Llama 4 Maverick API pricing

By the aitokensai editors · Verified 2026-11-14 Sources: Llama API docs

Meta's Llama 4 Maverick. Available at listed at platform rate (no published official USD price).

USD, standard tier. Rate read from the live rate config on 2026-11-14. Meta publishes no USD list price for this model, so nothing here is cross-checked against one — this is a gateway rate, not a discount.

Specifications

What Llama 4 Maverick costs at your volume

Calculated at $0.07 input / $0.35 output per 1M tokens, 5.0× apart on this model, assuming a 5:1 input-to-output ratio. That ratio is the single largest source of error in this table: resend-heavy workloads drift well past it, and at 5.0× the correction is not linear. The cost calculator takes the actual figures.

What Llama 4 Maverick is for

$0.07 per million input tokens puts Llama 4 Maverick low enough that volume stops being the first question. This is a model you can point at thousands of calls a day and not think about: summarisation at scale, autocomplete, extraction pipelines, tier-one filtering in front of a stronger model.

Two honest limits. At 5.0×, output is still the majority of the bill, so this is not the cheapest option for genuinely long generation. And at this price the saving from staying cheap is small — escalating a hard case to a flagship costs pennies next to the cost of getting it wrong.

Where Llama 4 Maverick sits in the index

Llama 4 Maverick is 22 of 231 on input rate and 41 of 231 on output rate. A gap of 19 places, because on this model the output rate is 5.0× the input rate, steeper than the index median of 4.0× An output-heavy workload therefore leaves it almost where it started, which is why the cheapest model per input token is routinely not the cheapest per task. Percentile is the share of the 231 models in this index that cost more than Llama 4 Maverick.

On an agent mix the lowest blended rate in the same index is Gemma2 9B It at $0.01 per 1M tokens (about 28).0× less than this model at the same ratio. Across the whole index, sorting by input price alone moves a model by as much as 77 places; on Llama 4 Maverick specifically the shift is 19 — which is what the re-ranked index exists to show.

Llama 4 Maverick is 22 of 231 across every model in the index; the comparison that usually settles a choice is Meta's own lineup further down.

Llama 4 Maverick among the 28 US frontier-family models

Llama 4 Maverick is the 4th cheapest of the 28 US frontier-family models in this index at $0.07 per 1M input tokens. The range inside that group runs from GPT-5 nano at $0.025 to GPT-6 Astra at $5, a spread of 200×, and this one sits at 2.8× the group's cheapest tier.

On input rate alone, stepping up to GPT-5.6 Luna costs 1.4× the input rate and dropping to Claude Haiku 5.5 cuts it by 29%. Both measured at 2026-11-14, the same check as every other rate on this page.

Similarly priced alternatives

Blended comparison (input + output per 1M). Price is one axis These are the three closest to Llama 4 Maverick in blended rate, not interchangeable substitutes for it.

Calling Llama 4 Maverick

The identifier you send is llama-4-maverick. Copy it exactly, because a mismatch returns a model error rather than a connection error, which sends most people to debug the wrong thing. Everything else is a base URL argument (Llama 4 Maverick's own SDKs work unchanged).

from openai import OpenAI

client = OpenAI(base_url="https://aicomp.ai/v1", api_key="sk-...")

resp = client.chat.completions.create(
    model="llama-4-maverick",
    messages=[
        {"role": "system", "content": "Answer in one short paragraph."},
        {"role": "user", "content": "Explain what changed in this release."},
    ],
    max_tokens=300,
)

# prompt_tokens / completion_tokens 是真实账单的唯一依据 —— 记下来，别猜
print(resp.usage.prompt_tokens, resp.usage.completion_tokens)

Log the usage object on every call, not just while you are optimising. On Llama 4 Maverick output costs 5.0× what input costs, so the same monthly token count bills very differently depending on how much of it comes back as output — measure your own split before extrapolating from the headline input rate, because rates move with vendor promotions.

Setting it up

Pointing an existing integration at Llama 4 Maverick is a base URL change, not a rewrite. Pick your tool:

• Claude Code — set the base URL via environment variables
• Cursor — override the API endpoint in settings
• OpenAI / Anthropic SDK — one base_url argument
• Python, sync, streaming and async examples
• Cutting the bill — eight levers ranked by effect

What different usage shapes cost on Llama 4 Maverick

A monthly token budget does not convert cleanly into a monthly invoice, and Llama 4 Maverick is an ordinary case rather than an extreme one: output is priced at 5.0× input, above the median of 4.0× across the 231 models in our index. Everything follows from that one number At 5:1 it is manageable, at 1:1 the same token count costs half as much. Below are five shapes real Llama 4 Maverick deployments fall into, each costing it out at the split it actually produces.

The cheapest comparison is GPT-5 nano at $0.025 / $0.2 per 1M — 1.9× cheaper than Llama 4 Maverick on a blended rate. Treat it as a rate floor, not a recommendation: it says nothing about whether GPT-5 nano can do the work you need Llama 4 Maverick for.

Read the table as a sanity check rather than a forecast. If your own output share sits anywhere near 3%, everything above is priced almost entirely at Llama 4 Maverick's $0.07 input rate; at 40% the $0.35 output rate has taken the larger half. Nobody's split is exactly the one you assumed when you picked the model, which is why the calculator asks for it before quoting anything.

FAQ

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