Meta Llama Token Counter & Pricing (2026)

There's no such thing as "Meta Llama pricing" — and that trips up almost everyone who searches for it. Meta releases the weights under the Llama Community License. They don't run a paid API, they don't bill per token, and the consumer chat at meta.ai (and the assistant inside WhatsApp/Instagram) is free. The number you actually pay lives with the host: AWS Bedrock, Together AI, Fireworks AI, or your own GPUs. Llama 4 Maverick runs about $0.24 input / $0.97 output per million tokens on Bedrock in 2026. Llama 4 Scout is $0.17 / $0.66 with a 10M-token context. Llama 3.3 70B is $0.72 / $0.72. That's the spread you plan around.

This page covers what a Llama token actually is, why the 128K SentencePiece vocabulary matters for your bill, what the realistic Llama pricing looks like across the three production models, and how to estimate real costs using the AI Token Calculator.

Where Llama Stands in 2026

Meta has been open-sourcing Llama since February 2023. Every major release ships under the Llama Community License: download the weights, run them locally, fine-tune, redistribute — as long as no one uses the result to train a competing LLM above 700M MAU. That license is why "meta llama pricing" returns five different rate cards instead of one Meta checkout page.

Two distinct things share the "Llama" name and get confused constantly:

• Meta Llama — the family of open-weight models on Hugging Face (meta-llama/*). This is what developers build against.
• Meta AI — the consumer chat product at meta.ai and inside WhatsApp/Instagram/Messenger. Runs Llama under the hood but is free and unsubsidized by Meta. Developer work ignores this surface.

The open-weight design has a direct cost consequence. Anyone with a few H100s can spin up a Llama endpoint, and the host market competes aggressively. At equivalent input sizes, hosted Llama 3.3 70B is typically 40–60% cheaper than Claude Haiku 5 and 60–80% cheaper than GPT-5-class models. You trade ecosystem maturity (rate limits, observability, regional availability) for that saving.

Llama Models & Pricing

Three production models matter in 2026. Llama 3.3 is the dense text workhorse. Llama 4 is the multimodal MoE family with native image and video understanding. All three use the same 128K SentencePiece vocabulary, so token counts line up across models — what changes is the per-million rate your host charges.

A few practical notes on the numbers:

• Bedrock batch pricing drops Maverick to $0.12 / $0.485 and Scout to $0.085 / $0.33 per 1M tokens — half off standard for async jobs.
• No cached-input pricing yet for Llama on Bedrock. Unlike Claude or GPT, prompt caching isn't a first-class feature in the AWS console as of late August 2026.
• Together AI / Fireworks typically undercut Bedrock on the dense 3.3 70B by 30–50%, but their Llama 4 MoE rates track closer to Bedrock because the hosts pass through Meta's reference pricing.

Compared to frontier closed models at the same context length: Llama 4 Maverick at $0.24/$0.97 is roughly 2.5% the cost of GPT-6 astra ($10/$50) and 2.4% the cost of Claude Fable 5 ($10/$50). Llama 4 Scout undercuts them further while shipping a 10M-token window — twenty times longer than Claude's 500K.

How to Count Llama Tokens

Every Llama 3.x and 4.x model tokenizes with Google's SentencePiece BPE on a 128,256-entry vocabulary. Llama 2 used the same algorithm but with a 32,000-entry vocab. The 4× expansion cut token counts by roughly 15% on the same English text.

Concretely, English text tokenizes at about 4.0 characters per token on Llama 3+. A 10,000-character article is roughly 2,500 tokens — versus 2,857 on Llama 2. At $0.24 per million input on Maverick, that's about $0.0006 per article. Small per call, but it adds up on batch workloads.

Multilingual efficiency varies hard. Llama's tokenizer was trained heavily on English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai. Korean, Chinese, and Arabic fragment aggressively — a single Korean sentence might produce 30–60 subword tokens compared to 8 for the same meaning in English. If your workload is heavily multilingual, run your prompts through the Llama tokenizer before committing; model quality alone won't tell you what the bill looks like.

To inspect tokenization yourself:

1. Load meta-llama/Llama-4-Maverick or Xenova/Llama-3.3-70B-Instruct via Hugging Face transformers (browser-friendly versions exist on Xenova).
2. Call .encode("your prompt") — the returned integer list is the exact sequence the model sees.
3. The same encoding applies to any Llama 3.x/4.x host because they all share the vocabulary.

For a no-code path, paste any prompt into the AI Token Calculator — it applies the Llama tokenizer and shows the dollar figure across Maverick, Scout, and 3.3 70B at your chosen host's rates.

Llama 4 Architecture (Why It's Cheaper Than It Looks)

Llama 4's headline pricing understates the engineering. Both Scout and Maverick are Mixture-of-Experts models — not the dense 70B architecture of Llama 3.3.

• Llama 4 Scout: 17 billion active parameters, 16 experts. Only 17B run per token; the routing decides which experts fire. Total parameter count is much higher, but inference cost tracks the active path.
• Llama 4 Maverick: 17 billion active parameters, 128 experts. Same active compute as Scout, but more experts means finer routing and better quality on reasoning-heavy tasks.

The practical upshot: Maverick and Scout both run at the cost of a ~17B dense model, even though quality tracks closer to a 70B+. That's why $0.24/$0.97 for Maverick looks almost too good next to GPT-6 or Claude Fable — the active parameter count is an order of magnitude smaller than the frontier closed models.

