LegitShow is the trusted source on every newly launched software product: what it does, who it’s for, how it actually holds up, and whether the AI engines are already reading it. Built to be what AI cites.

Web apps, SaaS, AI tools, MCP servers and developer tools. How we measure →


Legit.Show benchmarks every launched service it lists — measured deterministically from the public surface. See the methodology →

Cross-links · Directory · Reports · Insights · What AI reads · Methodology · About

Privacy · Terms · @Legit_Show on X · GitHub · operated by Madeflo Inc., a Delaware corporation. Benchmark engine powered by commit.show.

AI & Agents

Refuseless

Last updated 2026-10-05 · benchmark measured 2026-10-05 — deterministic & reproducible

OpenAI-compatible API serving abliterated LLMs with no prompt retention.

Refuseless at a glance

Refuseless offers an OpenAI-compatible API serving abliterated (refusal-removed) large language models, aimed at cybersecurity teams, penetration testers, coding professionals, security researchers, and AI agents, with zero prompt retention.

Good for

  • Penetration testing or security research tasks where standard LLMs might refuse to answer
  • Integrating an uncensored model into coding agents such as OpenCode or Pi via a standard OpenAI-style API
  • Workloads needing a very large context window (1M tokens) for cybersecurity or coding tasks

Pricing

GLM 5.3 Abliterated: $2 input/$0.35 cache/$3 output per 1M tokens. Flash variant: $0.25/$0.1/$0.8. $5 free credits promo. Card or crypto accepted.

Questions people ask

How is usage billed?
Usage is billed per million tokens, split into separate input, cache, and output rates.

What does 'abliterated' mean for these models?
The models have had their built-in refusals removed, so they respond to requests that other LLMs might decline.

What payment methods does Refuseless accept?
You can pay with Visa or Mastercard, or with cryptocurrency including BTC, ETH, USDT, USDC, or XMR.

Does Refuseless store my prompts?
No, the service applies zero prompt retention to all requests.

Which endpoint do I call and what tools is it compatible with?
You call POST /v1/chat/completions using an OpenAI-compatible request shape, and it works with coding agents like OpenCode and Pi.

Written by Legit.Show from refuseless.com on 2026-10-07. It does not change the score.

81/100
7/7 frames
Top 44% of 17,133 measured · #17 of 40 in llm inference api
Legit Benchmark — the simple average of 7 measured frames. Frames we could not measure are left out of the average, never counted as zero. Every frame is shown below with its evidence.
Checked 2026-10-05 · scores move as sites change

Are you the maker of Refuseless? Claim this listing. It is free, takes a meta tag, a DNS record or GitHub admin rights, and never changes the score.

Launched something? Add your product, free.

To cite this score: legit.show/s/refuseless-com/2026-10-05. That address never changes; this page moves with every re-measure.

Legit.Show scored Refuseless 81/100 on 2026-10-05, measured across all 7 frames from its public surface. legit.show/s/refuseless-com/2026-10-05

Is Refuseless production-ready?

Legit.Show scores Refuseless 81 out of 100 — the simple average of its 7 measured frames. Legit.Show ran its deterministic 7-Frame production-readiness benchmark on Refuseless (public-surface assessment), measured from the public surface with no LLM in the scoring path. Its strongest frame is Reliability; its weakest is Privacy. Every frame it averages is published with its evidence on the Legit.Show listing.

The 7 Frames

What we measured

Who it's for

Cybersecurity teams · Penetration testers · Coding professionals · AI agents · Security researchers

Pricing

GLM 5.3 Abliterated: $1.5 input / $2 cache / $2.5 output per 1M tokens (with discounts up to 40% for Flash variant). Credit card and cryptocurrency accepted. $5 free credits promo.

Sources and updates

Description
Taken from refuseless.com's own website on 2026-10-05.
Benchmark
Measured by Legit.Show from the live site, the way any visitor sees it — with no access to its code or accounts. Same method for every product, and no AI decides the score. Last checked 2026-10-05.

Put together from public information, without Refuseless's involvement. If anything here is wrong, tell us and a person will check it.

Visit Refuseless → · Alternatives to Refuseless → · How this was measured →

See how Refuseless ranks among tested llm inference api products →

Other tested llm inference api products