How Llama API pricing actually works
Llama is different from every other model family on this site in one important way: Meta doesn't sell a single, first-party, metered API with one published per-token rate. Llama's weights are open, and a number of independent companies (Groq, Together AI, Fireworks, and others) host and serve the models, each setting its own pricing. That means "the price of the Llama API" isn't a single number; it's a range that depends entirely on which host you choose. This calculator uses representative rates for Llama 4 Maverick and Llama 4 Scout to give you a starting estimate, but you should verify the actual rate on your chosen host before finalizing a budget. The spread between hosts can be large.
The Llama 4 model lineup
Llama 4 Maverick is the larger, more capable model in the current lineup, priced above Llama 4 Scout on both input and output tokens in typical hosted pricing. Llama 4 Scout is the lighter option, priced lower and suited to workloads that don't need Maverick's full capability. Both are explicitly host-dependent: the numbers shown here are representative rather than fixed, and real-world pricing across hosts for Maverick specifically has been observed spanning roughly $0.10 to $3 per million tokens, a genuinely wide range compared with the single fixed rate you'd get from a closed-model provider.
Why the host matters more than the model here
Because multiple hosts compete to serve the same open-weight model, your actual cost is shaped as much by host choice as by model choice. A host optimizing for raw throughput on cheap hardware can undercut a host bundling in extra reliability, tooling, or support; neither is wrong, but they land at very different price points for functionally the same underlying model. If cost is your primary driver, it's worth benchmarking two or three hosts directly with your real traffic pattern rather than picking one and assuming the price is representative of "Llama" as a whole.
Self-hosting as an alternative to per-token pricing
Because Llama's weights are open, self-hosting is a real option that doesn't exist for closed models like GPT or Claude. At sufficient volume, running your own inference infrastructure can be cheaper than paying any host's per-token rate, trading API simplicity for infrastructure ownership and operational overhead. This only makes sense once your volume is high enough to justify the fixed cost of hardware and the engineering time to run it; for most teams starting out, a hosted per-token API remains the simpler and often cheaper path until volume changes that calculation.
Cutting your Llama bill
Start by shopping hosts directly rather than accepting the first rate you see. The spread on Llama 4 Maverick alone spans an order of magnitude across providers. Default to Llama 4 Scout instead of Maverick wherever its lighter capability is enough for the task, since the gap between them adds up fast at volume. Beyond host and model choice, the usual levers still apply: cap output length, trim prompts to what's necessary, and reuse repeated context across calls where your chosen host's API supports it.
A worked example on Llama 4 Maverick
Using the representative rate shown here ($0.20 input / $0.60 output per million tokens), a 1,000-token prompt with a 500-token answer costs 1,000 ÷ 1,000,000 × $0.20 = $0.0002 for input, plus 500 ÷ 1,000,000 × $0.60 = $0.0003 for output, $0.0005 per call, or $50 at 100,000 calls a month. That's a useful baseline, but remember it's tied to one representative rate: a host at the low end of the observed $0.10-$3 range could land well below that $50 figure, and a host at the high end well above it, for the identical model and the identical traffic.
Compare Llama's host-dependent range against fixed-rate providers like OpenAI's GPT API pricing, Mistral's API pricing, or the equally budget-focused DeepSeek API pricing calculator. For a side-by-side view across every provider at once, use the all-provider hub calculator.
