The headline price gap is stark: Google’s gemini‑3.5‑flash‑lite charges $0.3 per 1M input tokens and $2.5 per 1M output tokens, while OpenAI’s gpt‑5‑nano asks for $0.05 per 1M input tokens and $0.4 per 1M output tokens. For developers whose workloads are dominated by generation, the difference translates into a ten‑fold swing in cost per million output tokens.
### How the numbers break down
Both vendors offer cached‑input discounts, but the savings are modest compared with the raw input price gap. Google’s flash‑lite line starts at $0.1 per 1M input tokens with a $0.01 cached‑input discount, whereas OpenAI’s nano model starts at $0.05 per 1M input tokens and drops to $0.005 when the prompt is cached. On the output side, Google’s flash‑lite sits at $2.5 per 1M tokens, while OpenAI’s nano model stays at $0.4 per 1M tokens. The table below captures the exact rates for the two models under comparison.
| Vendor | Model | Input | Cached input | Output |
|---|---|---|---|---|
| gemini‑3.5‑flash‑lite | $0.3 per 1M tokens | $0.03 per 1M tokens | $2.5 per 1M tokens | |
| OpenAI | gpt‑5‑nano | $0.05 per 1M tokens | $0.005 per 1M tokens | $0.4 per 1M tokens |
### Which side of the fence fits your use case?
If your application pours large prompt batches into the model—think retrieval‑augmented generation, log summarization, or code linting—OpenAI’s lower input price can shave dollars off every million tokens before any output is even produced. The cached‑input discount further rewards prompt reuse, making the nano tier especially attractive for workloads that hit the same context repeatedly.
Conversely, if your workload is heavily output‑centric—such as creative text generation, long‑form summarization, or dialogue agents that emit many tokens per request—the output price becomes the decisive factor. Here, Google’s flash‑lite is a clear outlier; at $2.5 per 1M output tokens it costs more than six times what OpenAI’s nano model charges. Only a compelling non‑price advantage, such as a unique multimodal capability, could justify that premium.
For teams that need a middle ground, both vendors expose a broader family of flash‑lite and nano models at varying scales. Google’s gemini‑2.5‑flash‑lite offers $0.1 input and $0.4 output, which narrows the gap on output but still lags behind OpenAI’s $0.05/$0.4 combo. OpenAI also supplies a gpt‑oss‑20b tier with $0.03 input and $0.13 output, which is cheaper on output than Google’s flash‑lite but lacks the ultra‑low input price of the nano line.
In practice, most startups and SaaS teams that are cost‑sensitive will gravitate toward the model that minimizes the dominant token class in their workload. For prompt‑heavy pipelines, OpenAI’s nano wins on input economics; for generation‑heavy pipelines, it also wins on output economics. Google’s flash‑lite might only make sense if you need a specific feature set that only the Gemini family provides, and you are willing to absorb the higher per‑token bill.
Pick the model that aligns with where you spend most of your token budget. If you’re unsure, start with OpenAI’s gpt‑5‑nano—it gives you the lowest possible spend on both input and output, and you can always migrate to a higher‑performance tier later. Pricing verified on 2026-09-12.