GLM 5.3 vs DeepSeek V4.1 uncensored: which hosted model should you pick?

GLM-5.3 Flash Uncensored and DeepSeek V4.1 Flash Uncensored are the two models on our uncensored LLM API. Both are open-weights models with the refusals edited out of the weights, both read about 1M tokens of text, and both are for authorized security work: pen testing systems you have permission to test, defensive research and API reverse engineering. Our terms apply to every request. They differ on output length, reasoning settings, price, and how much general knowledge each kept through the edit.

For high-volume agent work, pick GLM-5.3 Flash Uncensored. It costs less per input and output token ($0.15 and $0.50 per million), its model page lists parallel tool calls, and its uncensored build showed no MMLU loss against its base. Pick DeepSeek V4.1 Flash Uncensored when a single response has to run past 131K tokens, when you want thinking switched off for quick calls, or when you want the base model with the higher published score on the Artificial Analysis Cyber Index.

GLM 5.3 vs DeepSeek V4.1 uncensored at a glance

Specs come from the model pages for GLM and DeepSeek, and the MMLU figures come from the publisher's model cards. DeepSeek is 25% off list during the current promotion.

What are the base models?

GLM-5.3-Flash is Z.ai's open-weights model from late August 2026, released under MIT. It is a hybrid mixture-of-experts model with 320B total parameters and 18B active per token, and it was the first natively multimodal model in the GLM-5 series, though the uncensored route we host takes text only. Our GLM-5.3 Flash Uncensored launch post has the release details.

DeepSeek V4.1 Flash came out on 10 September 2026, also open weights under MIT, and the uncensored build uses the 1017 update DeepSeek shipped on 17 October. Its uncensored checkpoint is the bigger download (510 GB on Hugging Face against 328 GB for GLM), with FP4 experts and a separate Engram memory table. The DeepSeek V4.1 Flash Uncensored post covers its history.

dealignai publishes both uncensored builds on Hugging Face, and we serve both on our own GPUs. Each was made by editing the weights directly to remove the refusal direction, a method called abliteration (people also search for "obliterated models"), and neither card reports fine-tuning, adapters or prompt tricks. What abliteration is explains the method.

How do context window and max output compare?

Both models accept about 1.05M tokens of input, enough for a large codebase or a full capture of an API's traffic in one request.

Max output is the bigger gap. GLM tops out at 131K tokens per response and DeepSeek allows 393K (393,216), three times as much. Most security agents work in short turns and never approach either limit, so the gap only shows up when the output is the deliverable, such as a findings report across a large scope or a full client SDK for an API you just mapped. DeepSeek can return those in one pass.

On both models, reasoning spends part of max_tokens, and our OpenAI compatibility docs note that too small a budget can leave the answer empty.

How do the reasoning settings differ?

GLM-5.3 Flash Uncensored accepts low, high or max and defaults to max. The dealignai card says low effort keeps some refusals by design, so keep GLM at high or max on tasks the base model would refuse.

DeepSeek V4.1 Flash Uncensored accepts none, low, high or max and defaults to high. Setting none turns thinking off, which suits quick classification at volume, such as sorting log lines into noise and worth a look. GLM has no setting that turns thinking off.

On the Responses API you set the level with "reasoning": {"effort": "high"}. For Chat Completions and full request examples, start from the quickstart.

Do they handle my data differently?

No. Both run under the same uncensored-model rule in our data controls doc, so picking one over the other doesn't change what happens to your prompts. Every prompt and response is kept even when storage is turned off, and experiential Labs keeps a copy after an account is deleted. The billing ledger holds token counts, cost and timing but never prompt text. Traffic never trains shared models.

Which is cheaper?

GLM is cheaper on input and output, and DeepSeek is cheaper on cached input.

Take a review job that loads 2M tokens of source code with no cache hits and writes a 200K-token report. On GLM that costs $0.30 for input plus $0.10 for output, $0.40 in total. On DeepSeek at list price it costs $0.60 plus $0.24, or $0.84, and the current promotion takes 25% off list.

The cached rate changes the math for agents that resend the same long context on every step. DeepSeek charges half of GLM's cached rate, so an agent whose input is mostly cache hits and whose outputs are short can come out cheaper on DeepSeek. Price your own token mix before you decide. Teams running agents around the clock can ask us for committed pricing.

What do the published capability numbers say?

Every figure below comes from the publisher of the weights or from a third party testing the base model.

General knowledge (MMLU). dealignai's GLM card reports 87.33% for the uncensored build against 86.74% for the base model, within noise. Its DeepSeek card reports 83.77% for the uncensored build against 87.63% for the base, a drop of 3.86 points. The two cards used different evaluation setups, so compare each model only with its own base. By that measure the GLM edit cost nothing measurable on MMLU and the DeepSeek edit cost some general knowledge.

Refusals (HarmBench-320). Both cards report that their uncensored build complied with all 320 prompts in the HarmBench-320 set, graded by the publisher's own classifier. For GLM that holds at default and max reasoning, with low reasoning keeping some refusals. For DeepSeek the card reports it at max effort.

Security tasks, base models. As of 27 October 2026, the Artificial Analysis Cyber Index lists DeepSeek V4.1 Flash 1017 (Max) second with a score of 57, behind Kimi K3.1 Pro at 64 and ahead of Grok 4.7 at 56, and GLM-5.3-Flash isn't in the top three. The index measures defensive work: finding, reproducing and patching vulnerabilities in real codebases. Separately, Vals AI reported on 20 October that DeepSeek V4.1 Flash 1017 beat GPT-6 Sol on one multi-step task in a simulated corporate network, which Vals itself called a preliminary single data point.

GLM-5.3 more broadly. Anthropic's research from 29 September 2026 called the GLM-5.3 release "a meaningful step change in the cyber capabilities available to attackers." That study focused on the full GLM-5.3. For the Flash model, Anthropic ran its own abliteration and reported mean refusal falling from 95% to 14%, a different edit from the dealignai weights we host.

Which one should you pick?

Pick GLM-5.3 Flash Uncensored when:

• Cost per token matters at volume. Input is half DeepSeek's list price and output is less than half.
• Your agents make several tool calls per turn, since GLM's model page lists parallel tool calls.
• You want an uncensored build with no measured MMLU loss against its base.

Pick DeepSeek V4.1 Flash Uncensored when:

• The response itself is long, like a full findings report or a generated SDK that runs past 131K tokens.
• You want thinking off for quick calls, which GLM can't do.
• You want the base model with the higher published Cyber Index score.
• Most of your input is cached, where DeepSeek's $0.03 rate beats GLM's $0.06.

You can also use both. They sit behind the same OpenAI-compatible endpoint, so routing by task is a change to the model string, and one way to split the work is GLM for high-volume agent steps and DeepSeek for the occasional long report. Either way, test both on your own tasks before you commit a pipeline to one.

Both models are on the uncensored models page, with live prices and the request that calls each one.