DeepSeek V4-Flash — Spezifikationen
| Entwickler | DeepSeek |
|---|---|
| Typ | LLM (MoE) |
| Modality | Text → Text |
| Parameter | 284 Mrd. insgesamt / ~13 Mrd. aktiv (MoE) |
| Kontextfenster | 1 Mio. |
| Maximale Ausgabe | 384 K |
| Lizenz | MIT (offen) |
| Offene Gewichte | Ja |
| Veröffentlicht | 2026-04 |
| Eingabepreis | 0,14 $ pro 1 Mio. |
| Ausgabepreis | 0,28 $ pro 1 Mio. |
| API-Anbieter | DeepSeek, OpenRouter |
Lokal ausführen
| VRAM (4-Bit) | ~140 GB |
|---|---|
| Mindest-GPU | 2× H100 80 GB (4-Bit) |
What is DeepSeek V4-Flash?
DeepSeek V4-Flash is the lighter member of the DeepSeek V4 family — 284B total parameters
with roughly 13B active per token, a 1M-token context window, and open MIT weights. At $0.14
in / $0.28 out per million tokens it is priced for high-volume use, and it is one of the
strongest capability-per-dollar options anywhere in the market.
The number worth internalising is the blended rate of about $0.17 per million tokens
against a frontier model’s $10. That is a ~60× spread for a model that still scores in the
respectable middle of the intelligence rankings, which makes V4-Flash the obvious candidate
for any workload where volume matters more than peak reasoning: bulk classification,
document processing, first-pass summarisation, synthetic data generation, and the retrieval
layer of a RAG pipeline. The 1M context at that price is close to unmatched. Self-hosting is
possible but not casual — about 140 GB of VRAM at 4-bit, so two H100 80GBs — which for most
teams means the API is the practical route and the open weights are insurance rather than a
deployment plan.
DeepSeek V4-Flash pricing: API cost per 1M tokens
| Eingabe (pro 1 Mio. Token) | $0.140 |
|---|---|
| Ausgabe (pro 1 Mio. Token) | $0.280 |
| Output/input ratio | 2× |
| Blended (4:1 in:out) | $0.168 per 1M tokens |
What DeepSeek V4-Flash 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 | $0.21 |
| Kleines Team | 20 Mio. Eingabe / 5 Mio. Ausgabe | $4.20 |
| Produktion | 200 Mio. Eingabe / 50 Mio. Ausgabe | $42 |
Run your own numbers in the KI-API-Kostenrechner.
Cheaper alternatives to DeepSeek V4-Flash
| Modell | Gewichteter Preis pro Million US-Dollar | You save |
|---|---|---|
| Mistral 7B offenere | $0.0220 | 87% cheaper |
| Llama 3.1 8B offenere | $0.0220 | 87% cheaper |
| Mistral NeMo 12B offenere | $0.0240 | 86% cheaper |
Self-host or pay the API?
DeepSeek V4-Flash is open-weight, so you can run it yourself. It needs ~140 GB of VRAM at 4-bit (2× H100 80GB (4-bit)). 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 DeepSeek V4-Flash cost per 1M tokens?
DeepSeek V4-Flash costs $0.140 per 1M input tokens and $0.280 per 1M output tokens. At a typical 4:1 input-to-output mix that blends to about $0.168 per 1M tokens.
How much does DeepSeek V4-Flash cost per month?
A small-team workload of 20M input and 5M output tokens a month costs about $4.20 on DeepSeek V4-Flash. A side project (1M in / 0.25M out) costs roughly $0.21.
What is a cheaper alternative to DeepSeek V4-Flash?
Mistral 7B is the strongest cheaper option in our database at $0.0220 per 1M blended — about 87% less than DeepSeek V4-Flash. It is also open-weight, so self-hosting is an option.
Can I run DeepSeek V4-Flash locally?
Yes. DeepSeek V4-Flash is open-weight and needs about ~140 GB of VRAM at 4-bit quantisation (2× H100 80GB (4-bit)).
Why does DeepSeek V4-Flash 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 V4-Flash charges 2× 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 Datenbank für KI-Modelle oder das LLM-Leaderboard.
