Unquant

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Unquant

Uncensored AI for coding and agents

Refusals are not only a creative-writing problem. Security research, scraping, moderation pipelines and plenty of ordinary code get blocked by over-cautious models. An uncensored model with a big window and no meter removes both bottlenecks.

Use case2 min readUpdated 1 October 2026

Where safety-tuned models get in the way

  • Security workexploit analysis on your own systems, malware triage, CTFs and pentest reports.
  • Content pipelinesclassifying, summarising or moderating material that is itself explicit or violent.
  • Reverse engineering and scrapinglegitimate tasks that pattern-match to "misuse".
  • Agentsone refusal in step 37 of an automated run breaks the whole run.

Why GLM-5.3-Flash

  • Frontier-class reasoningfor complex coding and long-running agents.
  • Tool callingin the standard OpenAI format.
  • Adjustable reasoningreasoning_effort from none to max, per request.
  • 262,144-token context on every planwhole-repository context and long tool histories fit.
  • Visionscreenshots, diagrams and UI mock-ups as input.

Why unlimited matters for agents

Agents are the most token-hungry workload there is: every step re-sends the task, files and tool results. A day of agent work can consume hundreds of millions of tokens (see the maths). On a flat plan that costs nothing extra, so you can let the agent read the whole codebase instead of rationing context.

Get started

Setup guides: Claude Code · Codex CLI · OpenCode · VS Code · Cursor · Python and JavaScript. Model details: GLM-5.3-Flash, from $109/month.

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