Gemini 3 Flash Preview vs Nano Banana Pro (Gemini 3 Pro Image Preview) Comparison: Benchmarks, Pricing & Speed (September 2026)

the live rate on each model's own page before you budget against it.

The context window caps how much source material — documents, code, prior conversation — you can send in a single request. A larger window lets you skip chunking and retrieval plumbing for long inputs, but you still pay input-token rates for everything you send, so a bigger window is a capability, not a discount. Match the window to the longest single request your workload realistically produces rather than the largest number on the page. Also keep in mind that quality can degrade toward the end of a very long context on any model, so a large window is best treated as headroom for occasional long inputs rather than a licence to stuff every request to the limit.

For interactive chat and agent loops, low p50 latency matters most because the user is waiting on the first token; for batch generation and long-form output, throughput dominates the wall-clock time because the answer is long. The 7-day trend charts above show whether each model's latency is stable or drifting, which a single headline number hides — a model with a great average but a noisy tail can still miss a strict p95 SLA. If your product has a latency budget, read both the median and the shape of the curve, and remember that end-to-end latency also includes your network hop and any retrieval or tool calls you make around the model.

The composite indices shown here aggregate multiple public evaluations, and the percentile marks where each model lands against every comparable model in the catalog — a useful shortlist signal, not a guarantee for your task. A model that leads on a general intelligence index can still trail on your domain (coding, extraction, multilingual, long-context reasoning), so use the benchmarks to narrow the field, then run both models on a representative slice of your traffic. Pay attention to the specific index that matches your use case rather than the top-line number: a coding-heavy product should weight the coding index, a research assistant the reasoning index. Benchmarks also age as models are updated, so treat them as a starting hypothesis you confirm with your own evaluation set.