Llama 4 Maverick — Spezifikationen
| Entwickler | Meta |
|---|---|
| Typ | Multimodal (MoE) |
| Modality | Text, Bild → Text |
| Parameter | 400 Mrd. insgesamt / 17 Mrd. aktiv (MoE) |
| Kontextfenster | 1 Mio. |
| Maximale Ausgabe | — |
| Lizenz | Llama 4 Community (EU-beschränkt) |
| Offene Gewichte | Ja |
| Veröffentlicht | 2025 |
| Eingabepreis | 0,15 $ pro 1 Mio. |
| Ausgabepreis | 0,60 $/1 Mio. |
| API-Anbieter | Meta, Together, OpenRouter |
Lokal ausführen
| VRAM (4-Bit) | ca. 240 GB |
|---|---|
| Mindest-GPU | Multi-GPU-Server |
What is Llama 4 Maverick?
Llama 4 Maverick is the larger Llama 4 — 400B total parameters with 17B active across 128
experts, natively multimodal, with a 1M-token context. It is fully open under the Llama 4
Community License, with the important caveat that the licence restricts use in the EU.
That restriction is not a footnote for European teams; it is the deciding factor. The
Llama 4 Community License places conditions on EU-based use that many organisations cannot
accept, which means Maverick is effectively unavailable to a large part of the market
regardless of its technical merits. Where it is usable, the profile is strong: native
multimodality, a 1M context, and $0.15 in / $0.60 out per million tokens — cheap for a
model of this class. Self-hosting requires roughly 240 GB at 4-bit, so a multi-GPU server.
If you need permissively licensed open weights without geographic conditions, Qwen3 235B-A22B
(Apache 2.0) and Mistral Large 3 (Apache 2.0) occupy similar ground with cleaner terms.
Llama 4 Maverick pricing: API cost per 1M tokens
| Eingabe (pro 1 Mio. Token) | $0.150 |
|---|---|
| Ausgabe (pro 1 Mio. Token) | $0.600 |
| Output/input ratio | 4× |
| Blended (4:1 in:out) | $0.240 per 1M tokens |
What Llama 4 Maverick costs per month
Real monthly spend at a 4:1 input-to-output mix — the ratio a typical chat or RAG workload actually produces.
| Workload | Tokens/Monat | Cost / month |
|---|---|---|
| Nebenprojekt | 1 Mio. Eingabe / 0,25 Mio. Ausgabe | $0.30 |
| Kleines Team | 20 Mio. Eingabe / 5 Mio. Ausgabe | $6.00 |
| Produktion | 200 Mio. Eingabe / 50 Mio. Ausgabe | $60 |
Run your own numbers in the KI-API-Kostenrechner.
Cheaper alternatives to Llama 4 Maverick
| Modell | Gewichteter Preis pro Million US-Dollar | You save |
|---|---|---|
| DeepSeek V4-Flash offenere | $0.168 | 30% cheaper |
| Qwen3 32B offenere | $0.120 | 50% cheaper |
| Llama 4 Scout offenere | $0.140 | 42% cheaper |
Self-host or pay the API?
Llama 4 Maverick is open-weight, so you can run it yourself. It needs ca. 240 GB of VRAM at 4-bit (Multi-GPU server). Self-hosting only beats the API once your volume is high enough to keep that hardware busy — the Selbsthosting-vs.-API-Rechner works out the break-even point for your token volume.
Häufig gestellte Fragen
How much does Llama 4 Maverick cost per 1M tokens?
Llama 4 Maverick costs $0.150 per 1M input tokens and $0.600 per 1M output tokens. At a typical 4:1 input-to-output mix that blends to about $0.240 per 1M tokens.
How much does Llama 4 Maverick cost per month?
A small-team workload of 20M input and 5M output tokens a month costs about $6.00 on Llama 4 Maverick. A side project (1M in / 0.25M out) costs roughly $0.30.
What is a cheaper alternative to Llama 4 Maverick?
DeepSeek V4-Flash is the strongest cheaper option in our database at $0.168 per 1M blended — about 30% less than Llama 4 Maverick. It is also open-weight, so self-hosting is an option.
Can I run Llama 4 Maverick locally?
Yes. Llama 4 Maverick is open-weight and needs about ~240 GB of VRAM at 4-bit quantisation (Multi-GPU server).
Why does Llama 4 Maverick charge more for output than input?
Output tokens are generated one at a time and cannot be batched the way a prompt can, so they cost the provider more to serve. Llama 4 Maverick charges 4× more for output, which is why prompt-heavy workloads are far cheaper to run than generation-heavy ones.
Prices are the published list rates for the model's primary API and are reviewed as providers change them. Volume, batch and cached-input discounts are not included. Compare every model side by side in the Datenbank für KI-Modelle oder das LLM-Leaderboard.
