GPT-5.4 vs Gemini 3: Benchmarks vs Context
This is a matchup between two different philosophies. OpenAI has focused on pushing benchmark scores and raw quality. Google has invested heavily in context window size and multimodal capabilities. Both approaches have merit, and the right choice depends entirely on what you’re building.
Raw Performance
GPT-5.4 leads on every benchmark we track. MMLU: 93.1 vs 92.8. HumanEval: 92.8 vs 89.5. GPQA: 76.2 vs 72.1. The MMLU gap is negligible, but GPT-5.4’s code generation advantage (3+ points on HumanEval) and reasoning edge (4+ points on GPQA) are meaningful if those capabilities matter for your use case.
For tasks like code review, complex analysis, and structured reasoning, GPT-5.4 has a measurable edge. For general-purpose text generation, the difference is harder to spot.
The Context Factor
Gemini 3 processes up to 2M tokens per request. GPT-5.4 handles 256K. That’s nearly an 8x difference, and it’s Gemini’s biggest selling point.
If you need to analyze an entire codebase, process a batch of legal contracts, or work with very long conversation histories, Gemini 3 can handle it in a single request where GPT-5.4 would require chunking and multiple calls. The convenience factor alone can justify choosing Gemini for document-heavy workflows.
Pricing
Gemini 3 is cheaper across the board: $7/$21 versus $10/$30 per million tokens. The savings are modest compared to budget models, but they add up at scale. For a workload processing 100M tokens per month, you’d save roughly $1,200/month on input and $900/month on output by choosing Gemini 3.
Output Capacity
Gemini 3 also doubles GPT-5.4 on max output: 65,536 tokens versus 32,000. This matters for generating long documents, detailed technical specs, or extended code files. If you regularly hit GPT-5.4’s output cap, Gemini 3 removes that constraint.
When to Pick Each
Choose GPT-5.4 when: You need the highest benchmark performance, especially for code generation and complex reasoning. Your inputs fit within 256K tokens, and you value quality over context length.
Choose Gemini 3 when: Your workflow involves long documents, you want lower costs at scale, or you need to generate very long outputs. Gemini 3’s combination of 2M context, 65K output, and competitive pricing makes it hard to beat for document-processing pipelines.