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LatestApodex1.1-mini
ApodexText / LLM

Apodex 1.1 mini targets long-horizon agentic work

An MoE model built on Qwen3.5-35B-A3B aims at complex, multi-step tasks rather than one-shot answers.

Aug 17, 2026
NotableOther
Apodex 1.1 mini

Apodex has published Apodex 1.1 mini, a text and reasoning model built on the Qwen3.5-35B-A3B base and released on Hugging Face. The project frames the model as an agentic system, tuned for long-horizon, multi-step tasks rather than quick single-turn responses.

The model uses a mixture-of-experts (MoE) design, an architecture that activates only a fraction of its parameters per token. That approach keeps inference costs closer to a smaller dense model while retaining a larger pool of specialized capacity — a common choice for teams that want reasoning ability without paying full price on every forward pass.

Why it matters

The pitch here is agentic durability: models that can hold a plan together across many steps, tool calls, and intermediate decisions. Much of the recent open-weights work has emphasized exactly this shift.

  • Built on the Qwen3.5-35B-A3B base, itself part of Alibaba's Qwen line
  • MoE architecture for efficiency at inference time
  • Positioned for reasoning and long-horizon task completion

Some details remain unspecified in the release record, including active parameter count and context length, and the model ships under an unspecified "other" license — worth checking before any commercial use. Supporting material is linked from the associated paper. As always with fast-moving open releases, independent evaluation will determine whether the agentic claims hold up in practice.

Sources

  • apodex/Apodex-1.1-mini

    Hugging Face

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OlderOpenMOSS Debuts MOSS-VL for Real-Time Vision InteractionOpenMOSS · Vision-Language · 2 months agoNewerAnt Research releases 4DAnyone for 4D human videoAntResearch · Image → Video · 2 months ago

Get the model

Hugging Face

Specs

Context window262K tokens
Size71.9 GB
PrecisionBF16
ArchitectureQwen3_5MoeForConditionalGeneration
LicenseOTHER
Downloads3.3K
Likes153

Can you run it?

Runs on a 24 GB GPU at 4-bit.

  • BF16 (as published)

    80 GB GPU (A100 / H100) · 128 GB Mac Studio

    73.6 GB
  • 8-bit

    48 GB GPU (RTX 6000) or 2×24 GB · 64 GB Mac

    39.8 GB
  • 4-bit

    24 GB GPU (RTX 3090 / 4090) · 32 GB Mac

    23.3 GB
Your machine
GGUF builds
  • Mixture-of-experts: every expert must be loaded, so memory follows total parameters, not the active slice.
  • KV cache sized for 8,192 tokens of context; longer prompts need proportionally more.

Based on Hugging Face weights metadata. Assumes an 8K context and ~1 GB runtime overhead; actual needs vary. How we estimate


Modalities

Text / LLMReasoning

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