Janus Pro 7B

Image generationAvailable
by DeepSeekModel ID: janus-pro-7b

DeepSeek's unified multimodal model. Decouples vision encoding for both understanding and generation tasks.

Price
โ‰ˆ US$0.0169/run
Input โ†’ output
Text โ†’ Image
Developer
DeepSeek
Updated
23 September 2026
01

Playground

Try Janus Pro 7B

No input form

โ‰ˆ US$0.0169/run

No input form for this model yet

Its inputs are not documented yet. So that no run fails on a wrong input, we don't offer a form here. Pick a comparable model instead.

02

Examples

Real outputs from the public examples of this model on Replicate, with the prompt and settings that produced them. They were not generated live on this page.
  • InputInput

    Prompt

    Convert the formula into latex code.

    Response

    Here is the formula in LaTeX code: \[ A_n = a_0 \left[ 1 + \frac{3}{4} \sum_{k=1}^{n} \left( \frac{4}{9} \right)^k \right] \]

  • InputInput

    Prompt

    explain this meme

    Response

    This image is a humorous meme that plays on the similarity in appearance between two individuals, one labeled "OpenAI" and the other "DeepSeek." Here's a detailed breakdown: 1. **Top Image (OpenAI)**: - The person is holding a firearm and appears to be in a shooting stance. - The label "OpenAI" is placed over the individual, suggesting that they are being humorously compared to the AI company OpenAI. 2. **Bottom Image (DeepSeek)**: - The person is also holding a firearm and in a similar shooting stance. - The label "DeepSeek" is placed over the individual, implying a humorous comparison to DeepSeek, a company known for its work in artificial intelligence and machine learning. 3. **Humor and Meaning**: - The meme humorously compares the two individuals based on their similar appearance and actions. - It plays on the idea that both OpenAI and DeepSeek are involved in AI research and development, and the meme creator is using the visual similarity to create a joke. 4. **Context**: - The image is likely taken from a sporting event, as indicated by the background and the seating. - The meme is a lighthearted way to highlight the similarities between the two entities, with no serious intent. In summary, the meme is a playful comparison between two individuals, using their visual similarity and the context of holding firearms to create a humorous joke.

  • InputInput

    Prompt

    explain this meme

    Response

    This image is a humorous meme comparing two concepts: "Decoupling Visual Encoding" and "Single Visual Encoder." 1. **Left Side: Decoupling Visual Encoding** - The image shows a muscular, strong-looking dog, representing a robust and powerful concept. - This symbolizes "Decoupling Visual Encoding," which implies a more efficient or effective way of encoding visual information. The muscular dog suggests strength, efficiency, and robustness in handling visual data. 2. **Right Side: Single Visual Encoder** - The image shows a smaller, less muscular dog, looking tired or less confident. - This represents "Single Visual Encoder," which suggests a simpler or less powerful approach to encoding visual information. The smaller dog implies that this method might not be as effective or robust as the decoupled approach. ### Overall Meaning: The meme humorously contrasts two concepts in the field of computer vision or machine learning, where "Decoupling Visual Encoding" is depicted as a more powerful and efficient method, while "Single Visual Encoder" is portrayed as less effective or less robust. The use of the Doge meme characters adds a layer of humor and relatability to the comparison.

03

About Janus Pro 7B

TL;DRAs of 23 September 2026

Janus Pro 7B is a model by DeepSeek in the Image generation category. On Railwail, Janus Pro 7B costs โ‰ˆ US$0.0169 per run.

Background

About DeepSeek

Founded 2023 ยท Hangzhou, China

DeepSeek (ๆทฑๅบฆๆฑ‚็ดข) is a Chinese AI research lab founded in May 2023 in Hangzhou by Liang Wenfeng, the founder of the quant hedge-fund High-Flyer (which funds the lab). DeepSeek became globally prominent in late 2024 and early 2025 with the release of DeepSeek-V3 (Dec 2024), DeepSeek-R1 (Jan 2025) and the Janus multimodal family. The Janus series is DeepSeek's unified understanding-and-generation model: Janus (Oct 2024), JanusFlow (Nov 2024) and Janus Pro (Jan 2025) extend a single Transformer to both interpret images and generate them with a decoupled visual encoder design. All Janus models are open-sourced under a permissive license on Hugging Face and GitHub.

Visit DeepSeek

Architecture

Unified autoregressive multimodal Transformer (understanding + image generation)

Janus-Pro-7B (January 2025) is the largest member of the Janus family from DeepSeek. The model unifies multimodal understanding and image generation in a single autoregressive Transformer, but uniquely decouples the visual encoders for the two tasks: a SigLIP-style ViT encoder is used for image understanding (so visual features are semantic), while a VQ tokeniser based on LlamaGen is used for image generation (so visual features are reconstruction-oriented). Both feature paths are projected to the same 7B LLM backbone, which generates either text or image tokens depending on the task. For image generation Janus-Pro produces 384x384 images by autoregressive sampling of VQ tokens which are then decoded by the LlamaGen decoder. Janus-Pro improves over Janus by scaling data to ~90M image-text pairs and adding a second stage of supervised fine-tuning. DeepSeek reports that Janus-Pro-7B beats DALL-E 3, SD3-Medium and SDXL on GenEval and DPG-Bench, despite being a much smaller and unified model.

