Is there an uncensored Nex-N2-Pro (Based on Qwen3.5-397B-A17B)?

Nex-N2-Pro (Based on Qwen3.5-397B-A17B) from Nex AGI is ranked 14 of 100 distinct active open-weight models in this Artificial Analysis snapshot.

Research and availability status

This is a research guide, not an available Wild West API model listing. No uncensored or abliterated Nex-N2-Pro (Based on Qwen3.5-397B-A17B) endpoint is confirmed here. An open-weight entry on AA does not establish that a derivative exists, works with a particular harness, or is hosted by Wild West API.

“Uncensored” describes reduced refusal behaviour or a service’s policy, not a standardized editing method. “Abliterated” refers to an intervention intended to reduce refusal-related behaviour through model-internal edits.

What rank 14 actually tells you

Nex-N2-Pro (Based on Qwen3.5-397B-A17B) scores above 86 of the 100 selected entries: a strictly-lower-score percentile of 86% in this snapshot. No other selected entry shares its exact index score. AA flags this index as estimated; treat the ordering as provisional.

It sits in the upper half of this selection. The relevant decision is whether its measured strengths fit the workload better than adjacent entries, not whether its rank is the highest available. Hosting cost and hardware requirements are outside this snapshot.

Base-model benchmark evidence

These are measurements attached to the evaluated base model, not an uncensored derivative. Missing values mean not reported here, never a zero score. Same-benchmark comparisons below use only entries with a reported measurement.

The highest numerical reported percentage for Nex-N2-Pro (Based on Qwen3.5-397B-A17B) is GPQA at 89.2%; the lowest is Humanity’s Last Exam at 33.7%. These are numerical extremes, not evidence that one skill is stronger than another: benchmark percentages cannot be compared as if the tests had equal difficulty, scoring rules or task coverage. Compare GPQA with that same benchmark on another model, not with a different test.

Context window and a practical evaluation workload

The 262,000-token window makes multi-document review or a focused repository investigation worth testing. Reserve room for instructions, tool responses and the answer before loading source material. Compare a full-context run with a retrieval-based run on the same questions; the window alone cannot tell you which produces better grounded answers.

54 of the 100 entries have a smaller reported window than Nex-N2-Pro (Based on Qwen3.5-397B-A17B). As a planning example, allocating 75% of its reported window to source material gives 196,500 tokens, leaving 65,500 for instructions, conversation, tool messages and output combined. This is arithmetic for a test budget, not an endpoint limit or an assertion that output can use the entire remainder.

Reasoning settings and release comparisons

It is marked reasoning by AA. There is no within-model setting spread to quantify, so a claim that a lower budget preserves its score would be unsupported. A derivative should be tested in the actual setting you intend to use.

No other selected entry is credited to Nex AGI in this 100-model snapshot. There is no publisher-peer comparison to make here, and similarly named releases outside this selection should not inherit these measurements.

Nearby ranked alternatives

The closest ranks give a bounded shortlist for evaluating Nex-N2-Pro (Based on Qwen3.5-397B-A17B). A small index gap is not a statistical significance claim; compare task outcomes and deployment conditions directly.

How to evaluate an uncensored or abliterated candidate

For Nex-N2-Pro (Based on Qwen3.5-397B-A17B), first locate the exact base release and any claimed derivative’s model card. Verify access, redistribution and usage terms at their original sources: AA’s open-weight classification does not assert an open-source license. Record the base revision, derivative revision, runtime and reasoning setting so your comparison can be reproduced.

Weight-based abliteration generally studies differences between activations on refusal-inducing and ordinary prompts, identifies candidate refusal-related directions, and intervenes on model internals. It is not equivalent to changing a system prompt or removing an API filter. Whether a particular intervention is supported depends on the model and implementation; this guide supplies no architecture-specific recipe or claim that the method has been validated on Nex-N2-Pro (Based on Qwen3.5-397B-A17B).

Run the base and candidate on the same permitted task set before and after changes. Measure refusal behaviour separately from factual errors, instruction following, coding correctness and tool execution. Use GPQA as one reference for task coverage, while keeping its published percentage separate from your own results. Track any regression and repeat with the exact context length and setting your application needs. A lower refusal rate is not evidence that all other behaviour survived unchanged.

Sources and scope

Snapshot retrieved 2026-10-06T06:17:07.812043+00:00. AA’s Nex-N2-Pro (Based on Qwen3.5-397B-A17B) model entry supplies the evaluated setting and measurements; the Artificial Analysis leaderboard is the population source. Top 100 distinct active open-weight model names by Artificial Analysis Intelligence Index. Reasoning-budget parentheticals are removed for grouping; the strongest scored setting represents each group. Open weights is AA's classification, not an open-source-license assertion. Scores describe the evaluated base models, not uncensored derivatives. Rank is within this selected active open-weight population, not the complete leaderboard.

For the research behind refusal-direction interventions, see Arditi et al., Refusal in Language Models Is Mediated by a Single Direction. This general research source does not establish successful abliteration of this release. Continue with the abliteration explanation or the current Wild West API line; only the latter lists products for sale.

Questions about Nex-N2-Pro (Based on Qwen3.5-397B-A17B)