Coding / Qwen

Qwen3.5 · 35B A3B

Qwen3.5-35B-A3B is an Apache-2.0 licensed causal language model with a vision encoder, hosted in the Qwen/Qwen3.5-35B-A3B repository. It supports image-text-to-text generation and is provided in Hugging Face Transformers format with safetensors weights. The model has 35B total parameters with 3B activated, uses a hybrid architecture combining Gated DeltaNet, Gated Attention, and a sparse Mixture-of-Experts layer, and supports a native context length of 262,144 tokens, extensible up to 1,010,000 tokens. It is based on Qwen/Qwen3.5-35B-A3B-Base and is described as compatible with Hugging Face Transformers, vLLM, SGLang, and KTransformers. The model card highlights a unified vision-language foundation, efficient hybrid architecture, scalable reinforcement learning, coverage of 201 languages and dialects, and a next-generation training infrastructure.

Verified sourceRepository opened Feb 24, 2026Source checked 9/23/2026Version: 59d61f3c
LOCALRENTED GPUOPEN WEIGHTS
Freshness 41 out of 100. Repository age: 233 days old, Feb 24, 2026. Measures age, not quality.
Popularity 83 out of 100. 1,484,223 downloads in 30 days; 1,534 lifetime likes. Checked Oct 15, 2026. Measures adoption, not quality.
Arena Open Source Web coding: rank 51 of 55. Ranked model: qwen3.5-35b-a3b. Export dated Oct 13, 2026. Source checked Oct 15, 2026. Human preference ranking, not hardware suitability.
Category rankings & sources

Each position belongs to a separate Arena category. These are human preference rankings—not hardware recommendations. We do not combine the categories into one score.

Web coding#51of 55 open models
qwen3.5-35b-a3balibabaLicense: Apache 2.0
Export dated Oct 13, 2026Checked: Oct 15, 2026Overall board: #129
View official Web coding board
Text#60of 225 open models
qwen3.5-35b-a3balibabaLicense: Apache 2.0
Export dated Oct 13, 2026Checked: Oct 15, 2026Overall board: #169
View official Text board

Rankings reflect the dated export; live Arena boards can change. Open-source positions use Arena’s open-model filter. The overall position also includes the other models on that category board. Availability of weights and license terms still need to be checked separately.

What do these scores mean?

Two ways to compare models. Neither score measures answer quality, benchmark performance, or whether a model fits your machine.

Freshness · release age

A new release starts at 100. The score halves every 180 days: 100 × 2^(−age in days / 180). When the release date is unavailable, a repository date is a fallback—not proof of when the model was released. Missing dates stay unscored.

Check the date source
Popularity · adoption

Downloads over the past 30 days carry 80% of the score; lifetime likes carry 20%. Both use a logarithmic scale, capped at 10 million downloads and 100,000 likes. If likes are unavailable, downloads carry all the weight. Missing download statistics stay unscored.

round(80 × min(1, log10(1 + downloads) / 7) + 20 × min(1, log10(1 + likes) / 5))

Check the statistics source
Scores as of Oct 15, 2026. Statistics checked Oct 15, 2026. Statistics are marked for refresh after 72 hours.Full scoring method

At a glance

Parameters
35B total · 3B active
Architecture
Mixture of experts
Context length
262,144 native
License
Apache 2.0
Disk space
24 GB
Software
Ollama, MLX, vLLM, SGLang
View the model source
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Best for

  • Image-text-to-text generation
  • Multimodal reasoning and visual understanding
  • Coding tasks
  • Agent-based applications
  • Multilingual and cross-lingual generation across 201 languages and dialects

Tradeoffs

  • No performance benchmarks, evaluations, or numerical hardware requirements are provided in the source facts.
  • The source does not include information on licensing terms beyond Apache-2.0.
  • Actual compatibility may depend on the specific inference framework and version; no detailed compatibility testing is described.
  • No testing or validation of the model is claimed by the source facts.
  • This summary is limited to the supplied model facts and does not infer any missing details.

Ways to run it

vLLM · Linux

Repository-specific example documented in the publisher model card; confirm dependencies and platform in the source.

vllm serve Qwen/Qwen3.5-35B-A3B --port 8000 --tensor-parallel-size 8 --max-model-len 262144 --reasoning-parser qwen3
Official instructions

Transformers · See official guide

Repository-specific example documented in the publisher model card; confirm dependencies and platform in the source.

Official instructions

Explore its uses

SAFETENSORSIMAGE-TEXT-TO-TEXTCODINGAGENTSREASONINGMULTIMODAL

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Code generation, repair, and reasoning in a compact open-weight coding model.

Freshness 5 out of 100. Repository age: 758 days old, Sep 17, 2024. Measures age, not quality.
Popularity 83 out of 100. 1,597,653 downloads in 30 days; 831 lifetime likes. Checked Oct 15, 2026. Measures adoption, not quality.
MODEL SIZE7.61B
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Freshness 36 out of 100. Repository age: 265 days old, Jan 23, 2026. Measures age, not quality.
Popularity 66 out of 100. 74,916 downloads in 30 days; 451 lifetime likes. Checked Oct 15, 2026. Measures adoption, not quality.
MODEL SIZE119B total · 6.5B active
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Qwen3-Coder-Next · 80B A3B

Qwen3-Coder-Next is an open-weight text-generation model from Qwen/Qwen3-Coder-Next, licensed under apache-2.0. It is designed specifically for coding agents and local development. The model has 80B total parameters with 3B activated parameters, a 262,144-token native context length, and a hybrid architecture combining Gated DeltaNet, Gated Attention, and Mixture-of-Experts layers. It supports only non-thinking mode and does not generate thinking blocks.

Freshness 37 out of 100. Repository age: 258 days old, Jan 30, 2026. Measures age, not quality.
Popularity 79 out of 100. 588,526 downloads in 30 days; 1,696 lifetime likes. Checked Oct 15, 2026. Measures adoption, not quality.
MODEL SIZE80B total / 3B active
Qwen3.5 · 35B A3B: hardware, VRAM & setup | YouRunAI