Multimodal deployment shortlist
Explore open-weight multimodal model candidates for documents, images, and audio, with encoder-memory caveats and modality-specific testing guidance.
- What is included
- Six current candidates with multimodal or vision-language capabilities recorded in their official profiles. The exact supported inputs differ; the architecture label is not a modality compatibility matrix.
- How this list is ordered
- Alphabetical by model name. Inclusion is based on publisher-described capabilities, not a measured quality score.
Publisher sources are linked for every model. No independent performance scores have been measured. On smaller screens, scroll the table horizontally.
| Model | Architecture | Context | Deployment note |
|---|---|---|---|
| Dense · multimodal | 256K | The unified model covers text, image, and audio inputs.Compare model | |
| Dense · multimodal | 128K | Review the extra modality encoders when planning the full checkpoint budget.Compare model | |
| MoE · multimodal | 1M | A large multimodal candidate with additional modules beyond the backbone.Compare model | |
| MoE · vision | 1M | Test the intended image or video workload with its supported runtime.Compare model | |
| MoE · vision | 256K | A vision-language MoE; verify the image-processing and reasoning configuration.Compare model | |
| Dense · vision | 256K native | The language-model parameter count does not include all vision-related scope.Compare model |
Choose an input workload before choosing a winner
A document assistant, a photo question-answering service, and an audio application need different evaluations. Start with one supported modality and define what a correct response looks like. Keep any text-only baseline separate from results that include image or audio processing.
Record preprocessing alongside generation
Image resolution, page count, video sampling, and audio duration change the workload. Capture the processor configuration and actual generated input representation where available. The text context label alone does not describe how much media a deployment can process within its memory budget.
Inspect the entire model package
Encoders, projections, embeddings, and auxiliary modules can sit outside a headline parameter count. Use the source-linked profile notes to identify what still needs review, then measure peak memory for the actual inputs. This shortlist does not assign a minimum GPU or claim that all listed modalities work in every inference engine.
Before you choose hardware
KV cache and context length: why a model runs out of VRAM explains the next planning step. For the distinction between publisher specifications, calculations, and measurements, read the BenchGrid methodology.