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Qwen
qwen/qwen3.5-flash-02-23Catalogue: 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.
Qwen qwen/qwen3.5-flash-02-23
The Qwen3.5 native vision-language Flash models are built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving higher inference efficiency. Compared to the...
Qwen3.5-Flash earns a request when it needs step-by-step reasoning, tool calls, very long inputs; a short, general request would go to Nemotron 3.5 Super VL (free) instead - free per million input tokens against $0.07 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 (this model has no published index).
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 Qwen3.5-Flash
targets:
- id: qwen3-5-flash-02-23
kind: llm
name: Qwen3.5-Flash
metadata:
model: qwen/qwen3.5-flash-02-23 # links the target to this catalogue entry
capabilities:
domains: [general]
supports_tools: true
context_window: 1000000
cost:
usd_per_1k_tokens: 0.000162Input list price per million tokens; Qwen3.5-Flash is the 3rd 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.