DeepSeek R1 Distill Llama 70B — Spécifications
| Développeur | DeepSeek |
|---|---|
| Type | LLM (dense, raisonnement) |
| Modalité | Texte → Texte |
| Paramètres | 70 milliards |
| Fenêtre de contexte | 128 K |
| Sortie maximale | — |
| Licence | MIT (ouverte) |
| Poids ouverts | Oui |
| Publié | 2025 |
| Prix de l’entrée | 0,80 $ par million de jetons |
| Prix de la sortie | 0,80 $ par million de jetons |
| Fournisseurs d'API | DeepInfra, OpenRouter, Ollama |
Exécutez-le localement
| VRAM (4 bits) | ~40 Go |
|---|---|
| GPU minimal requis | 2 × RTX 4090 / 1 × GPU 48 Go |
What is DeepSeek R1 Distill Llama 70B?
This is DeepSeek R1’s reasoning distilled into a 70B Llama base — an attempt to carry
R1’s step-by-step chain-of-thought behaviour onto hardware a single team can actually own.
It is MIT-licensed with a 128K context, needs about 40 GB of VRAM at 4-bit, and runs on two
RTX 4090s or one 48 GB card.
That hardware line is the whole point. Full DeepSeek R1 needs roughly 400 GB at the same
quantisation; this distill needs a tenth of that, which moves it from “rent a server” to
“buy a workstation”. The trade is real — a distill inherits the teacher’s reasoning style
more faithfully than its raw capability, so expect it to show its working well but to fall
short of R1 on the hardest problems. Where it earns its place is in workloads that must stay
on-premises: regulated data, air-gapped environments, or products whose margins cannot
absorb per-token API pricing at volume. At $0.80 for both input and output it is also
unusually simple to budget, since the input/output ratio is 1:1 and generation-heavy
workloads cost no more than prompt-heavy ones.
DeepSeek R1 Distill Llama 70B pricing: API cost per 1M tokens
| Entrée (par million de jetons) | $0.800 |
|---|---|
| Sortie (par million de jetons) | $0.800 |
| Output/input ratio | 1× |
| Blended (4:1 in:out) | $0.800 per 1M tokens |
What DeepSeek R1 Distill Llama 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.
| Charge de travail | Jetons/mois | Cost / month |
|---|---|---|
| Projet secondaire | 1 million en entrée / 0,25 million en sortie | $1.00 |
| Petite équipe | 20 millions en entrée / 5 millions en sortie | $20 |
| Production | 200 millions en entrée / 50 millions en sortie | $200 |
Run your own numbers in the Calculateur de coûts des API IA.
Cheaper alternatives to DeepSeek R1 Distill Llama 70B
| Modèle | Coût combiné par million de dollars | You save |
|---|---|---|
| Mistral 7B ouverte | $0.0220 | 97% cheaper |
| Llama 3.1 8B ouverte | $0.0220 | 97% cheaper |
| Mistral NeMo 12B ouverte | $0.0240 | 97% cheaper |
Self-host or pay the API?
DeepSeek R1 Distill Llama 70B is open-weight, so you can run it yourself. It needs ~40 Go 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 calculateur auto-hébergement vs API works out the break-even point for your token volume.
Questions fréquemment posées
How much does DeepSeek R1 Distill Llama 70B cost per 1M tokens?
DeepSeek R1 Distill Llama 70B costs $0.800 per 1M input tokens and $0.800 per 1M output tokens. At a typical 4:1 input-to-output mix that blends to about $0.800 per 1M tokens.
How much does DeepSeek R1 Distill Llama 70B cost per month?
A small-team workload of 20M input and 5M output tokens a month costs about $20 on DeepSeek R1 Distill Llama 70B. A side project (1M in / 0.25M out) costs roughly $1.00.
What is a cheaper alternative to DeepSeek R1 Distill Llama 70B?
Mistral 7B is the strongest cheaper option in our database at $0.0220 per 1M blended — about 97% less than DeepSeek R1 Distill Llama 70B. It is also open-weight, so self-hosting is an option.
Can I run DeepSeek R1 Distill Llama 70B locally?
Yes. DeepSeek R1 Distill Llama 70B is open-weight and needs about ~40 GB of VRAM at 4-bit quantisation (2× RTX 4090 / 1× 48GB).
Why does DeepSeek R1 Distill Llama 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. DeepSeek R1 Distill Llama 70B charges 1× more for output, which is why prompt-heavy workloads are far cheaper to run than generation-heavy ones.
See every DeepSeek model priced side by side: DeepSeek API pricing.
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 Base de données des modèles d'IA ou le Classement des grands modèles linguistiques (LLM).
