Leistungsstarke zweisprachige GLM-Modelle mit modifizierten Gewichten für offenes Schließen und Informationsverarbeitung auf MuAPI.
GLM 5.3 Flash with refusal behavior reduced through abliteration: fast reasoning, a 1M-token context window, and optional image input.
The full-size GLM 5.3 with refusal behavior reduced through abliteration: a 1M-token context window, text in and text out.
GLM 4.6 (v5) with its refusal behavior reduced through derestriction: a 131K-token context window, text in and text out.
This page covers GLM 5.3 Flash Abliterated, GLM 5.3 Abliterated and GLM 4.6 Derestricted v5 on Muapi. GLM 5.3 Flash with refusal behavior reduced through abliteration: fast reasoning, a 1M-token context window, and optional image input. The full-size GLM 5.3 with refusal behavior reduced through abliteration: a 1M-token context window, text in and text out. GLM 4.6 (v5) with its refusal behavior reduced through derestriction: a 131K-token context window, text in and text out.
Senden Sie einen Prompt, optional mit einem System-Prompt zur Vorgabe von Rolle und Tonfall sowie einer Bild-URL bei vision-fähigen Modellen, und fragen Sie den generierten Text ab. Typische Einsatzbereiche sind kreatives Schreiben, Red-Teaming- und Sicherheitsforschung sowie Szenarien, in denen Standard-Verweigerungen legitime Arbeitsabläufe behindern. Abliterated Modelle sind Sprachmodelle mit reduzierter Verweigerungsneigung; sie garantieren keine vollkommen unbeschränkte Ausgabe und heben die Nutzungsrichtlinien von MuAPI nicht auf. Sie tragen die Verantwortung für die Verwendung der generierten Ergebnisse.
Every endpoint accepts the same request body and returns text through the shared result endpoint, so switching models is a one-line change.
Abliteration and related techniques lower how often the model declines a request. Behavior varies by model and prompt, and is not a guarantee.
Set a system_prompt to steer persona, tone, and output format for creative, analytical, or evaluation work.
Billing follows the token counts each call returns. There is no subscription and no model hosting to manage.
Pass an image_url to GLM 5.3 Flash Abliterated.
Reduced refusals do not mean no rules. Muapi's acceptable-use policy and applicable law still apply to what you generate.
| Endpoint | Input | Output | Price | Best For |
|---|---|---|---|---|
| glm-5-3-flash-abliterated | Prompt + system_prompt, max_tokens, optional image_url | Complete text response (poll for result) | $0.40/M in · $1.60/M out | Fast, low-cost reasoning with image input and very long context |
| glm-5-3-abliterated | Prompt + system_prompt, max_tokens | Complete text response (poll for result) | $2.50/M in · $4.50/M out | Full-size reasoning over very long context |
| glm-4-6-derestricted-v5 | Prompt + system_prompt, max_tokens | Complete text response (poll for result) | $0.80/M in · $3.00/M out | Mid-size GLM reasoning with a 131K-token context |
Billed per token from the usage each call returns; prices are per million input and output tokens.
Submit a JSON request with your Muapi API key, save the request ID, and poll the shared result endpoint.
curl -X POST https://api.muapi.ai/api/v1/glm-5-3-flash-abliterated \
-H "x-api-key: YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"prompt": "Analyze this chart and write a blunt executive summary of the risks.", "system_prompt": "You are a candid, detail-oriented assistant.", "image_url": "https://example.com/reference.png", "max_tokens": 1024}'
# Response: {"request_id":"REQUEST_ID"}Call glm-5-3-flash-abliterated with a prompt and optional system_prompt, image_url.
curl -X POST https://api.muapi.ai/api/v1/glm-5-3-abliterated \
-H "x-api-key: YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"prompt": "Summarize this 500-page report and flag every unsupported claim.", "system_prompt": "You are a candid, detail-oriented assistant.", "max_tokens": 1024}'
# Response: {"request_id":"REQUEST_ID"}Call glm-5-3-abliterated with a prompt and optional system_prompt.
curl -X POST https://api.muapi.ai/api/v1/glm-4-6-derestricted-v5 \
-H "x-api-key: YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"prompt": "Draft a frank critique of this business plan.", "system_prompt": "You are a candid, detail-oriented assistant.", "max_tokens": 1024}'
# Response: {"request_id":"REQUEST_ID"}Call glm-4-6-derestricted-v5 with a prompt and optional system_prompt.
curl https://api.muapi.ai/api/v1/predictions/REQUEST_ID/result \ -H "x-api-key: YOUR_API_KEY" # Poll until status is completed, then read the generated text from outputs.
Poll the shared result endpoint until the task is completed, then read the generated text from outputs.
An abliterated language model is a model whose refusal behavior has been reduced by editing the internal direction that drives refusals, usually without retraining from scratch. Related terms such as obliterated and derestricted describe similar techniques. The result tends to decline fewer requests than the original model, but it is not a guarantee and quality can differ from the base model.
GLM 5.3 Flash Abliterated, GLM 5.3 Abliterated and GLM 4.6 Derestricted v5. Each is a separate endpoint with its own model name in the URL.
Billing is per token, deducted after each call from the counts the model returns. Rates start at $0.40 per million input tokens; see the pricing table for every model.
No. Reduced refusals do not mean unrestricted output, and each model's knowledge and quality still bound the answer. You are responsible for how you use the output, and requests that violate Muapi's acceptable-use policy or the law are not permitted.
Yes on GLM 5.3 Flash Abliterated: add an image_url next to your prompt. The others are text-only.
POST to /api/v1/<model-name> returns a request_id. Poll /api/v1/predictions/{request_id}/result until the status is completed, then read the text from outputs.
Ja. Alle 3 Modelle hier unterstützen Tool-Calling: GLM 5.3 Flash Abliterated, GLM 5.3 Abliterated und GLM 4.6 Derestricted v5. Setzen Sie in Claude Code ANTHROPIC_BASE_URL auf https://api.muapi.ai/anthropic und ANTHROPIC_AUTH_TOKEN auf Ihren Muapi-API-Schlüssel und starten Sie es dann mit claude --model glm-5-3-flash-abliterated (setzen Sie außerdem ANTHROPIC_DEFAULT_HAIKU_MODEL und ANTHROPIC_SMALL_FAST_MODEL auf dieselbe ID). Codex CLI funktioniert ebenfalls: Fügen Sie einen Modellanbieter mit base_url https://api.muapi.ai/openai/v1 hinzu und setzen Sie model auf dieselbe ID. Die Qualität variiert je nach Modell, und kleinere Modelle können bei Aufgaben mit mehreren Dateien Schritte auslassen; es gibt kein Prompt-Caching, daher wird jede Runde für die gesamte bisherige Unterhaltung berechnet, und jede Antwort ist auf 16,384 Tokens begrenzt. Einrichtungsanleitung: /de/docs/ai-agent-install-chat-agents.
Create one Muapi API key and call every model on this page with a plain REST request.