DeepSeek V4-Flash — Specifications
| Developer | DeepSeek |
|---|---|
| Type | LLM (MoE) |
| Modality | Text → Text |
| Parameters | 284B total / ~13B active (MoE) |
| Context window | 1M |
| Max output | 384K |
| License | MIT (open) |
| Open weights | Yes |
| Released | 2026-04 |
| Input price | $0.14 /1M |
| Output price | $0.28 /1M |
| API providers | DeepSeek, OpenRouter |
Run it locally
| VRAM (4-bit) | ~140 GB |
|---|---|
| Minimum GPU | 2× H100 80GB (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
| Input (per 1M tokens) | $0.140 |
|---|---|
| Output (per 1M tokens) | $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 / month | Cost / month |
|---|---|---|
| Side project | 1M in / 0.25M out | $0.21 |
| Small team | 20M in / 5M out | $4.20 |
| Production | 200M in / 50M out | $42 |
Run your own numbers in the AI API cost calculator.
Cheaper alternatives to DeepSeek V4-Flash
| Model | Blended $/1M | You save |
|---|---|---|
| Mistral 7B open | $0.0220 | 87% cheaper |
| Llama 3.1 8B open | $0.0220 | 87% cheaper |
| Mistral NeMo 12B open | $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 self-hosting vs API calculator works out the break-even point for your token volume.
Frequently asked questions
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 AI models database or the LLM leaderboard.
