Features · Chat
A chat that makes things.
Not just answers — assets. Describe what you need and the assistant generates images, video, voiceovers, music, and sound effects with the models you've chosen, then crops, trims, and converts them in follow-ups. Every result lands in your project folder as a real file.
Choose
One chat model, a full crew behind it.
Pick the model that runs the conversation — a frontier cloud model through Runware or a local one through Ollama — and it writes the replies too. Behind it, each kind of media has its own slot: your image model, your video model, your voices. The assistant calls whichever the request needs.
Generate
Ask in plain language, get the asset.
Describe the shot mid-conversation and the assistant routes it to your image model — no switching workspaces, no re-typing the prompt. The result appears inline and is saved to your project folder at the same moment.The image in the thread is a real, unretouched Flux 2 Pro generation from CSuite — the same sample shown on its catalog page.
Iterate
Follow-ups that actually follow up.
“Crop it to 1:1.” “Make it matte black.” The assistant knows “it” means the last image in the thread: simple edits dispatch straight to the local edit engine with no model round-trip, and creative revisions run image-to-image on the file it just made. Up to six chained tool steps per message — generate, crop, convert, done.
Multimodal
Every modality at the table.
One thread can carry a whole deliverable: a voiceover from your speech model, a music bed, a sound effect, a video clip that animates the image two messages up. Speech, music, and sound effects each route to their own model slot, so asking for a voiceover never runs your music model.
Catalog
Available models & providers.
Models you can chat with — hosts that can execute the assistant's tools: cloud models through Runware with your own key, and local models through Ollama, free and offline. (Replicate-only models stay available in the Text workspace.)
RunwareCloud · your API key
OllamaLocal · your hardware
GPT 5.6 Luna
CloudOpenAI's fastest, most affordable GPT-5.6 tier — for high-volume, latency-sensitive, and budget-conscious workloads.
Replicate · Runware
GPT 5.6 Terra
CloudOpenAI's balanced GPT-5.6 tier — GPT-5.5-level quality at roughly half the cost, for production workloads.
Replicate · Runware
GPT 5.6 Sol
CloudOpenAI's flagship GPT-5.6 tier — leads the family on every benchmark, for frontier coding, long-horizon agentic work, and research.
Replicate · Runware
Claude Fable 5
CloudAnthropic's Claude 5-family model for the most demanding reasoning, coding, and agentic work — with vision and a 128K output budget.
Replicate · Runware
Claude Opus 4.8
CloudAnthropic's most capable model — frontier reasoning, coding, and agentic tool use with vision.
Runware
Claude Sonnet 4.6
CloudAnthropic's balanced model for high-quality coding, reasoning, and agentic tool use — with a 1M-token context (beta) and vision.
Replicate · Runware
Claude Haiku 4.5
CloudAnthropic's fast, cost-efficient model with strong reasoning, coding, and tool use at low latency.
Replicate · Runware
Grok 4.3
CloudxAI's frontier Grok model with strong reasoning, coding, and agentic tool use, plus vision and real-time knowledge from X.
Runware
Gemini 3 Flash
CloudFast, cost-efficient Gemini model for high-throughput text and multimodal tasks.
Replicate · Runware
V4 Flash
CloudDeepSeek's fast, cost-efficient V4 mixture-of-experts model (MIT) with long context and native tool calling.
Runware
Gemma 4 E2B
LocalGoogle's efficient 2B multimodal model supporting text, image, and audio input.
Ollama
Gemma 4 E4B
LocalGoogle's efficient 4B multimodal model — stronger reasoning than E2B with text, image, and audio.
Ollama
Gemma 4 12B
LocalGoogle's 12B multimodal Gemma 4 model with strong text and image understanding.
Ollama
Gemma 4 26B (MoE)
LocalGoogle's 26B mixture-of-experts model with long context and strong multimodal reasoning.
Ollama
Gemma 4 31B
LocalGoogle's flagship 31B dense model with 256K context and top-tier text and image understanding.
