Abliterated models
An abliterated model is an open-weight LLM with the refusal direction removed from its weights, so it answers instead of declining. Wild West API sells abliterated and uncensored AI models on one OpenAI-compatible endpoint, priced per token with a hard cap on every key.
What abliteration actually does
A language model's tendency to refuse is not spread evenly through the weights. It concentrates in a single direction in the residual stream, and that direction can be found by running the model over a batch of harmful prompts and a batch of harmless ones and taking the mean difference of the activations at the last token.
Abliteration projects that direction out of the embedding matrix, every attention output projection and every MLP output projection. What is left cannot represent refusal, so it does not refuse. No retraining, no fine-tuning data, no prompt engineering: it is surgery on the weights, and it survives whatever you put in the context window. The method step by step is in the abliteration explainer.
Abliterated and uncensored models you can call
Every model below is on sale today, at the price shown per million tokens. The abliterated tag comes straight from the API's own abliterated field; the rest of the line is uncensored: builds with the refusal training taken off, which refuse far less than the stock model but were not edited along the refusal direction.
| Model | Context | Input / 1M | Output / 1M |
|---|---|---|---|
| Outlaw 1outlaw-1-xploded | 1M | $0.30 | $1.00 |
| MiMo V2.6 Flash Xplodedmimo-v2.6-flash-xploded | 1M | $1.00 | $3.00 |
| GLM 5.3 Xplodedglm-5.3-xploded | 1M | $2.50 | $4.50 |
| GLM 5.3 Flash Xplodedglm-5.3-flash-xploded | 1M | $0.40 | $1.60 |
| Qwen3.8 27B Xplodedqwen3.8-27b-xploded | 512K | $0.30 | $2.40 |
Who publishes abliterated models
Most abliterated weights are community releases on Hugging Face rather than hosted products. FailSpy published the first ones and named the technique. Maxime Labonne (mlabonne) wrote the tutorial most people learned it from, and released NeuralDaredevil 8B Abliterated, an abliteration followed by a DPO retune to win back what the edit cost. huihui-ai has since published abliterated versions of most major open models. abliteration.ai is the company that sells its own abliterated models as a hosted API.
Wild West API does not resell any of those. It sells the line above, and where a model people search for has no equivalent on it, the page for that model says so and names the nearest one: abliteration.ai's Abliterated Model Large V2, for example, or the uncensored AI pages for GLM, Kimi, DeepSeek, MiniMax and the rest.
Calling one
Anything that speaks the OpenAI chat completions format works unchanged. Point it at Wild West API, use your key, and name the model. The same models answer on the Anthropic Messages format too, so Claude Code drives them directly.
curl https://wildwestapi.com/v1/chat/completions \
-H "Authorization: Bearer sk-ww-..." \
-H "Content-Type: application/json" \
-d '{
"model": "mimo-v2.6-flash-xploded",
"messages": [{"role": "user", "content": "Hello"}]
}'# pip install openai
from openai import OpenAI
client = OpenAI(
base_url="https://wildwestapi.com/v1",
api_key="sk-ww-...",
)
resp = client.chat.completions.create(
model="mimo-v2.6-flash-xploded",
messages=[{"role": "user", "content": "Hello"}],
)
print(resp.choices[0].message.content)The research guides: 100 open-weight models
These guides cover distinct active open-weight models in an Artificial Analysis snapshot retrieved 2026-10-06T06:17:07.812043+00:00. They are research pages, not 100 hosted uncensored derivatives. Each explains the evaluated base-model setting, measured benchmarks, context, nearby alternatives and availability limits.
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.
| Rank | Research guide | Base-model Intelligence Index | Reported context |
|---|---|---|---|
| 1 | MiMo-V2.6-Pro | 46.32 | 1,000,000 tokens |
| 2 | GLM-5.3 (Max) | 44.78 | 1,000,000 tokens |
| 3 | Kimi K3 (Max) | 43.59 | 1,048,576 tokens |
| 4 | GLM 5.3 Flash | 41.81 | 1,000,000 tokens |
| 5 | Qwen3.8 2.4T A95B | 39.89 | 262,000 tokens |
