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New accounts get trial credit — enough to run a full eval suite

On our own GPUs

DeepSeek V4 Pro API

1.6 trillion parameters, thinking and non-thinking modes, MIT licensed.

  • Context

    1M

  • Max output

    384K

  • Serving precision

    BF16

  • Licence

    MIT

DeepSeek V4 Pro is DeepSeek’s open-weight flagship, released on 24 April 2026. A 1.6T-parameter mixture-of-experts with 49B active per token, a 1-million-token context window and up to 384K output tokens. Like its sibling V4 Flash it ships dual thinking / non-thinking modes, and both are released under the MIT licence. It is currently the strongest reasoning tier available in the open-weight camp — the model to hand your hardest work to.

Open weights, deployed in facilities we build out and lease directly, operated by us. We resell nobody else’s account quota.

Specifications

Model ID
deepseek-v4-pro
Published by
DeepSeek
How it is served
On our own GPUs
Licence
MIT
Total parameters
1.6T MoE
Active parameters
49B
Context
1M
Max output
384K
Serving precision
BF16
Input modalities
Text
Released
2026-04-24

What it is good at

Reasoning only when the task earns it
The same model answers fast in non-thinking mode and runs long chains when you switch modes on. You pay for depth on the requests that need it instead of on all of them.
384K tokens of output
Combined with the 1M window, read-a-book-write-a-book workloads finish in a single call — no manual segmentation, no state to carry between requests.
The strongest open reasoning under MIT
This capability tier under an essentially unrestricted licence is one of the best deals in self-hosting. We run it on our own cluster with the weight version and precision published.

Where it fits

  • Mathematics, algorithms and hard multi-step logic
  • Defect localisation and cross-file refactors in large codebases
  • Reports, translations and generation that need very long output
  • The escalation tier behind a cheaper default model

Three steps

Change one line of base_url, put the model ID below in the `model` field, and leave the rest of your code alone.

model: "deepseek-v4-pro"

curl https://api.nexaix.net/v1/chat/completions \
  -H "Authorization: Bearer $NEXAIX_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek-v4-pro",
    "stream": true,
    "messages": [
      { "role": "user", "content": "Summarise this contract." }
    ]
  }'

About this model

V4 Pro or V4 Flash?

Pro is 1.6T with 49B active — the top reasoning tier, for hard work and escalation. Flash is 284B with 13B active at a much lower unit cost, for bulk production traffic. The usual pattern is Flash by default with Pro behind a difficulty check you control.

Does thinking mode cost extra?

Thinking mode emits chain-of-thought tokens, which are billed as output. See the pricing page for rates. Turn it on only where long-chain reasoning actually pays for itself.

Does DeepSeek V4 accept images?

No. Both shipped variants are text-only mixture-of-experts language models with no vision or audio capability. For multimodal work use Kimi K3 or MiniMax M3.

Also worth a look

Run your own eval on it

Take the trial credit, run the evals you already have against this model, and then decide whether it gets your production traffic.