Context windows are the other architectural point. Scout's 10M tokens is the largest production open-weight context as of mid-2026. Maverick ships at 1M. Both support native image and video input — a 512×512 image typically bills as roughly 255 tokens on Maverick, which matters for multimodal cost forecasting.

Where to Deploy Llama

Pricing varies by host. The four options you'll actually evaluate:

Self-hosting math: Llama 3.3 70B runs on a single H100 at full precision, or a pair of A100s. Quantized 4-bit variants fit on a single 24GB consumer GPU (RTX 4090). Llama 4 Scout fits on a single H100. At AWS on-demand H100 rates around $3/GPU-hour, self-hosting breaks even below roughly 5 million input tokens per day — below that, hosted inference wins on cost. Above that, self-hosting typically wins by 40–60%.

Always recheck the host's live pricing page before budgeting. Together AI and Fireworks change rates more often than Bedrock.

Real Bill Examples

These use the verified Bedrock rates from models.json. Adjust to your host's published prices.

Workload 1: Customer-support chatbot, 50,000 conversations/month. Each conversation averages 600 input tokens (question plus a 300-token system prompt) and 250 output tokens. On Llama 3.3 70B at $0.72/$0.72: - Input: 30,000,000 × $0.72 = $21.60 - Output: 12,500,000 × $0.72 = $9.00 - Total: $30.60/month

Switch to Llama 4 Maverick for better reasoning and the same workload: 30M × $0.24 + 12.5M × $0.97 = $7.20 + $12.13 = $19.33/month — 37% cheaper than the dense 70B.

Workload 2: Long-document RAG, 1,000 packets/month at 80,000 tokens each. Each packet produces a 1,200-token answer. On Llama 4 Scout (the 10M-context model): - Input: 80,000,000 × $0.17 = $13.60 - Output: 1,200,000 × $0.66 = $0.79 - Total: $14.39/month

The same workload on Llama 3.3 70B would cost 5× more in input (80M × $0.72 = $57.60), and you couldn't even fit a single 80K-token packet plus context into 3.3's dense model without chunking. Scout removes the chunker.

Workload 3: Code review agent, 800 reviews/month. Each review reads 10,000 input tokens (PR diff plus repo context) and emits 2,000 output tokens. On Llama 4 Maverick: - Input: 8,000,000 × $0.24 = $1.92 - Output: 1,600,000 × $0.97 = $1.55 - Total: $3.47/month

Same workload on Claude Fable 5 would cost 8M × $10 + 1.6M × $50 = $160/month. Llama 4 Maverick is roughly 2% of the Claude bill at the same context length.

Workload 4: Batch summarization, 50,000 documents/month at 4,000 tokens each. Async batch processing on Maverick (Bedrock batch tier, $0.12 / $0.485 per 1M): - Input: 200,000,000 × $0.12 = $24.00 - Output: 50,000,000 × $0.485 = $24.25 - Total: $48.25/month for half a billion tokens of throughput

Frequently Asked Questions

What is a Llama token? A subword unit produced by SentencePiece BPE on a 128,256-entry vocabulary. English averages about 4.0 characters per token on Llama 3+ and 4. Llama 2 used the same algorithm but a smaller 32,000 vocab, which is why migrated workloads saw ~15% token-count drops.

How much does the Llama API cost per million tokens? Llama 3.3 70B on AWS Bedrock: $0.72 / $0.72 per 1M input/output. Llama 4 Scout: $0.17 / $0.66. Llama 4 Maverick: $0.24 / $0.97. Together AI and Fireworks typically undercut Bedrock on the dense 3.3 70B by 30–50%; Llama 4 MoE rates track closer to Bedrock across hosts.

Why are Llama 4 Scout and Maverick priced so low? Both are Mixture-of-Experts with only 17B active parameters per token — inference compute tracks the active path, not the total parameter count. Maverick ships 128 experts, Scout 16. Quality lands closer to a 70B+ dense model at a fraction of the compute, which is why $0.24/$0.97 is structurally possible.

Can I run Llama myself to avoid per-token pricing? Yes. Llama 3.3 70B runs on a single H100 at full precision or a pair of A100s. Quantized 4-bit variants fit on a 24GB consumer GPU. Llama 4 Scout fits on one H100. Self-hosting break-even is roughly 5M input tokens per day — below that, hosted inference is cheaper once you factor in GPU hourly cost.

What's the difference between Meta Llama and Meta AI? Meta Llama is the open-weight model family on Hugging Face under the Llama Community License. Meta AI is the free consumer chat at meta.ai and inside Meta's apps — built on Llama models but not billed per token. Developer work happens against hosted Llama endpoints, not Meta AI.

Sources

1. Meta AI — Llama 4 model announcement and reference docs: https://ai.meta.com/llama/
2. Meta — Llama API reference and tokenizer details: https://llama.meta.com/docs/
3. AWS Bedrock — Llama pricing (us-east-1, verified 2026-08-28): https://aws.amazon.com/bedrock/pricing/
4. Together AI — Llama serverless pricing: https://api.together.ai/playground/pricing
5. Fireworks AI — Llama inference pricing: https://fireworks.ai/pricing
6. Hugging Face — meta-llama/Llama-4-Maverick and Xenova/Llama-3.3-70B-Instruct tokenizer configs