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Abliterated Large Flash

abliteration-ai·abliteration-ai/abliterated-model-large-flash·GA·Closed·NEW
Abliterated Large Flash is an uncensored reasoning model based on DeepSeek V4.1-Flash-1017. It accepts text and images and returns text, with tool calling, JSON mode, JSON Schema output, and automatic prompt caching. Its shared input/output context is 1,048,576 tokens, with up to 262,144 output tokens when space permits. Reasoning defaults to high and can be turned off or set to low, high, or max. Supports up to four images across the request history.

Abliterated Large Flash by abliteration-ai is currently listed from a single provider. No official API price is listed for it; the prices below come from third-party channels.

The context window is 1,048,576 tokens, with an output limit of 262,144 tokens. It supports reasoning, tool use, and structured output. Accepted input modalities are Text and Image.

Specs & pricing

Input / output per 1M tokens
Official price
—
Lowest paid·NanoGPTGateway
$1.00 / $2.00
Blended $1.25
Context
1,048,576
Output limit
262,144
Knowledge cutoff
—
Released / updated
2026-10-23 / 2026-10-23
Capabilities
✓ Reasoning✓ Tool use✓ Structured output? Temperature✓ Attachments
Modalities
TextImage

Available at 1 providers1 with public prices

ProviderTierInputOutputCache readCache writeContextOutput limitStatus
NanoGPTGateway$1.00$2.00$0.10—1,048,576262,144

Sorted by blended price (input×0.75 + output×0.25) asc. The official channel always shows regardless of rank. Whether a gateway's low price is actually usable can't be verified.

Reasoning control

effort = noneeffort = minimaleffort = loweffort = mediumeffort = higheffort = xhigheffort = max

Your usage cost

1NanoGPT$192.00
The cheapest paid channel is the only channel.

Price historyone sample accumulated per data sync

Input list —Output list —Min blended $1.25

Price history accumulates from each data sync; currently only 1 sample(s) (2026-10-23). Each future sync adds a point, and once accumulated a line is drawn here.

Data partly from models.dev (MIT)