blue gradient
highlight 5

Llama 4 Pricing Calculator

Get accurate, up‑to‑date Llama 4 pricing for Scout & Maverick. LiveChatAI’s calculator converts words, tokens, or chars, reveals true cost & savings tips.
vector
burst pucker
Trusted by +2K businesses
popupsmart
userguiding
VLmedia
ikas
formcarry
Peaka

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 2025 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 2025 “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 (2025)

Meta quotes a single “blended” cost assuming 3 input : 1 output tokens. We reverse‑engineer that into separate input/output lines so you can budget accurately.

Token type Rate per 1 M Notes
Input $0.143 Text, code, or vision patches
Output $0.429 Streaming or chunked
Blended (3:1) $0.19–$0.49 Range reflects single‑host vs. distributed inference

⭐ Same cost for Scout & Maverick. The only difference is latency and GPU count.

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 a line‑item cost that matches Meta’s invoice.

How the Llama 4 Pricing Calculator Works

1. Choose your unit. Tokens for API logs, words for docs, characters for snippets.

Llama 4 Pricing Calculator Calculation Options


2. Enter three numbers.

  • Input size (prompt)
  • Output size (expected reply)
  • API calls (per day, week, or month)
Llama 4 Pricing Calculator Input, Output and API Calls

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) ✅ 10 M tokens keep everything in one window
Vision Q&A (damage detection, product QA, alt‑text) ✅ Stronger image grounding
Single‑GPU deployment (edge device, on‑prem PoC) ✅ Int4 fits on 1× H100 Needs multi‑GPU for best latency
Top‑tier reasoning / creative writing Good ✅ Slightly higher ELO & benchmark scores
Lowest total hardware cost ✅ Runs cheap locally Cloud GPU recommended

Rule of thumb: If your prompt regularly breaks 1 M tokens, go Scout. Otherwise, use Maverick for its sharper reasoning and vision accuracy.

Quick Benchmark Snapshot

Benchmark Scout (17 B‑16E) Maverick (17 B‑128E) GPT‑4o Gemini 2.5 Pro Claude 3.7
MMMU (vision) 71.7 73.4 69.1 75.0 75.0
LiveCodeBench v5 34.5 43.4 32.3 70.3
Multilingual MMLU 84.6 81.5 89.8
Cost / 1 M blended $0.19–$0.49 $0.19–$0.49 $4.38 $0.17 $18.00 (input + output)

⭐ Takeaway: Maverick edges Scout on accuracy, both demolish GPT‑4o on price.

More Useful (and Free!) Tools at LiveChatAI

Don't forget—we offer a wide range of free tools to help you better leverage AI:

Bookmark them, run your what‑ifs, and never be surprised at month‑end.

Summary for Busy Builders

  • Scout vs. Maverick – same token price; Scout wins on length, Maverick on brains.
  • Preview rates – ~$0.19–$0.49 per blended million tokens, an order‑of‑magnitude cheaper than GPT‑4o.
  • Calculator – paste words, characters, or tokens; see dollars instantly.
  • Cost hacks – stream, cache, batch, and trim context to cut up to half your spend.

Open the calculator, plug in your real numbers, and ship with confidence—Llama 4 won’t chew through your budget.

Frequently asked questions

How much does Llama 4 cost per 1 000 tokens?
plus icon
Preview math comes out to $0.000143 for input and $0.000429 for output. A 1 000‑input + 1 000‑output call costs ≈ $0.00057.
Do image patches cost extra?
plus icon
No—each patch token is billed at the same input rate.
Can Scout really handle 10 M tokens?
plus icon
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?
plus icon
• Self‑host from Hugging Face weights
• Meta’s partner clouds (AWS, Azure, GCP)
• Edge GPU with quantised Scout
Will pricing change after preview?
plus icon
Meta hasn’t locked GA rates. LiveChatAI updates the calculator the moment new prices land.