Parameters
7B parameters (Janus-Pro-7B)
Context
4,096 tokens

Capabilities

  • Unified image understanding + image generation in one 7B model
  • Open weights under permissive DeepSeek license
  • Outperforms DALL-E 3 and SD3-Medium on GenEval (per DeepSeek paper)
  • 384x384 native generation resolution
  • Compatible with Hugging Face Transformers and vLLM
  • Useful for multimodal agents that both see and draw
  • Strong instruction-following thanks to LLM-style backbone
  • Best for: research, multimodal agents, prototyping unified pipelines, fine-tuning.

Training & license

Pretrained on a mix of ~90M image-text pairs, text-only data and image-only data. Janus-Pro-7B adds extra supervised fine-tuning stages and a larger unified dataset compared to Janus 1B.

License: DeepSeek Janus License โ€” open weights, free for research and commercial use with attribution and standard restrictions.

Safety testing: DeepSeek applies content filters and reports safety evaluations in the Janus-Pro technical report, but the open weights ship without integrated safety classifier.

Known limitations

  • Only 384x384 native resolution โ€” needs upscaler for production
  • Image quality below dedicated diffusion models like FLUX 1.1 [pro]
  • Open weights have no built-in safety filter
  • Autoregressive sampling is slower per pixel than diffusion at high res
04

Pricing

Prices in US dollars. Usage is charged from prepaid credits.
Typical run (โ‰ˆ 14 s on L40S)US$0.0169 per run
GPU time (L40S)US$0.00117 per GPU second
  • Billed by the GPU time the run actually takes. When the run starts, 3ร— the typical price is reserved from your balance and settled afterwards.
  • 1 credit = US$0.01

Cost calculator

Price calculator

s

Typical according to the provider: about 14.4 s

Total

US$1.69

169 credits

Per run

US$0.0169 ยท 1.69 credits

Billed by the actual GPU time; this is an estimate.

05

API

Call Janus Pro 7B with your Railwail API key. Use this model ID in the request:

No verified API example

The inputs of this model are not documented yet.

06

Specifications

Model ID
janus-pro-7b
Developer
DeepSeek
Input
Text
Output
Image
Billing
By usage (tokens or GPU time)
Model size
7B parameters (Janus-Pro-7B)
License
DeepSeek Janus License โ€” open weights, free for research and commercial use with attribution and standard restrictions.
Catalog entry updated
23 September 2026

Tags

  • deepseek
  • janus
  • open-weights
  • unified-multimodal
  • pricing-tbd
07

Use cases

What it is used for

  • Multimodal research and prototyping
  • Unified vision-language agents that draw + describe
  • Interactive image dialogue systems
  • Fine-tuning base for specialised generators
  • Educational demos of unified multimodal models
  • Visual question answering with generation
08

Frequently asked questions

What is Janus Pro 7B?

Janus Pro 7B is a model by DeepSeek in the Image generation category.

How much does Janus Pro 7B cost on Railwail?

On Railwail, Janus Pro 7B costs โ‰ˆ US$0.0169 per run. You are charged for what each request actually uses. Usage is paid from prepaid credits; 1 credit equals US$0.01.

How fast is Janus Pro 7B?

There are not enough measured runs of Janus Pro 7B on Railwail yet to state a run time. It depends on the input, the settings and the load at the provider.

Is Janus Pro 7B better than FLUX 1.1 Pro?

That depends on the task. Janus Pro 7B (DeepSeek) and FLUX 1.1 Pro (Black Forest Labs) are both models in the Image generation category. The comparison page shows their prices and specifications side by side.

Compare Janus Pro 7B and FLUX 1.1 Pro
09

Comparable models

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    โ‰ˆ US$0.0094/run

    44 % cheaper per unit

    Compare Janus Pro 7B vs. Icons (SDXL Flat Pop)
  • InstantIDCommunity

    InstantID makes realistic portraits of a real person from a single reference photo without per-user training. Combines a face encoder with an IdentityNet adapter on SDXL to keep identity and pose while following a text prompt, so it is fast and tuning-free.

    โ‰ˆ US$0.084/run

    397 % more expensive per unit

    Compare Janus Pro 7B vs. InstantID
  • Sticker MakerCommunity

    fofr's sticker generator that outputs graphics with transparent backgrounds, so the result drops straight into chat apps or print sheets. Runs an SDXL-based pipeline at high speed (default 17 steps) and returns die-cut style art without manual background removal.

    โ‰ˆ US$0.0055/run

    67 % cheaper per unit

    Compare Janus Pro 7B vs. Sticker Maker

All models through one API

One API key for every model on Railwail. Usage is charged from prepaid credits, 1 credit = US$0.01.