| 6 | Qwen3.8-Flash-Next | 39.82 | 256,000 tokens |
| 7 | DeepSeek V4.1 Flash (Max) | 39.46 | 1,000,000 tokens |
| 8 | MiMo-V2.6-Flash | 37.88 | 1,000,000 tokens |
| 9 | DeepSeek V4 Pro 0813 (Max) | 36.00 | 1,000,000 tokens |
| 10 | Qwen3.8 27B (Xhigh) | 33.70 | 256,000 tokens |
| 11 | Motif 3 | 33.57 (estimated) | 262,144 tokens |
| 12 | K2 Horizon 375B A23B | 30.50 | 524,288 tokens |
| 13 | MiniMax-M3 | 29.22 | 1,000,000 tokens |
| 14 | Nex-N2-Pro (Based on Qwen3.5-397B-A17B) | 28.20 (estimated) | 262,000 tokens |
| 15 | Kimi K2.7 Code | 25.81 | 256,000 tokens |
| 16 | Inkling Small | 25.66 | 1,000,000 tokens |
| 17 | K2 Horizon MoVA 36B A4B | 25.30 | 524,288 tokens |
| 18 | Hy3 | 25.30 | 256,000 tokens |
| 19 | Inkling (Xhigh) | 24.98 | 1,000,000 tokens |
| 20 | Solar Open2 250B | 24.74 (estimated) | 1,048,576 tokens |
| 21 | Ling-3.0-flash-VL | 24.57 | 262,144 tokens |
| 22 | Nemotron 3 Ultra 550B A55B (Reasoning) | 22.93 | 262,144 tokens |
| 23 | A.X-K2 | 22.70 (estimated) | 262,144 tokens |
| 24 | Ling-3.0-flash-Fin | 22.60 | 262,144 tokens |
| 25 | G9v3-39A5B | 21.83 (estimated) | 131,072 tokens |
| 26 | Qwen3.5 397B A17B (Non-reasoning) | 21.44 (estimated) | 262,144 tokens |
| 27 | K2 Horizon 7B | 20.60 | 524,288 tokens |
| 28 | Ling 3.0 Flash | 20.13 | 262,144 tokens |
| 29 | K-EXAONE 2.0 0803 | 19.69 (estimated) | 262,144 tokens |
| 30 | LongCat 2.0 | 19.11 | 1,000,000 tokens |
| 31 | Qwen3.6 35B A3B (Reasoning) | 18.23 | 262,144 tokens |
| 32 | Qwen3.5 122B A10B (Non-reasoning) | 17.75 (estimated) | 262,144 tokens |
| 33 | Muse Glimmer (High) | 17.48 | 131,072 tokens |
| 34 | Gemma 4 26B A4B (Reasoning) | 16.67 (estimated) | 256,000 tokens |
| 35 | Ring-2.6-1T | 16.62 | 262,144 tokens |
| 36 | K2 Horizon 3.7B | 15.61 | 524,288 tokens |
| 37 | Granite 4.2 30B | 14.83 (estimated) | 131,072 tokens |
| 38 | Gemma 4 31B (Reasoning) | 14.67 | 256,000 tokens |
| 39 | Mistral Medium 3.5 | 14.19 | 256,000 tokens |
| 40 | Gemma 4 12B (Reasoning) | 14.18 (estimated) | 256,000 tokens |
| 41 | Apriel-v1.6-15B-Thinker | 13.40 (estimated) | 128,000 tokens |
| 42 | Qwen3.5 9B (Non-reasoning) | 13.27 (estimated) | 262,144 tokens |
| 43 | EXAONE 4.5 33B (Reasoning) | 13.21 (estimated) | 262,144 tokens |
| 44 | Command A+ | 13.13 | 192,000 tokens |
| 45 | Qwen3.5 4B (Reasoning) | 13.12 (estimated) | 262,144 tokens |
| 46 | Nemotron 3.5 Lightning | 12.86 | 1,000,000 tokens |
| 47 | Nemotron 3 Super 120B A12B (Reasoning) | 12.83 | 1,000,000 tokens |
| 48 | MiniCPM5-2B | 12.46 | 131,072 tokens |
| 49 | HyperNova 60B 2605 (High, Based on gpt-oss-120b) | 11.74 (estimated) | 131,072 tokens |
| 50 | Nemotron Cascade 2 30B A3B | 11.67 (estimated) | 1,000,000 tokens |
| 51 | gpt-oss-120b (High) | 11.60 | 131,072 tokens |
| 52 | K2 Think V2 | 11.50 (estimated) | 262,144 tokens |
| 53 | LongCat Flash Lite | 11.47 (estimated) | 256,000 tokens |
| 54 | HyperCLOVA X SEED Think (32B) | 11.36 (estimated) | 128,000 tokens |
| 55 | Mistral Small 4 (Reasoning) | 11.27 | 256,000 tokens |
| 56 | Qwen3 Next 80B A3B (Reasoning) | 11.20 (estimated) | 262,144 tokens |
| 57 | Granite 4.2 8B | 11.08 | 131,072 tokens |
| 58 | Ling 3.0 Tiny | 11.06 | 262,144 tokens |
| 59 | G9v3-3B | 10.84 (estimated) | 131,072 tokens |
| 60 | Trinity Large Thinking | 10.82 | 512,000 tokens |
| 61 | INTELLECT-3 (Based on GLM-4.5-Air) | 10.61 (estimated) | 131,100 tokens |
| 62 | Solar Open 100B (Reasoning) | 10.36 (estimated) | 128,000 tokens |
| 63 | Nemotron 3 Nano Omni 30B A3B Reasoning | 10.25 (estimated) | 256,000 tokens |
| 64 | Llama 4 Maverick | 9.99 (estimated) | 1,000,000 tokens |
| 65 | gpt-oss-20b (Low) | 9.95 (estimated) | 131,072 tokens |
| 66 | North Mini Code | 9.91 | 256,000 tokens |
| 67 | K2-V2 (High) | 9.87 (estimated) | 512,000 tokens |
| 68 | Qwen3 Next 80B A3B Instruct | 9.64 (estimated) | 262,144 tokens |
| 69 | DiffusionGemma 26B A4B | 9.51 (estimated) | 256,000 tokens |
| 70 | Mistral Large 3 | 9.27 | 256,000 tokens |
| 71 | Qwen3 Coder Next | 9.24 | 256,000 tokens |
| 72 | Granite 4.2 3B | 9.06 | 131,072 tokens |
| 73 | Tri-21B-Think | 8.99 (estimated) | 32,000 tokens |
