Janus-Pro 7B
Janus-Pro 7B is DeepSeek's larger unified multimodal model for image understanding and text-to-image generation. It improves instruction following and generation stability while retaining separate visual encoding paths for understanding and generation.
Task Fit
Not marked for typed decisions in the current library.
Not marked for code agent in the current library.
Not marked for code in the current library.
General writing, Q&A, and assistant use.
Not marked for rag in the current library.
Image or visual understanding, not necessarily image generation.
Native text-to-image or image editing model.
Not marked for video generation in the current library.
Not marked for voice in the current library.
Evidence level
How this is assessed →Memory and hardware-fit values are planning estimates unless a tested report is linked.
Variants and Quant Artifacts
Choose the artifact first; hardware fit follows from RAM, VRAM, format, and runtime.
| Quant | Format | Quality | Min RAM | Reco RAM | Runtime | Action |
|---|---|---|---|---|---|---|
| BF16 | safetensors | high | 24GB | 32GB | transformers | Plan with this |
Recommended Hardware
Lowest estimated 5-year cost that can run this model.
Enough unified/system memory with a balanced 5-year cost.
Highest local performance signal among compatible hardware.
Benchmarks
No benchmark data is available for this model yet.
Sources and compatibility
Execution evidence
Run Janus-Pro 7B with a documented recipe
Recipes connect hardware, a model artifact, tools, settings, verification, and a reportable result.
No verified recipe is linked to this record yet.
Compatibility estimates remain available in the planner. A recipe appears here only after its exact stack and verification protocol are documented.
Similar Models
Janus-Pro 1B is DeepSeek's compact unified multimodal model for image understanding and text-to-image generation. Its decoupled visual encoding paths separate understanding from generation while sharing one transformer architecture.
DeepSeek V4.1 Flash is DeepSeek's MIT-licensed open-weight multimodal MoE for reasoning, coding, agents, and native image understanding. The complete checkpoint is approximately 763B parameters, including a 552B backbone; its Causal Encoder-Decoder architecture activates 8B parameters during input prefill and 16B during output decoding. CSA2 and FP4 KV caching reduce long-context cache cost. The model supports a 1M-token context window and is available through official FP8 weights, the DeepSeek API, and Ollama Cloud. It remains a data-center-class deployment despite the low active-parameter count.
DeepSeek V4.1 Flash 1017 is an updated MIT-licensed 763B-parameter multimodal MoE checkpoint. It keeps the V4.1 family's 1M-token context, coding, reasoning, tool use, and image understanding, while remaining a data-center-class deployment despite activating far fewer parameters per token.