Spark-X2.6-4B has landed: an open-source model with 1M context, three thinking budgets, and deployment-ready formats for local agents.@SparkLLM
🤖 https://modelscope.ai/collections/XHToken/Spark-X26

🏆 Stronger at 4B: In the team’s reported comparison table, it reaches a 63.3 average, ahead of Spark-X2.5-4B at 52.1 and Qwen3.5-9B at 49.1 across the evaluated suite.
🧠 Set the thinking budget: Switch between none, low, and high thinking modes to balance reasoning quality, latency, and cost for each task.
⚡ Built for efficient local inference: A hybrid attention design combines 1M context with resource-conscious deployment, while FP8, INT8, INT4, and GGUF releases offer practical precision and footprint tradeoffs.
🛠️ One model, multiple workflows: Use the Instruct checkpoint for everyday agent work or the Base model for continued training and customization. Supports Transformers, SGLang, vLLM, llama.cpp, Ollama, MLX, and LM Studio.
📜 Apache 2.0.