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Specifications
Specifications and pricing as served by Composite
Model ID
meta-llama/llama-3.2-1b-instruct
Provider
cv11
Context window
60,000 tokens
Accepts
text
Returns
text
Input price
$0.0338 per million tokens
Output price
$0.251 per million tokens
Prompt caching
Not offered for this model
Moderated
No
Cost on a monthly plan
About 0.4 plan credits per typical call (8k in, 1k out)
Routing endpoints
2 (base plus 1 routing variant)
Use Meta: Llama 3.2 1B Instruct in JanitorAI or SillyTavern
Make a Composite API key and type meta-llama/llama-3.2-1b-instruct as the model name. JanitorAI takes the full URL https://composite.lucidity.sh/v1/chat/completions; SillyTavern, ChubAI, Agnai and RisuAI take https://composite.lucidity.sh/v1. It runs on credits, a plan, or your own provider key.
This model has not carried enough traffic on Composite yet to publish a success rate, a latency figure or a traffic rank. The numbers appear here once it has.
Routing variants
The same model behind 1 alternative endpoint. Call the id directly to pin a route; the base id above picks for you.
Route
Model ID
Provider
Price
cheapThink
meta-llama/llama-3.2-1b-instruct:cheapThink
cv11
$0.0338 per million tokens input · $0.251 per million tokens output
Pricing
$0.0338 per million tokens input · $0.251 per million tokens output
Credits pay the provider’s price plus 25%; cache hits cost less.
On a monthly plan, a typical call (8k tokens in, 1k out) uses about 0.4 plan credits, at the provider's own price.
Provider
cv11
Roleplay fit
Llama models are a widely-supported open baseline, useful when you want predictable, well-documented behavior.
Context behavior
Its 60,000-token context window covers most single-session roleplay threads comfortably. For campaigns that run over many sessions, expect older messages to eventually drop out of context.
Cost for regular use
It is priced low enough for high-volume daily roleplay without the per-message cost adding up quickly.
From the provider
Llama 3.2 1B is a 1-billion-parameter language model focused on efficiently performing natural language tasks, such as summarization, dialogue, and multilingual text analysis. Its smaller size allows it to operate...