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[ARTICLE · art-145378] src=refuseless.com ↗ pub= topic=ai-safety verified=true sentiment=· neutral

Show HN: Abliterated LLM provider for cyber tasks

Refuseless launched an abliterated version of GLM 5.3, an open model with refusals removed, served through OpenAI-compatible endpoints with zero prompt retention for cybersecurity teams. The provider reports the model scores 66.9 on DeepSWE agentic coding, 84.5 on CyberGym cybersecurity, and solves 105 of 130 exploits on ExploitGym, and offers $5 in free credits with credit card and cryptocurrency accepted. The lineup includes glm-5.3-abliterated and glm-5.3-flash-abliterated, both with a 1,048,576-token context window and 16,384-token output limit, accessible via the base URL https://api.refuseless.com/v1.

read1 min views1 publishedOct 5, 2026
Show HN: Abliterated LLM provider for cyber tasks
Image: source

Frontier coding and cybersecurity

GLM 5.3 Abliterated

The most powerful open model for cyber tasks to date, with refusals removed.

  • DeepSWE66.9* agentic coding*
  • CyberGym84.5* cybersecurity*
  • ExploitGym105/130* exploits solved*

PROMO$5 FREE CREDITS

Abliterated models served via OpenAI-compatible endpoints, zero prompt retention.

Credit card & cryptocurrency accepted

for cybersecurity teams and professionals

Cost-effective cyber intelligence

Very powerful model competing with frontier at much lower cost.

OpenCode and Pi both speak this API.

{
  "$schema": "https://opencode.ai/config.json",
  "model": "refuseless/glm-5.3-abliterated",
  "provider": {
    "refuseless": {
        "npm": "@ai-sdk/openai-compatible",
        "name": "Refuseless",
        "options": {
          "baseURL": "https://api.refuseless.com/v1",
          "apiKey": "{env:REFUSELESS_API_KEY}"
        },
        "models": {
          "glm-5.3-abliterated": {
            "name": "GLM 5.3 Abliterated",
            "tools": true,
            "reasoning": true,
            "limit": {
              "context": 1048576,
              "output": 16384
            }
          },
          "glm-5.3-flash-abliterated": {
            "name": "GLM 5.3 Flash Abliterated",
            "tools": true,
            "reasoning": true,
            "limit": {
              "context": 1048576,
              "output": 16384
            }
          }
        }
      }
  }
}

export REFUSELESS_API_KEY="rk-…" on Mac, $env:REFUSELESS_API_KEY="rk-…" on Windows. The sample above reads it via {env:REFUSELESS_API_KEY}.~/.config/opencode/opencode.json, Windows: C:\Users\<you>\.config\opencode\opencode.json. Create it if missing. If it already has a "provider" object, add "refuseless" inside it — don't overwrite the rest.opencode --model refuseless/glm-5.3-abliterated, or /models inside the TUI and pick refuseless / glm-5.3-abliterated. Model glm-5.3-abliterated (context 1,048,576) — all 2 lineup models ship in the block, swap the id after refuseless/ to switch — send Say ok. to verify. Same request shape as OpenAI chat completions. Point the base URL at Refuseless, set a lineup id, send the key.

curl https://api.refuseless.com/v1/chat/completions \
  -H "Authorization: Bearer $REFUSELESS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "glm-5.3-refuseless",
    "messages": [{"role": "user", "content": "Write a tight system prompt."}]
  }'

Host and path are the intended contract. Auth is the API key.

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