Get accurate, up‑to‑date Llama 4 pricing for Scout & Maverick. LiveChatAI’s calculator converts words, tokens, or chars, reveals true cost & savings tips.
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Llama 4 Pricing Calculator - The Clear‑Cut Guide for Scout and Maverick
Searching “Llama 4 Pricing Calculator” because you need hard numbers before you ship? You’re in the right spot.
Llama 4 is Meta’s 2026 multimodal model family—two variants, Scout and Maverick, that chew through text and images while holding up to 10 million tokens of context. That means you can feed a legal archive, a season of video transcripts, or an entire codebase into a single prompt without breaking it into chunks.
Scout is the long‑context specialist: 10 M‑token window, single‑GPU deploy.
Maverick is the flagship brain: 1 M‑token window plus sharper reasoning and vision grounding.
LiveChatAI’s free Llama 4 Pricing Calculator turns those monster capabilities into clear dollars and cents.
Why Llama 4 Matters
Llama 4 is Meta’s 2026 “multimodal‑native” model family—built to pause, reason, and then respond. Both variants understand text and images out of the box and ship with monster‑sized context windows, so you can paste an entire code repo, a season of video transcripts, or thousands of legal PDFs into a single request.
Variant
Context Window
Params (Active / Total)
Key Benefit
Scout
10 M tokens
17 B / 109 B
Industry‑record long‑form retrieval on a single H100 GPU
Maverick
1 M tokens
17 B / 400 B
Top‑tier reasoning + vision accuracy
Both run a Mixture‑of‑Experts (MoE) architecture: only 17 B parameters fire per token, so you get frontier quality without frontier hardware bills.
Bottom line: Scout is your go‑to for length, Maverick for brains—and both cost the same per token while Llama 4 is in preview.
Up‑to‑Date Preview Pricing of Llama 4 (2026)
Meta publicly highlights a blended cost estimate for Llama 4 Maverick rather than separate input and output token pricing. That makes blended pricing the safest reference point for budgeting at the preview stage.
Token type
Rate per 1 M
Notes
Blended (distributed inference)
$0.19
Meta’s published cost estimate for Llama 4 Maverick using a 3:1 input-to-output blend
Blended (single host projection)
$0.49
Meta’s projected cost estimate for serving Llama 4 Maverick on a single host
Pricing model
Blended estimate
Meta publicly highlights blended pricing rather than separate input and output token rates
⭐ Scout and Maverick differ mainly in context size, deployment profile, and performance focus. Meta’s public pricing estimate is shown most clearly for Maverick, so it is safer to avoid presenting both models as having exactly the same official per-token price.
Tokens, Words, or Characters—Which Do You Use?
Tokens are Meta’s billing atom (≈ ¾ word in English).
Words feel natural for marketers and lawyers (1 word ≈ 1.33 tokens).
Characters are handy for tweets or code (≈ 4 chars per token).
Our calculator accepts any of these, converts behind the scenes, and shows an estimated blended cost based on Meta’s published preview pricing guidance.
How the Llama 4 Pricing Calculator Works
1. Choose your unit. Tokens for API logs, words for docs, characters for snippets.
2. Enter three numbers.
Input size (prompt)
Output size (expected reply)
API calls (per day, week, or month)
3. Instant cost read‑out.
Input cost vs. output cost
Total spend for the period you picked
Side‑by‑side comparison with GPT‑4o, Gemini 2.5 Pro, Claude 3.7, Grok 3, DeepSeek‑R1
4. Tweak on the fly. Slide API calls up or down, adjust output length, and watch the dollars update in real time.
No spreadsheets, no mental math—just numbers you can sanity‑check in 30 seconds.
Five Proven Cost‑Cutting Moves
Stream & stop early. Cap max_output_tokens, stream, and kill the feed once you have the answer. Save 10–40 %.
Chunk once, reuse often. Summarise each PDF section, store summaries, query summaries. 15–35 % input cut.
Function calling. Let Llama return structured JSON; skip downstream parsing calls. 5–20 % fewer round‑trips.
Context caching. Reuse an identical system prompt; Meta bills cached tokens at half rate. Up to 50 % on static context.
Batch inference. Pack multiple prompts into one call via vLLM or Llama.cpp server. 20–45 % overhead saved.
When to Choose Scout vs. Maverick
Use‑case
Pick Scout
Pick Maverick
Ultra‑long retrieval (multi‑doc search, codebase Q&A, research synthesis)
Meta currently presents Llama 4 Maverick pricing as a blended cost estimate instead of separate public input and output token rates. The published reference point is about $0.19 per 1M tokens using a 3:1 input-to-output blend for distributed inference, with a projected cost of about $0.49 per 1M tokens on a single host. If you know your expected usage, the calculator can help you estimate cost based on that blended pricing approach.
Do image patches cost extra?
No—each patch token is billed at the same input rate.
Can Scout really handle 10 M tokens?
Yes. Meta’s “Needle‑in‑a‑Haystack” tests show 100 % retrieval up to 10 M. Expect higher latency, but it works.
Where can I run Llama 4?
• Self‑host from Hugging Face weights • Meta’s partner clouds (AWS, Azure, GCP) • Edge GPU with quantised Scout
Will pricing change after preview?
Meta hasn’t locked GA rates. LiveChatAI updates the calculator the moment new prices land.