Wild West API

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 is not the same as uncensored. Uncensored is the loose claim: a finetune that refuses less. Abliterated is the specific one: the refusal direction was removed. The model list tags them separately for that reason, and the difference is worth reading before you pick one.

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.

ModelContextInput / 1MOutput / 1M
Outlaw 1outlaw-1-xplodeduncensoredabliteratedreads imagestoolsreasoning1M$0.30$1.00
MiMo V2.6 Flash Xplodedmimo-v2.6-flash-xplodeduncensoredabliteratedreads imagestoolsreasoning1M$1.00$3.00
GLM 5.3 Xplodedglm-5.3-xplodeduncensoredtoolsreasoning1M$2.50$4.50
GLM 5.3 Flash Xplodedglm-5.3-flash-xplodeduncensoredreads imagestoolsreasoning1M$0.40$1.60
Qwen3.8 27B Xplodedqwen3.8-27b-xplodeduncensoredreads imagestoolsreasoning512K$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
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"}]
  }'
Python
# 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 cap is the point. Every key carries a hard spend ceiling that is enforced before the request goes upstream, not reconciled after it. The reply cannot cost more than the hold placed before it was sent, which is what makes handing a key to a script or a test harness survivable.

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.

RankResearch guideBase-model Intelligence IndexReported context
1MiMo-V2.6-Pro46.321,000,000 tokens
2GLM-5.3 (Max)44.781,000,000 tokens
3Kimi K3 (Max)43.591,048,576 tokens
4GLM 5.3 Flash41.811,000,000 tokens
5Qwen3.8 2.4T A95B39.89262,000 tokens
6Qwen3.8-Flash-Next39.82256,000 tokens
7DeepSeek V4.1 Flash (Max)39.461,000,000 tokens
8MiMo-V2.6-Flash37.881,000,000 tokens
9DeepSeek V4 Pro 0813 (Max)36.001,000,000 tokens
10Qwen3.8 27B (Xhigh)33.70256,000 tokens
11Motif 333.57 (estimated)262,144 tokens
12K2 Horizon 375B A23B30.50524,288 tokens
13MiniMax-M329.221,000,000 tokens
14Nex-N2-Pro (Based on Qwen3.5-397B-A17B)28.20 (estimated)262,000 tokens
15Kimi K2.7 Code25.81256,000 tokens
16Inkling Small25.661,000,000 tokens
17K2 Horizon MoVA 36B A4B25.30524,288 tokens
18Hy325.30256,000 tokens
19Inkling (Xhigh)24.981,000,000 tokens
20Solar Open2 250B24.74 (estimated)1,048,576 tokens
21Ling-3.0-flash-VL24.57262,144 tokens
22Nemotron 3 Ultra 550B A55B (Reasoning)22.93262,144 tokens
23A.X-K222.70 (estimated)262,144 tokens
24Ling-3.0-flash-Fin22.60262,144 tokens
25G9v3-39A5B21.83 (estimated)131,072 tokens
26Qwen3.5 397B A17B (Non-reasoning)21.44 (estimated)262,144 tokens
27K2 Horizon 7B20.60524,288 tokens
28Ling 3.0 Flash20.13262,144 tokens
29K-EXAONE 2.0 080319.69 (estimated)262,144 tokens
30LongCat 2.019.111,000,000 tokens
31Qwen3.6 35B A3B (Reasoning)18.23262,144 tokens
32Qwen3.5 122B A10B (Non-reasoning)17.75 (estimated)262,144 tokens
33Muse Glimmer (High)17.48131,072 tokens
34Gemma 4 26B A4B (Reasoning)16.67 (estimated)256,000 tokens
35Ring-2.6-1T16.62262,144 tokens
36K2 Horizon 3.7B15.61524,288 tokens
37Granite 4.2 30B14.83 (estimated)131,072 tokens
38Gemma 4 31B (Reasoning)14.67256,000 tokens
39Mistral Medium 3.514.19256,000 tokens
40Gemma 4 12B (Reasoning)14.18 (estimated)256,000 tokens
41Apriel-v1.6-15B-Thinker13.40 (estimated)128,000 tokens
