Kimi-VL A3B on live video streams

What Kimi-VL A3B is good at

Kimi-VL A3B’s MoonViT vision encoder processes images at their native resolution instead of forcing them into fixed-size tiles, which preserves detail that fixed-tile encoders tend to lose. Combined with sparse mixture-of-experts routing that activates just 2.8B parameters per token, it delivers strong understanding quality for its serving cost.

The 128K context window makes it well suited to long video and agentic tasks that need to track many sampled frames or a long running conversation. Moonshot AI also offers a long-thinking variant for harder reasoning tasks that benefit from extended deliberation before answering.

• Native-resolution image encoding without fixed-tile detail loss
• Long-video and agentic tasks across a 128K-token context window
• High understanding quality per active parameter

Kimi-VL A3B and the Overshoot workflow

Kimi-VL A3B is not currently in Overshoot's live model catalog. When a model like this is available through the API, the workflow is the standard one: publish a camera or screen share over WebRTC to open a Stream, then send a chat-completions request whose video_url is an ovs:// reference to a recent segment, sampled at up to 1 fps by default, or point at the latest frame for a live read. The large context window leaves room to include many sampled frames alongside conversation history in a single request.

Because Kimi-VL A3B activates only a small fraction of its parameters per token, it is inexpensive to serve on any hosting stack. Overshoot's live models stream answers back over SSE at low time to first token, with thread_id reuse keeping prompt caching effective across a session. Check GET /v1beta/models for the current catalog.

Kimi-VL A3B in the small and edge category

Kimi-VL A3B competes with other efficient MoE vision models such as Gemma 4 26B-A4B, trading a larger total parameter count for a small active-parameter footprint and a native-resolution encoder rather than raw scale. For workloads that need long context and agentic reasoning at low serving cost, that tradeoff favors Kimi-VL A3B over larger dense flagships.

Frequently asked questions

Can Kimi-VL A3B analyze live video?

Kimi-VL A3B is built for long-video reasoning, but it is not currently in Overshoot's live model catalog, so it cannot be queried against an Overshoot stream today. The catalog changes over time; check GET /v1beta/models for the current list of hosted and passthrough models.

Is Kimi-VL A3B open source?

Yes. Kimi-VL A3B is released under the MIT license, which permits unrestricted commercial use, self-hosting, and fine-tuning of the weights.

Is Kimi-VL A3B available on Overshoot?

Not currently. Kimi-VL A3B is not in the live model catalog at the moment; the catalog changes over time, so check GET /v1beta/models. Its sparse routing, activating 2.8B of 16B parameters per token, keeps latency low at longer context lengths on any serving stack.

When should I use the long-thinking variant of Kimi-VL A3B?

The long-thinking variant trades some latency for extended deliberation before answering, which helps on harder reasoning tasks. For straightforward live-video queries where speed matters most, the standard variant is the better default.

Related models

• Gemma 4 26B-A4B
• Qwen2.5-VL 7B
• SmolVLM2 2.2B
• DeepSeek-VL2
• Holo 3.1 35B A3B
• Holo3 35B A3B