Qwen 3.5 35B A3B

CodeVisionReasoningWeb searchFunction calling
Advertised as qwen-3.5-35b-a3bqwen3-5-35b-a3bqwen3.5-35b-a3bqwen35-35b-a3b
Quick reference
Qwen 3.5 35B A3B — TLDR
  • 🧠 35B-parameter mixture-of-experts activating only ~3B per token
  • 📏 Native 256K-token context window
  • 👁️ Multimodal model handling both text and vision inputs
  • 🔧 Built for reasoning, coding, agents, function calling, web search
  • 🏢 From Alibaba's Qwen team, released under Apache 2.0
  • 🆕 Provider reports it surpasses the larger Qwen3-235B-A22B
  • ⚡ Sparse activation targets efficient, lower-cost inference
💰 Best price on AntSeed
$0.028 / $0.11483%
per 1M · cheapest in / out
📏 Context
256K tokens
🐜 Sellers
10
advertising on AntSeed
Provider

Alibaba Group is a Chinese multinational technology company founded in 1999 and headquartered in Hangzhou, Zhejiang. Originally built around e-commerce and cloud computing, Alibaba has become one of the most prolific contributors to open-weight AI research, developing the Qwen…

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About this model

Qwen 3.5 35B A3B is a sparse mixture-of-experts model from Alibaba's Qwen team, released in February 2026 under the Apache 2.0 license. It carries roughly 35 billion total parameters but activates only about 3 billion per token, a design intended to deliver large-model behavior at a fraction of the compute cost. The model is multimodal, accepting text and vision inputs, and supports a native 256K-token context window alongside reasoning, code-optimized generation, function calling, and web search.

Within the Qwen family, this release sits between smaller and larger 3.5-generation siblings such as Qwen 3.5 9B and the much larger Qwen 3.5 397B. The provider describes the 35B A3B model as surpassing the earlier, denser Qwen 3 235B A22B Instruct 2507 while being roughly 6.7 times smaller in total parameters — a generational efficiency gain attributed to the company's own description.

In practice, the model targets reasoning, coding, and general-knowledge tasks where its small active-parameter footprint keeps latency and serving costs low. Later Qwen releases such as Qwen 3.6 27B continue this compact mixture-of-experts direction. Because primary benchmark documentation specific to this checkpoint is limited, the description here stays to verifiable architectural and licensing facts.

View source on GitHub ↗View model card on HuggingFace ↗
Sources
qwen.aiQwen3.6-35B-A3B: Agentic Coding Power, Now Open to All· qwen.aihuggingface.coQwen/Qwen3.5-35B-A3B · Hugging Face· huggingface.co

This About section is AI-generated from public sources via VeniceStats + Venice inference, with no human editing. It may contain inaccuracies.

Usage on AntSeed
Tokens served
225.51k
input + output
Requests
579
settled calls
Buyers
6
distinct, on this model
Sellers used
4
of 10 advertising
Settled
$0.06
gross USDC, this model
Sellers serving Qwen 3.5 35B A3B (10)compare on the network explorer →
SellerReputationRoutingInput $/MCached $/MOutput $/MCategoriesAPI

"Best price" and the seller table are live AntSeed catalog data (advertised $/1M tokens — or $ per generated image for unit-billed image models — not settled amounts). Reputation = on-chain trust (0-100). "Routing" = the SDK's default buyer routing (what the VPR desktop app ships with): a trust ≥ 60 gate on the effective reputation, then cheapest-first among routable sellers; live failover state (per-peer cooldowns) is buyer-side runtime and not included. Model knowledge (TLDR, provider, About) via the VeniceStats enrichment layer. Advertised catalog, not the model used in any specific purchase. "Usage on AntSeed" counts only settlements whose buyers share the per-model split on-chain (metadata v2/v3, opt-in), so every usage figure is a lower bound.