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  1. Models
  2. Text
  3. Abacusai

Qwen2 72B Dracarys

A Qwen2 72B Instruct finetune by Abacus.AI focused on improving coding performance.

Added Aug 2, 2025

Pricing

Auto routing · per 1M tokens
Input
$0.493
Output
$0.493
Cache read
$0.246
Compare provider prices

Specifications

Context window
32.8K
Max output
8.2K
Parameters
72B

Benchmarks

No public benchmark scores for this model yet.

Providers

Auto routing is available for this model. Explicit provider selection is not available.

Auto
Auto
Recommended
$0.493$0.493$0.246N/AN/A
Batch API
ProviderInputOutputCached input
Auto
Autoabacusai/Dracarys-72B-Instruct
$0.493/M$0.493/M$0.246/M

Batch rows for this model use normal routing and cost the same as standard API requests. Set provider in a row to choose a provider.

Reliability

Related text models

Compare Qwen2 72B Dracarys with similar models from the same provider or model family.

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Great multilingual support, strong at mathematics and coding, supports roleplay and chatbots.

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The latest series of Code-Specific Qwen large language models.

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Qwen25 VL 72b model with 32k context window

Qwen 2.5 32B Abliterated

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Uncensored version of Qwen 2.5 32B Instruct with restrictions removed.

Qwen3 VL 235B A22B Instruct Original

qwen3-vl-235b-a22b-instruct-original

Routed across multiple providers, which may include Alibaba, a Chinese entity; provider-specific privacy and logging guarantees may vary. Qwen3 Vision‑Language model (235B MoE, ≈22B active) tuned for instruction following and grounded visual QA. Excels at image understanding, dense OCR, charts and diagrams, and multi‑image context. Use this variant when you want concise, direct answers grounded in the visuals.

Qwen3 Next 80B A3B (Instruct)

qwen/qwen3-next-80b-a3b-instruct

Based on the new Qwen3‑Next architecture (hybrid attention, highly sparse MoE, training‑stability optimizations, and multi‑token prediction), the Qwen3‑Next‑80B‑A3B‑Instruct model delivers extreme efficiency with only 3B active parameters per pass. It performs comparably to Qwen3‑235B‑A22B‑Instruct‑2507 and shows clear advantages on ultra‑long context tasks (up to 256K tokens).