Llama 3.3 70B — Spezifikationen
| Entwickler | Meta |
|---|---|
| Typ | LLM (dicht) |
| Modality | Text → Text |
| Parameter | 70 Mrd. |
| Kontextfenster | 128 K |
| Maximale Ausgabe | — |
| Lizenz | Llama 3.3 Community (offen) |
| Offene Gewichte | Ja |
| Veröffentlicht | 2024 |
| Eingabepreis | 0,10 $/1 Mio. |
| Ausgabepreis | 0,32 $/1 Mio. |
| API-Anbieter | Together, DeepInfra, OpenRouter, Ollama |
Lokal ausführen
| VRAM (4-Bit) | ~40 GB |
|---|---|
| Mindest-GPU | 2× RTX 4090 / 1× 48 GB |
What is Llama 3.3 70B?
Llama 3.3 70B is Meta’s efficient dense 70B — close to 405B-class quality at a fraction of
the size, with a 128K context. It needs about 40 GB of VRAM at 4-bit, so two RTX 4090s or a
single 48 GB card, and it is among the most-deployed open models for self-hosting.
Dense is the operative word. Nearly every large open model released since is a
mixture-of-experts, which lowers inference compute but keeps the full parameter count
resident in memory — DeepSeek V4-Pro activates 49B parameters but still needs roughly 800 GB
of VRAM to hold. A dense 70B has no such gap: what you load is what you use, so 40 GB is the
whole story. For teams sizing hardware, that predictability is worth a lot, and it is why
3.3 70B remains the default for on-premises deployments even as newer architectures post
better benchmark numbers. At $0.10 in / $0.32 out per million tokens the API is cheap enough
that self-hosting only makes sense for data-residency reasons or at sustained high
volume.
Llama 3.3 70B pricing: API cost per 1M tokens
| Eingabe (pro 1 Mio. Token) | $0.100 |
|---|---|
| Ausgabe (pro 1 Mio. Token) | $0.320 |
| Output/input ratio | 3.2× |
| Blended (4:1 in:out) | $0.144 per 1M tokens |
What Llama 3.3 70B 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 | $0.18 |
| Kleines Team | 20 Mio. Eingabe / 5 Mio. Ausgabe | $3.60 |
| Produktion | 200 Mio. Eingabe / 50 Mio. Ausgabe | $36 |
Run your own numbers in the KI-API-Kostenrechner.
Cheaper alternatives to Llama 3.3 70B
| Modell | Gewichteter Preis pro Million US-Dollar | You save |
|---|---|---|
| Mistral 7B offenere | $0.0220 | 85% cheaper |
| Llama 3.1 8B offenere | $0.0220 | 85% cheaper |
| Mistral NeMo 12B offenere | $0.0240 | 83% cheaper |
Self-host or pay the API?
Llama 3.3 70B is open-weight, so you can run it yourself. It needs ~40 GB of VRAM at 4-bit (2× RTX 4090 / 1× 48GB). 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 3.3 70B cost per 1M tokens?
Llama 3.3 70B costs $0.100 per 1M input tokens and $0.320 per 1M output tokens. At a typical 4:1 input-to-output mix that blends to about $0.144 per 1M tokens.
How much does Llama 3.3 70B cost per month?
A small-team workload of 20M input and 5M output tokens a month costs about $3.60 on Llama 3.3 70B. A side project (1M in / 0.25M out) costs roughly $0.18.
What is a cheaper alternative to Llama 3.3 70B?
Mistral 7B is the strongest cheaper option in our database at $0.0220 per 1M blended — about 85% less than Llama 3.3 70B. It is also open-weight, so self-hosting is an option.
Can I run Llama 3.3 70B locally?
Yes. Llama 3.3 70B is open-weight and needs about ~40 GB of VRAM at 4-bit quantisation (2× RTX 4090 / 1× 48GB).
Why does Llama 3.3 70B 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 3.3 70B charges 3.2× 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.
