# ModelScope releases open-source Spark-X2.6-4B with 1M context for local agents

> Original title: Spark-X2.6-4B has landed: an open-source model with 1M context, three thinking budgets, and deployment-ready formats for local agents.@Sp...

- Source: ModelScope
- Published: 2026-10-21T05:40:55.000Z
- Bad Influence: https://badinfluence.news/items/ec90k0u5geofsuc6h7yfemrjh
- Original: https://x.com/ModelScope2022/status/2112781153065165291

## Summary

ModelScope announces Spark-X2.6-4B, an open-source model with 1M context and three selectable thinking modes: none, low, and high. The team says it scores 63.3 on average in its reported comparison, ahead of Spark-X2.5-4B at 52.1 and Qwen3.5-9B at 49.1. It ships in FP8, INT8, INT4, and GGUF formats and supports Transformers, SGLang, vLLM, llama.cpp, Ollama, MLX, and LM Studio, under Apache 2.0.

## Post

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.

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