When a developer scales a retrieval‑augmented pipeline, the price of the first million input tokens often decides the budget. OpenAI’s GPT‑5 nano charges $0.05 per 1 M input tokens and $0.40 per 1 M output tokens, with a cached‑input rate of $0.005. Anthropic’s Claude Haiku 4.5, by contrast, costs $1 per 1 M input tokens and $5 per 1 M output tokens, with a cached‑input discount of $0.10. Google’s Gemini Flash line sits in between, charging $0.30 per 1 M input tokens and $2.50 per 1 M output tokens, and a cached‑input rate of $0.03.
The table below captures the raw figures that drive these decisions:
| Vendor | Model | Input | Cached Input | Output |
|---|---|---|---|---|
| OpenAI | gpt‑5‑nano | $0.05 | $0.005 | $0.40 |
| Anthropic | claude‑haiku‑4.5 | $1 | $0.10 | $5 |
| gemini‑3.5‑flash‑lite | $0.30 | $0.03 | $2.50 |
These numbers reveal that GPT‑5 nano is the cheapest for both ingest and generation, making it the natural choice for high‑volume, low‑latency workloads where every token matters. Gemini Flash offers a middle ground: its input cost is six times higher than GPT‑5 nano but its output cost is only six times higher than GPT‑5 nano, which can be attractive when the model’s larger context window or different architecture justifies the extra spend. Claude Haiku 4.5, however, sits at the top of the price spectrum; its input and output rates are 20 × and 12.5 × higher than GPT‑5 nano, respectively, and even its cached‑input discount remains 20 × higher. For teams that can afford to pay for each token, Haiku 4.5 may provide a quality advantage over the other two, but the cost differential is substantial.
If your application is driven by volume, the most economical path is to stay within OpenAI’s nano tier or Google’s Flash line, both of which keep token costs well below $0.50 per million tokens for input and $3 per million tokens for output. If your use case demands higher reasoning depth and you are willing to pay a premium, Claude Haiku 4.5 offers a middle‑tier alternative that sits between the large Sonnet and Opus models and the ultra‑cheap mini models. In practice, a startup that needs to process millions of short queries will find GPT‑5 nano the best fit, while a research lab that values a slight edge in inference quality may justify the extra spend on Haiku 4.5. Google’s Gemini Flash remains a solid compromise for those who want a middle‑cost model with a different architecture.
Pricing verified on 2026-09-12.