Ollama
Gemma 3 Med 4B
LocalGoogle's medical multimodal model fine-tuned for healthcare and biomedical tasks.
Ollama
Gemma 3 Med 27B
LocalLarge medical variant for complex clinical reasoning and biomedical analysis.
Ollama
Mistral Small 4
LocalMistral AI's unified small model merging reasoning, vision, and agentic coding, with configurable reasoning effort and tool use.
Ollama
Ministral 3 14B
LocalMistral AI's 14B dense edge model (Apache 2.0) tuned for fast on-device reasoning and tool use.
Ollama
Ministral 3 8B
LocalMistral AI's efficient 8B dense edge model (Apache 2.0) for on-device chat, reasoning, and tool use.
Ollama
Ministral 3 3B
LocalMistral AI's compact 3B dense edge model (Apache 2.0) for fast on-device text generation and tool use.
Ollama
GPT-OSS 20B
LocalOpenAI's open-weight 20B reasoning model (Apache 2.0) with configurable reasoning effort and native tool calling — runs on 16 GB.
Ollama
Phi-4 14B
LocalMicrosoft's 14B state-of-the-art open model with strong reasoning, math, and code.
Ollama
Phi-4 Mini 3.8B
LocalMicrosoft's compact 3.8B model with a 128K context and native function calling.
Ollama
Phi-4 Reasoning 14B
LocalMicrosoft's 14B reasoning model fine-tuned for chain-of-thought on math, science, and code.
Ollama
OLMo 3.1 32B
LocalAi2's fully open 32B model — open weights, data, and training recipe — with long chain-of-thought reasoning.
Ollama
OLMo 3 7B
LocalAi2's fully open 7B model with open weights, training data, and recipe.
Ollama
OLMo 3 32B
LocalAi2's fully open 32B model with open weights, training data, and recipe.
Ollama
Laguna XS 2.1 (Q4)
LocalPoolside's 33B (3B active) MoE model for agentic coding and long-horizon local software work, with native reasoning and tool use.
Ollama
Laguna XS 2.1 (Q8)
LocalPoolside's 33B (3B active) MoE agentic-coding model — Q8 quantization for higher fidelity.
Ollama
Laguna XS 2.1 (BF16)
LocalPoolside's 33B (3B active) MoE agentic-coding model — full-precision BF16 weights.
Ollama
Qwen3 0.6B
LocalAlibaba's ultra-compact 0.6B Qwen3 model — runs in-process via HuggingFace or through Ollama, with tool use.
Ollama · Hugging Face
Llama 3.2 1B
LocalMeta's 1B instruction-tuned model — the smallest Llama, runnable in-process via HuggingFace or through Ollama.
Ollama · Hugging Face
Gemma 3 1B
LocalGoogle's compact 1B Gemma 3 model — runs in-process via HuggingFace or through Ollama.
Ollama · Hugging Face
LFM2 24B
LocalLiquid AI's 24B foundation model with efficient long-context reasoning.
Ollama
Llama 3.1 8B
LocalMeta's 8B instruction-tuned model with 128K context and strong general reasoning.
Ollama
Llama 3.2 3B
LocalMeta's compact 3B instruction-tuned model — fast on-device text generation.
Ollama · Hugging Face
Granite 4.1 3B
LocalIBM's compact 3B instruction-tuned model with multilingual support, tool use, and structured JSON output.
Ollama
Granite 4.1 8B
LocalIBM's 8B dense decoder-only foundation model for general instruction-following, RAG, and code tasks.
Ollama
Granite 4.1 30B
LocalIBM's flagship 30B dense foundation model — strong reasoning across business, coding, and multilingual tasks.
Ollama
Nemotron 3
LocalNVIDIA's multimodal Nemotron mixture-of-experts model unifying text and image understanding for enterprise Q&A, summarization, and document intelligence.
Ollama
Qwen3.6 27B
LocalAlibaba's 27B multimodal Qwen 3.6 model with a 256K context and strong vision-language reasoning.
