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DeepSeek
deepseek/deepseek-v3.2-expCatalogue: Indexed in the reference catalogue; nothing on this deployment answers as this model yet.
Published by the vendor; the platform bills at the target's declared price.
DeepSeek deepseek/deepseek-v3.2-exp
DeepSeek-V3.2-Exp is an experimental large language model released by DeepSeek as an intermediate step between V3.1 and future architectures. It introduces DeepSeek Sparse Attention (DSA), a fine-grained sparse attention mechanism...
DeepSeek V3.2 Exp earns a request when it needs step-by-step reasoning, tool calls, running on your own hardware; a short, general request would go to Nemotron 3.5 Super VL (free) instead - free per million input tokens against $0.27 here; and the hardest requests, where the quality floor rules it out, to Claude Opus 5.5 (batch). Your rules decide the floor; the trace shows the comparison for every request.
Lower input price and an intelligence index within 5 points.
Nothing on this deployment answers as this model yet. Connect your own provider under Model providers, or add a target to your catalogue - the router picks it up without retraining.
# targets.yaml - declare a target that runs DeepSeek V3.2 Exp
targets:
- id: deepseek-v3-2-exp
kind: llm
name: DeepSeek V3.2 Exp
metadata:
model: deepseek/deepseek-v3.2-exp # links the target to this catalogue entry
capabilities:
domains: [general]
supports_tools: true
context_window: 163840
cost:
usd_per_1k_tokens: 0.000340Input list price per million tokens; DeepSeek V3.2 Exp is the 10th cheapest of 13 listed.
Photo: RDNE Stock project
A higher intelligence index, best first, with the price it comes at.
Other models by the same vendor in the catalogue.
The catalogue prints input and output prices and what three typical monthly workloads cost on each.