42Qwen3.5 9B (Non-reasoning)13.27 (estimated)262,144 tokens
43EXAONE 4.5 33B (Reasoning)13.21 (estimated)262,144 tokens
44Command A+13.13192,000 tokens
45Qwen3.5 4B (Reasoning)13.12 (estimated)262,144 tokens
46Nemotron 3.5 Lightning12.861,000,000 tokens
47Nemotron 3 Super 120B A12B (Reasoning)12.831,000,000 tokens
48MiniCPM5-2B12.46131,072 tokens
49HyperNova 60B 2605 (High, Based on gpt-oss-120b)11.74 (estimated)131,072 tokens
50Nemotron Cascade 2 30B A3B11.67 (estimated)1,000,000 tokens
51gpt-oss-120b (High)11.60131,072 tokens
52K2 Think V211.50 (estimated)262,144 tokens
53LongCat Flash Lite11.47 (estimated)256,000 tokens
54HyperCLOVA X SEED Think (32B)11.36 (estimated)128,000 tokens
55Mistral Small 4 (Reasoning)11.27256,000 tokens
56Qwen3 Next 80B A3B (Reasoning)11.20 (estimated)262,144 tokens
57Granite 4.2 8B11.08131,072 tokens
58Ling 3.0 Tiny11.06262,144 tokens
59G9v3-3B10.84 (estimated)131,072 tokens
60Trinity Large Thinking10.82512,000 tokens
61INTELLECT-3 (Based on GLM-4.5-Air)10.61 (estimated)131,100 tokens
62Solar Open 100B (Reasoning)10.36 (estimated)128,000 tokens
63Nemotron 3 Nano Omni 30B A3B Reasoning10.25 (estimated)256,000 tokens
64Llama 4 Maverick9.99 (estimated)1,000,000 tokens
65gpt-oss-20b (Low)9.95 (estimated)131,072 tokens
66North Mini Code9.91256,000 tokens
67K2-V2 (High)9.87 (estimated)512,000 tokens
68Qwen3 Next 80B A3B Instruct9.64 (estimated)262,144 tokens
69DiffusionGemma 26B A4B9.51 (estimated)256,000 tokens
70Mistral Large 39.27256,000 tokens
71Qwen3 Coder Next9.24256,000 tokens
72Granite 4.2 3B9.06131,072 tokens
73Tri-21B-Think8.99 (estimated)32,000 tokens
74Gemma 4 E4B (Reasoning)8.91 (estimated)128,000 tokens
75NVIDIA Nemotron 3 Nano 30B A3B (Reasoning)8.901,000,000 tokens
76MiniCPM5-1B (Reasoning)8.80 (estimated)128,000 tokens
77Sarvam 105B (High)8.79 (estimated)128,000 tokens
78Magistral Small 1.28.59 (estimated)128,000 tokens
79Nanbeige4.1-3B8.40 (estimated)256,000 tokens
80LFM2.5-2.6B8.39 (estimated)128,000 tokens
81EXAONE 4.0 32B (Reasoning)8.18 (estimated)131,000 tokens
82Llama 4 Scout8.08 (estimated)10,000,000 tokens
83Hermes 4 - Llama-3.1 70B (Reasoning)7.91 (estimated)128,000 tokens
84Falcon-H1R-7B7.83 (estimated)256,000 tokens
85Gemma 4 E2B (Reasoning)7.77 (estimated)128,000 tokens
86Qwen3 Omni 30B A3B (Reasoning)7.76 (estimated)65,536 tokens
87Step3 VL 10B7.69 (estimated)65,536 tokens
88Llama 3.3 Instruct 70B7.66 (estimated)128,000 tokens
89Llama 3.1 Nemotron Ultra 253B v1 (Reasoning)7.53 (estimated)128,000 tokens
90ERNIE 4.5 300B A47B7.50 (estimated)131,072 tokens
91Hermes 4 - Llama-3.1 405B (Reasoning)7.49 (estimated)128,000 tokens
92NVIDIA Nemotron Nano 12B v2 VL (Reasoning)7.48 (estimated)128,000 tokens
93NVIDIA Nemotron Nano 9B V2 (Reasoning)7.43 (estimated)131,072 tokens
94NVIDIA Nemotron 3 Nano 4B7.38 (estimated)262,000 tokens
95Kimi Linear 48B A3B Instruct7.30 (estimated)1,000,000 tokens
96Llama 3.1 Instruct 405B7.29 (estimated)128,000 tokens
97LFM2.5-8B-A1B7.22 (estimated)32,768 tokens
98Ring-flash-2.07.15 (estimated)128,000 tokens
99Olmo 3.1 32B Think7.12 (estimated)65,500 tokens
100Command A6.96 (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.

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