Model

GLM-5.3 Flash Abliterated

Agentic all-rounder with strong terminal and tool-use scores.

Specs

Model id
glm-5.3-flash-abliterated
Context length
262,144 tokens
Max output
65,536 tokens (4,096 on trial accounts)
Input
Text only. Image, audio, video and file inputs are rejected.
Runs on
Dedicated GPUs operated by our infrastructure provider under a zero-data-retention configuration.
Precision
FP8 · Zero additional quantizationNative FP8 (block-FP8, e4m3): the precision Z.ai releases GLM-5.3 Flash in. Zero additional quantization: never compressed to INT8, INT4 or FP4.
Reasoning
Per request: reasoning_effort on Chat Completions, thinking on Messages, reasoning.effort on Responses.
Endpoints
/v1/chat/completions, /v1/messages, /v1/responses (stateless)
Base model
zai-org/GLM-5.3-Flash (MIT license)
Input
$0.0977
Cached input
$0.0098
Output
$0.3421

USD per 1M tokens, billed from prepaid credit.

Uptime, last 30 days

Status page

100%

15 of 15 canary checks passed (one every 5 minutes).

Last canary: passed, time to first token 495 ms, total 526 ms.

Speed, last 24 h

—

Speed is measured, not guaranteed: the median output tokens per second after the first token, and the median time to first token (TTFT), over streamed requests in the last 24 h. "—" until a model has 20 of them.

Benchmarks

Benchmarks: base model, vendor-reported unless noted (zai-org/GLM-5.3-Flash)

Terminal-Bench 2.1
84.3source
DeepSWE v1.1
63.4source
HLE (with tools)
55.3source
GPQA-Diamond
92.17NVIDIA BF16 run

Abliteration effect: MMLU (n=300) 83.3 → 82.7 (orcarouter build, reported by its publisher, source).

Quickstart

Create a key in the dashboard, then call glm-5.3-flash-abliterated from any OpenAI or Anthropic client.
curl https://api.abliteratedllm.dev/v1/chat/completions \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "glm-5.3-flash-abliterated",
    "stream": true,
    "messages": [{"role": "user", "content": "Write the opening line of a noir detective story."}]
  }'