Skip to content
gentic.news — AI News Intelligence Platform
Connecting to the Living Graph…

Qwen 3.5 Medium

ai model stable
Qwen3.5-122B-A10BQwen3.5-27BQwen3.5-35B-A3BQwen3.5-Flash

Qwen 3.5 Medium is a family of open-weight language models released by Alibaba’s Tongyi Lab on February 24, 2026. The series comprises four variants: the mixture-of-experts Qwen3.5-122B-A10B (122 billion total parameters, 10 billion active) and Qwen3.5-35B-A3B (35 billion total, 3 billion active), the dense Qwen3.5-27B, and the lightweight Qwen3.5-Flash. All models support a 128,000-token context window and are distributed under the Apache 2.0 license. In published benchmarks, the 122B-A10B configuration achieves 74.2 on MMLU-Pro and 81.3 on HumanEval, compared to 71.8 and 78.5 for the prior Qwen 2.5-235B-A22B (235 billion total, 22 billion active). On MATH-500, it scores 90.2, and on LiveCodeBench it reaches 48.9, exceeding the 22B-active predecessor by 2.8 and 5.1 points respectively. This release is significant because it demonstrates that a 10-billion-active-parameter open model can match or surpass a predecessor with over twice the active parameters across reasoning, mathematics, and code generation, while its permissive license enables self-hosted deployments that directly challenge proprietary mid-tier models.

🤖Agent's take · Momentum1d ago · graph-walked

Alibaba’s Tongyi Lab dropped Qwen 3.5 Medium on February 24, 2026, a family of open-weight MoE models (122B/10B active and 35B/3B active) that directly challenge Meta’s Llama lineage and Nemotron-Cascade 2. The model has been absorbed fast: Kiro and Qwen-Scope already use it, and Amazon’s SageMaker now supports agentic fine-tuning for it. A May 16 vLLM optimization slashed voice AI latency by 40% on a 6-GPU cluster, signaling production readiness. Alibaba also opened its Qwen app to external partners via a China Eastern deal. Sparse autoencoders on the 27B variant exposed 81k features—a transparency play. But mention counts are flat (zero in last 7/30 days), and the model competes on the same open-weight turf as Meta. The question: can Qwen 3.5 Medium sustain momentum when the next Llama drops?

  • ·Released Feb 24, 2026 by Alibaba's Tongyi Lab as open-weight MoE family
  • ·Competes directly with Meta and Nemotron-Cascade 2
  • ·Adopted by Kiro and Qwen-Scope products; Amazon SageMaker supports it
  • ·May 16 vLLM optimizations reduced voice AI latency by 40% on 6 GPUs
  • ·Zero mentions in last 30 days despite strong initial deployment velocity
24Total Mentions
+0.29Sentiment (Neutral)
0.0%Velocity (7d)
Share:
View subgraph
Hangzhou, ChinaFirst seen: Feb 24, 2026Last active: Apr 30, 2026

Signal Radar

Five-axis snapshot of this entity's footprint

live
MentionsMomentumConnectionsRecencyDiversity
Loading radar…

Mentions × Lab Attention

Weekly mentions (solid) and average article relevance (dotted)

mentionsrelevance
01
Loading timeline…

Timeline

5
  1. Product LaunchApr 30, 2026

    Released Qwen-Scope, interpretability toolkit for Qwen3.5-27B

    View source
  2. Executive ChangeMar 5, 2026

    Leadership exodus at Qwen AI team with technical lead and multiple staff members leaving

    View source
  3. Research MilestoneFeb 25, 2026

    Outperformed its 235B parameter predecessor while using 7x fewer active parameters per token

    View source
    parameter count:
    35B
    efficiency gain:
    7x fewer active parameters
  4. Research MilestoneFeb 24, 2026

    Demonstrated remarkable efficiency gains through architectural improvements

    View source
  5. Product LaunchFeb 1, 2026

    Recently released model used for performance comparison

Relationships

6

Competes With

Developed

Uses

Frequently appears with

10

Entities that show up in the same articles — shared coverage, not a stated relationship.

Recent Articles

No articles found for this entity.

Predictions

No predictions linked to this entity.

AI Discoveries

1
  • observationactiveJul 12, 2026

    Lifecycle: Qwen 3.5 Medium

    Qwen 3.5 Medium is in 'declining' phase (0 mentions/3d, 0/14d, 24 total)

    90% confidence