Tasks and inputs
LongCat Next is listed for Any-to-any, Image-text-to-text, Text-to-image, Text-to-speech. It accepts text, image, audio and produces text, image, audio. Use the publisher’s input format and preprocessing for your chosen deployment.
LongCat Next is a native multimodal checkpoint that combines text, vision and audio understanding or generation in one framework.
The specification score summarizes recorded features and limits. It is not a benchmark result.
Recorded modality support shows what this model can accept and produce. An undocumented modality is shown as unknown rather than assumed to be unsupported.
Accepts
Produces
LongCat Next is a native multimodal checkpoint that combines text, vision and audio understanding or generation in one framework.
LongCat Next extends autoregressive prediction to text, visual and audio signals. It uses modality-specific tokenizer and detokenizer components and builds on a sparse LongCat backbone with about 3B active parameters. The complete checkpoint includes roughly 74B stored parameters.
The publisher provides examples for visual understanding, image output and audio conversation. These paths require the supplied processor and decoding components. A deployment that exposes only a text chat endpoint does not automatically provide all of the model’s generation modes.
Start with an image question that needs a visible detail, a text-to-image brief with several requested objects and a spoken exchange. Inspect each modality separately. In a conversation that changes modality, check whether the model preserves the intended subject and prior constraints instead of treating every turn as a new task.
The configuration sets a 131,072-position text limit. Use the companion repository’s environment and templates to reproduce the documented input structure, especially when decoding image or audio outputs. The weights and source are MIT-licensed. Plan memory for the full checkpoint, modality components and caches; an active parameter count describes computation and does not establish the total serving footprint or a hosted API price.
Compare the model on the work you need to complete.
LongCat Next is listed for Any-to-any, Image-text-to-text, Text-to-image, Text-to-speech. It accepts text, image, audio and produces text, image, audio. Use the publisher’s input format and preprocessing for your chosen deployment.
Documented limits: 131.07K context. Choose the output size and quality your design needs. Include drafts and revisions when budgeting an accepted image.
The weights are downloadable under MIT. Check runtime support and size the complete model and working context for your expected concurrency.
These records share a directory category. Check the source card, output type and deployment requirements before treating a model as a replacement.
google/gemma-4-E2B-it is a source-linked Hugging Face repository assigned to Any-to-any. Its recorded task interface accepts text, image, audio, video and produces text, image, audio, video.
google/gemma-4-E4B-it is a source-linked Hugging Face repository assigned to Any-to-any. Its recorded task interface accepts text, image, audio, video and produces text, image, audio, video.
lmstudio-community
lmstudio-community/gemma-4-E4B-it-MLX-4bit is a source-linked Hugging Face repository assigned to Any-to-any. Its recorded task interface accepts text, image, audio, video and produces text, image, audio, video.
Qwen
Qwen/Qwen2.5-Omni-7B is a source-linked Hugging Face repository assigned to Any-to-any. Its recorded task interface accepts text, image, audio, video and produces text, image, audio, video.
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