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Qwen
qwen/qwen2.5-vl-72b-instructCatalogue: 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/qwen2.5-vl-72b-instruct
Qwen2.5-VL is proficient in recognizing common objects such as flowers, birds, fish, and insects. It is also highly capable of analyzing texts, charts, icons, graphics, and layouts within images.
Cached input reads $0.40 per 1M · weights on Hugging Face as Qwen/Qwen2.5-VL-72B-Instruct
Qwen2.5 VL 72B Instruct earns a request when it needs image input, 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 $0.80 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 Qwen2.5 VL 72B Instruct
targets:
- id: qwen2-5-vl-72b-instruct
kind: llm
name: Qwen2.5 VL 72B Instruct
metadata:
model: qwen/qwen2.5-vl-72b-instruct # links the target to this catalogue entry
capabilities:
domains: [general]
supports_tools: false
context_window: 128000
cost:
usd_per_1k_tokens: 0.000900Input list price per million tokens; Qwen2.5 VL 72B Instruct is the 9th 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.