GLM 5.3 Flash Abliterated

orca/orcacyber-glm-5.3-flash
NewFeaturedZDR
VisionToolsJSONReasoning
by OrcaCyber · 2026-10-27

OrcaCyber GLM 5.3 Flash Abliterated is the fast, cost-efficient tier of the OrcaCyber line, a cybersecurity model purpose-built for authorized security research and security engineering. It covers the full offensive and defensive workflow: vulnerability research and reproduction, exploit development and proof-of-concept construction, penetration testing and red-team tooling, security auditing and secure code review, malware and binary analysis, log and network-traffic triage, detection engineering, and multi-step cyber reasoning. Refusal behavior is removed, so the model engages directly with offensive-security tasks that general-purpose assistants decline. It reports 67.9 on CyberBench for security-specific tasks, 92.0 on SWE-bench and 80.5 on LiveCodeBench for agentic coding, 84.3 on Terminal-Bench 2.1 for terminal-driven agent work, and 91.2 on GPQA Diamond for scientific reasoning. It is natively multimodal, accepting text, image, and video input, so screenshots of interfaces and error states, network and architecture diagrams, dashboards, and recorded sessions can be reasoned over alongside source code and long textual context. A 1M-token context window lets whole repositories, large log sets, packet captures, or disassembly listings sit in a single call without a retrieval pipeline, while an output ceiling of up to 131,072 tokens supports long single-pass artifacts — complete audit reports, annotated exploit chains, or extended patch diffs. A hybrid sparse and linear attention design holds accurate long-context behavior while keeping compute low. Reasoning is always on, with selectable effort (low / high / max); native function calling and structured JSON output make it well suited to agentic security tooling that plans, invokes tools, and returns machine-parseable results. As the Flash tier it is tuned for high-throughput, long-context work where cost per token matters — large-scale vulnerability triage, bulk code and config auditing, and agent loops that carry a lot of state — rather than one-shot hardest-reasoning tasks, for which the heavier OrcaCyber tiers remain the better fit. Served through an OpenAI-compatible API and access-gated to the Security Research tier for trusted security researchers, red teams, and authorized security testing.

ctx1M tokens
Max output131K
Inputtext + image + video
Outputtext
p50 TTFT750 ms
Data retentionZero Data Retention available. With ZDR on, requests go only to providers with a zero-retention agreement.
PRICE$0.0000/ per request
p50 TTFT750 ms7d
p95 TTFT1.89 s7d
TRAFFIC28.3Ktokens / 7d
Get the GLM 5.3 Flash Abliterated API →🔒 Enable access

Offensive-capability model · an engagement, a passkey and the terms

Pricing

Pricing
Per request$0.0000
CurrencyUSD
Flat fee per API call (image generation models)

Performance

p50 TTFT
750 ms
Output speed
170 tok/s
p95 TTFT
1.89 s
Error rate
0%

Public benchmarks

CyberBench
67.9
GPQA Diamond
91.2
LiveCodeBench
80.5
SWE-bench
92.0
Terminal-Bench 2.1
84.3
Source: orca

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FAQ

How much does OrcaCyber: GLM 5.3 Flash Abliterated cost on OrcaRouter?
OrcaCyber: GLM 5.3 Flash Abliterated is priced at $0.0000 per request via OrcaRouter (flat per-call fee, charged per generation rather than per token).
What is OrcaCyber: GLM 5.3 Flash Abliterated's context window?
OrcaCyber: GLM 5.3 Flash Abliterated supports a context window of 1M tokens. Use long-context features (RAG, summarisation) up to that limit.
How do I call OrcaCyber: GLM 5.3 Flash Abliterated via the OpenAI SDK?
Set OpenAI base_url to https://api.orcarouter.ai/v1, supply your OrcaRouter API key, and pass model="orca/orcacyber-glm-5.3-flash" in the chat.completions.create call.
Does OrcaRouter rate-limit OrcaCyber: GLM 5.3 Flash Abliterated?
Per-model rate limits follow your OrcaRouter plan. Free tiers ship with conservative caps; paid tiers lift them. Check /pricing for current quotas.

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Model card as data

GET /api/public/models/orca/orcacyber-glm-5.3-flashOpen
Machine-readable:/llms.txt/llms-full.txt