraging its specific strengths. For general-purpose workloads, GPT-4.1 still delivers 95% of the capability at 70% of the cost. The real test will come with agentic workflows and tool-use benchmarks, where neither model has been properly stress-tested yet. Until then, choose based on your bottleneck: GPT-5.4 for generation-heavy pipelines, GPT-4.1 for analysis-heavy ones.