As of August 21, 2026, Llama 4 Scout API pricing is $0.08 per million input tokens and $0.30 per million output tokens. A typical 100K-in / 20K-out request costs $0.01. Source: https://llama.meta.com/
Source: Meta pricing (https://llama.meta.com/)
Input $/M
$0.08
Output $/M
$0.30
Blended / 1M
$0.38
100K / 20K request
$0.01
| Context Window | 10M |
|---|---|
| Max Output | 10M tokens |
| Release Date | 2025-04-05 |
| Knowledge Cutoff | N/A |
| License | Open Source |
| Throughput | 76.1 tok/s |
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As of August 21, 2026, Llama 4 Scout Meta API pricing is $0.08 per million input tokens and $0.30 per million output tokens. Input tokens are the prompt you send; output tokens are the completion. Long answers therefore cost more than short ones even when the prompt stays the same. Source: https://llama.meta.com/
On this page the blended Llama 4 Scout API cost is $0.38 for 1 million input tokens plus 1 million output tokens. That 1:1 mix is the comparison scale used across the LLM pricing table. A 3:1 mix (three million input tokens per one million output tokens), which some calculators use, would be about $0.54.
A typical Llama 4 Scout API request with 100K input tokens and 20K output tokens costs about $0.01 at current rates as of August 21, 2026. Multiply that by daily or monthly volume to estimate spend. Caching, batch APIs, and volume discounts from Meta can bring the real invoice lower than the public list rate at https://llama.meta.com/.
Llama 4 Scout supports a 10M token context window and can generate up to 10M output tokens per request. The context window is the total prompt plus conversation history the model can see in one call. Larger windows cost more when you fill them, because every input token is billed per million tokens.
Yes. Llama 4 Scout is an open-weight model by Meta. You can download the weights and self-host, or pay Meta's hosted API and skip GPU ops. Hosted API pricing on this page is the public per-million-token rate, not the electricity cost of running the weights yourself.
Llama 4 Scout currently reports GPQA 57.2%, GPQA Diamond 57.2%, LiveCodeBench 32.8%. See the performance table for the full eval set. Use those scores with the token prices above when you are choosing between a cheaper LLM and a frontier model. GPQA, SWE-bench, and HLE are the evals people usually compare first.
Llama 4 Scout scores 32.8% on LiveCodeBench. Pair that score with Meta API pricing on this page: a slightly worse SWE-bench model can still win if it is much cheaper per million tokens. Open the SWE-bench or coding leaderboard from the sidebar for the full ranking, then jump into an LLM comparison against a coding specialist.
Use the Compare picker on this page to start an LLM comparison. Search by model or provider, pick a second model, and AnotherWrapper opens a dedicated Llama 4 Scout vs page with per-million-token prices, blended 1M-in / 1M-out cost, context window, throughput, and benchmarks. You can also open any model from the sidebar, or go back to the LLM pricing table to sort the full index.
The cheapest LLM alternative depends on whether you optimize for blended token cost, coding evals, or context window. Start with the popular matchups on this page, then sort the LLM pricing table by blended cost to see which hosted APIs undercut Llama 4 Scout. Open-weight models can be cheaper still if you self-host, but that is not the same as Meta API pricing on this page.
Llama 4 Scout is called through Meta's API with an API key. Bill against input and output tokens separately; cache or shorten prompts if spend is high. If you are shipping a full product, AnotherWrapper includes pre-built API routes and templates for Meta and 5+ other providers with auth, payments, and deployment already configured.
This Llama 4 Scout page tracks the latest published Meta API pricing from Meta, quoted per million tokens. List rates can differ from invoices because of prompt caching, batch discounts, committed-use tiers, and private contracts. Official rate sheet: https://llama.meta.com/. Benchmark scores come from official publications and independent evals, not from a private lab run by AnotherWrapper.
Llama 4 Scout has a measured throughput of about 76.1 tokens per second (tok/s). Throughput is how fast completions stream once the model starts generating; time-to-first-token can still lag under load. Faster APIs matter when you are building chat or agents, even if the per-million-token price is the same.
Llama 4 Scout is a open-source model from Meta. Current API pricing is $0.08 per million input tokens and $0.30 per million output tokens, with a 10M token context window. A blended 1M-in / 1M-out workload costs $0.38.
Rank it on the LLM leaderboard, SWE-bench, and Humanity's Last Exam.
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