Spark-X2.6-4B review and Ollama coding setup

Spark-X2.6-4B is presented as a compact coding assistant with four billion parameters, adjustable reasoning budgets and a claimed one-million-token context window. The review compares reported benchmark scores with Qwen and Gemma models. Those figures are the speaker's cited results, rather than independently verified performance measurements.

The setup uses Ollama to download and run the model, with Google Antigravity as the presenter's coding environment. A Python script passes task details and documentation references from a JSON file to the model. Although the review discusses a much larger context capacity, this demonstration sets the context to 32,000 tokens and selects high reasoning.

The task concerns a scraper that retains downloaded articles in a global list and reportedly runs out of memory after about 100 pages. The presenter reviews a generated patch that limits the cache to 20 pages and uses a Requests session with a 10-second timeout. The pipeline also produces a JSON response, code differences and a browser report with a testing checklist. The transcript describes the proposed fix but does not show a completed memory test.

For readers exploring a local LLM, the practical cautions matter: the speaker says high reasoning can take several minutes even on basic questions, and whole-file rewrites can introduce formatting problems. The recommendation is to set thinking and token limits and edit files incrementally.