Kimi-VL-Thinking-2506

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What Kimi-VL-Thinking-2506 is

[!Note] This is an improved version of Kimi-VL-A3B-Thinking. Please consider using this updated model instead of the previous version. [!Note] Please visit our tech blog for recommended inference recipe of this model: Kimi-VL-A3B-Thinking-2506: A Quick Navigation 1. Introduction This is an updated version of Kimi-VL-A3B-Thinking, with following improved abilities: - It Thinks Smarter while Consuming Less Tokens: The 2506 version reaches better accuracy on multimodal reasoning benchmarks: 56.9 on MathVision (+20.1), 80.1 on MathVista (+8.4), 46.3 on MMMU-Pro (+3.3), 64.0 on MMMU (+2.1), while in average requires 20\% reduced thinking length. - It Sees Clearer with Thinking: Unlike the previous version that specializes on thinking tasks, the 2506 version can also achieve the same or even better ability on general visual perception and understanding, e.g. MMBench-EN-v1.1 (84.4), MMStar (70.4), RealWorldQA (70.0), MMVet (78.4), surpassing or matching abilties of our non-thinking model (Kimi-VL-A3B-Instruct). - It Extends to Video Scenarios: The new 2506 version also improves on video reasoning and understanding benchmarks. It sets new state-of-the-art for open-source models on VideoMMMU (65.2), while also retains good ability on general video understanding (71.9 on Video-MME, matching Kimi-VL-A3B-Instruct). - It Extends to Higher Resolution: The new 2506 version supports 3.2 million total pixels in a single image, 4X compared to the previous version. This leads to non-trivial improvements on high-resolution perception and OS-agent grounding benchmarks: 83.2 on V Benchmark (without extra tools), 52.8 on ScreenSpot-Pro, 52.5 on OSWorld-G (full set with refusal). 2. Performance Comparison with efficient models and two previous versions of Kimi-VL (Results of GPT-4o is for reference here, and shown in <iitalics</i): Comparison with 30B-70B open-source models: Text results, comparison with 30B-level non-thinking VLMs: 3. Usage 3.1. Inference with VLLM (recommended) As a long-decode model that will generates up to 32K tokens, we recommend using VLLM for inference, which has already supported Kimi-VL series. [!Note] It is important to explicitly install flash-attn to avoid

Summarised from the published model card. Read the full card on the HuggingFace links below.

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Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys kimi-vl-thinking-2506 for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (kimi-vl-thinking-2506 below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/chat/completions \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"kimi-vl-thinking-2506","messages":[{"role":"user","content":"Hello"}]}'

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