Ollama
Qwen3.6 35B
LocalAlibaba's flagship 35B multimodal Qwen 3.6 model for top-tier vision-language reasoning.
Ollama
Qwen3.5 2B
LocalAlibaba's compact 2B multimodal Qwen model with vision input and a 256K context — fast on-device text+image reasoning.
Ollama
Qwen3.5 4B
LocalAlibaba's efficient 4B multimodal Qwen model with vision input and strong multilingual reasoning.
Ollama
Qwen3.5 9B
LocalAlibaba's 9B multimodal Qwen model balancing vision-language reasoning with on-device performance.
Ollama
LFM2.5 8B
LocalLiquid AI's efficient 8B Liquid Foundation Model tuned for long-context reasoning on-device.
Ollama
North Mini Code 1 (Q4)
LocalCohere's compact code-specialized model tuned for fast local code generation, completion, and refactoring.
Ollama
Ornith 9B
LocalDeep Reinforce's 9B self-improving agentic-coding model (MIT) with 256K context and native tool use.
Ollama
Ornith 35B
LocalDeep Reinforce's flagship 35B self-improving agentic-coding model (MIT) with 256K context — state-of-the-art open-source coding for its size.
Ollama
R1 1.5B
LocalDeepSeek's compact 1.5B reasoning model distilled from Qwen2.5 — chain-of-thought on-device.
Ollama · Hugging Face
R1 8B
LocalDeepSeek's 8B reasoning model distilled from Qwen3 (R1-0528) with strong math, code, and logic.
Ollama
R1 14B
LocalDeepSeek's 14B reasoning model distilled from Qwen2.5 with exceptional math and coding benchmarks.
Ollama
R1 32B
LocalDeepSeek's 32B reasoning model distilled from Qwen2.5 for top-tier chain-of-thought reasoning.
Ollama
Qwen3 4B
LocalAlibaba's compact 4B instruction model with strong reasoning and multilingual capabilities.
Ollama · Hugging Face
FAQ
Chat questions, answered.
How the multimodal chat works in CSuite — capable models, tools and edits, files, offline use, and costs.
Chat needs a host that can execute tools, so the picker offers cloud models through Runware (the GPT 5.6 family, Claude, Gemini, Grok, and more) and local models through Ollama (Gemma, Qwen, Llama, and other open-weight models). Replicate-hosted models can't run chat tools and aren't selectable here — they remain available in the Text workspace.
Beyond conversation, it generates media with the models you've assigned per slot — images (including image-to-image updates of the last image), video clips (using up to two prior images as start and end frames), text-to-speech, music, and sound effects — and performs edits: crop or resize images, trim or convert audio, and crop, resize, or transcode video.
Edits target the most recent file of that kind in the conversation, so "crop it to 1:1" just works. Obvious requests are dispatched directly to the local edit engine without a model round-trip, and the assistant can chain up to six tool steps in one turn — generate, then crop, then convert, from a single message.
Into your project folder, as regular files — the thumbnails and players in the thread point at those files, not at copies trapped in a chat log. Anything the assistant makes is immediately usable in the Image, Audio, and Video workspaces, or in any app on your machine.
Yes — pick a local Ollama model and the conversation, tool-calling, and deterministic edits all run on your machine. Generating media in the same thread uses whatever models you've assigned to those slots: local ones stay offline, cloud ones need their platform key.
The chat model itself — it doubles as the text model, so there's no separate text slot to configure. Image, video, speech, music, and sound-effect generation each have their own model slot under "More models", and if a request needs a slot you haven't configured, the assistant pauses and opens the picker at exactly that slot.
Cloud chat models are billed per token by Runware directly to your account, and any media the assistant generates is billed by the model that made it — with no CSuite markup or metering. Local models cost nothing to run. The app's analytics show spend per provider.
Locally, in the app's database on your machine — conversations, messages, and attachment references never touch our servers. Delete a conversation and it's gone from your disk, not archived in a cloud.