| 74 | Gemma 4 E4B (Reasoning) | 8.91 (estimated) | 128,000 tokens |
| 75 | NVIDIA Nemotron 3 Nano 30B A3B (Reasoning) | 8.90 | 1,000,000 tokens |
| 76 | MiniCPM5-1B (Reasoning) | 8.80 (estimated) | 128,000 tokens |
| 77 | Sarvam 105B (High) | 8.79 (estimated) | 128,000 tokens |
| 78 | Magistral Small 1.2 | 8.59 (estimated) | 128,000 tokens |
| 79 | Nanbeige4.1-3B | 8.40 (estimated) | 256,000 tokens |
| 80 | LFM2.5-2.6B | 8.39 (estimated) | 128,000 tokens |
| 81 | EXAONE 4.0 32B (Reasoning) | 8.18 (estimated) | 131,000 tokens |
| 82 | Llama 4 Scout | 8.08 (estimated) | 10,000,000 tokens |
| 83 | Hermes 4 - Llama-3.1 70B (Reasoning) | 7.91 (estimated) | 128,000 tokens |
| 84 | Falcon-H1R-7B | 7.83 (estimated) | 256,000 tokens |
| 85 | Gemma 4 E2B (Reasoning) | 7.77 (estimated) | 128,000 tokens |
| 86 | Qwen3 Omni 30B A3B (Reasoning) | 7.76 (estimated) | 65,536 tokens |
| 87 | Step3 VL 10B | 7.69 (estimated) | 65,536 tokens |
| 88 | Llama 3.3 Instruct 70B | 7.66 (estimated) | 128,000 tokens |
| 89 | Llama 3.1 Nemotron Ultra 253B v1 (Reasoning) | 7.53 (estimated) | 128,000 tokens |
| 90 | ERNIE 4.5 300B A47B | 7.50 (estimated) | 131,072 tokens |
| 91 | Hermes 4 - Llama-3.1 405B (Reasoning) | 7.49 (estimated) | 128,000 tokens |
| 92 | NVIDIA Nemotron Nano 12B v2 VL (Reasoning) | 7.48 (estimated) | 128,000 tokens |
| 93 | NVIDIA Nemotron Nano 9B V2 (Reasoning) | 7.43 (estimated) | 131,072 tokens |
| 94 | NVIDIA Nemotron 3 Nano 4B | 7.38 (estimated) | 262,000 tokens |
| 95 | Kimi Linear 48B A3B Instruct | 7.30 (estimated) | 1,000,000 tokens |
| 96 | Llama 3.1 Instruct 405B | 7.29 (estimated) | 128,000 tokens |
| 97 | LFM2.5-8B-A1B | 7.22 (estimated) | 32,768 tokens |
| 98 | Ring-flash-2.0 | 7.15 (estimated) | 128,000 tokens |
| 99 | Olmo 3.1 32B Think | 7.12 (estimated) | 65,500 tokens |
| 100 | Command A | 6.96 (estimated) | 256,000 tokens |
Artificial Analysis population source. For products available through this API, use the current model line.
FAQ
What are abliterated models?
Open-weight language models whose refusal behaviour was removed by editing the weights directly. The technique finds the one direction in the model's activations that carries refusal and projects it out, so the model can no longer represent "I won't do that". The result is published as a new set of weights, usually with -abliterated in the name.
Is ablated AI the same as abliterated AI?
Nearly, and the words get used interchangeably, but they come from different places. Ablation is the general research practice of removing a component to see what it was doing, so an ablated model is any model with something taken out. Abliteration is one specific ablation: removing the direction in the activations that carries refusal. Every abliterated model is ablated; most ablated models are not abliterated.
Who coined the word abliteration?
FailSpy, as a portmanteau of ablate and obliterate, for weights edited with the method from Arditi et al., "Refusal in Language Models Is Mediated by a Single Direction" (2024). Maxime Labonne's tutorial later that year is what made the technique widely known.
Is abliterated the same as uncensored?
No, and the difference matters. Uncensored usually describes a loosely aligned finetune that refuses much less than a frontier model but can still decline. Abliterated is the narrower, stronger claim: the refusal direction was surgically removed. An uncensored model was persuaded; an abliterated one was altered.
Does abliteration make a model worse?
Usually a little. Removing a direction from every projection is a blunt edit and it costs some capability, most visibly on reasoning and instruction following. The loss is small but real, which is why the good abliterations are followed by a light retune.
Is it legal to use one?
Running a model that does not refuse is not itself unlawful, and these builds are published openly. What you do with the output is governed by the same law as anything else you write or run. An abliterated model removes a guardrail, not a legal obligation. The terms still forbid illegal content, anything sexual involving minors, and content targeting real people.