Gemini 3.5 Flash vs GPT-5.4

Choose Gemini 3.5 Flash if you want…

• ✓Faster output (~280 tokens/sec)
• ✓Lower price ($3.38 / 1M blended)

Choose GPT-5.4 if you want…

• ✓Higher intelligence (Artificial Analysis index 57)
• ✓Larger context window (1.1M)

Gemini 3.5 Flash

The first Flash-tier model to outperform a Pro on coding and agentic benchmarks, Gemini 3.5 Flash rewrites expectations for what a speed-optimized model can do. At over 280 tokens per second — roughly 4x faster than comparable frontier models — it sustains the throughput that production agent loops demand, while benchmark results on Terminal-Bench 2.1 (76.2%) and MCP Atlas (83.6%) put it ahead of Gemini 3.1 Pro on the tasks developers actually care about. Early users call it "an insane value" for delivering near-frontier intelligence at roughly a third of Pro's cost. The 31-point drop in hallucination rate over its predecessor makes it meaningfully more reliable in practice. The honest caveat: time to first token sits around 19 seconds, which stings in latency-sensitive interactions, and aggressive rate limiting has frustrated users hitting it hard. Deep reasoning, hard analytical problems, and ultra-long context retrieval still favor the Pro. But for teams running iterative coding agents, structured data pipelines, or high-throughput chatbots where cost and speed are the binding constraints, Flash 3.5 is the practical choice.

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.