GLM-5.3 uses the same base model as GLM-5.2 — every gain comes from post-training. Compared with GLM-5.2, it is much better at complex coding and long-horizon tasks: + Stronger Coding: GLM-5.3 is the most capable open-weights model for coding, with a 50% improvement over GLM-5.2 on our in-house Z.ai Code Bench. It also achieve open-source SOTA on public benchmarks including Terminal Bench 3.0 and Agents' Last Exam. + Emergent Cyber Capability: As we scaled post-training, cyber capability developed faster than we expected. GLM-5.3 is state of the art on CyberGym for vulnerability discovery, and its gains are largest further up the exploitation chain, where it more than doubles GLM-5.2 on exploitation benchmarks. GLM-5.3 supports deployment with the following frameworks. Feel free to try them out:
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View details ↗**Inferact/Kimi-K3-DSpark** is an **MLA-native DSpark** draft model that accelerates Kimi-K3 on **vLLM**, served through vLLM's native `dspark` speculative method. The draft trains on **target hidden states extracted from vLLM itself** — the same engine that serves it, so the numerics it learns from are the numerics it meets at inference. TorchSpec provides the loop, streaming those hidden states from live target inference into concurrent FSDP draft training. **DSpark** = a block-diffusion backbone of 5 dense layers with non-causal attention, drafting 7 tokens in a single parallel pass, a low-rank sequential **Markov head** supplying the intra-block dependency, and a confidence head for resource-aware scheduling. Mirroring Kimi-K3's own MLA attention means draft and target share one KV layout (a compact 576-element latent per token), so the draft's pages unify with the target's cache — KV offloading and P/D disaggregation work with no separate page format.
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View details ↗**Inferact/MiniMax-M3-EAGLE3** is an EAGLE3 draft model for accelerating inference of MiniMax-M3. It is served end-to-end with **vLLM** and was trained using **TorchSpec** — a torch-native online speculative-decoding training framework that runs FSDP training and vLLM-based target inference concurrently, learning from **MiniMax-M3-regenerated responses and live vLLM-generated hidden states** to match the base model's exact token distribution. The draft is a **1-layer** dense Llama (`LlamaForCausalLMEagle3`, ~3.3 B params) operating on MiniMax-M3's `hidden_size=6144` / `vocab_size=200064`; at serve time it shares the target's embedding and LM head (EAGLE3). See `config.json` for the full architecture. All numbers are measured end-to-end against `MiniMaxAI/MiniMax-M3-MXFP8` served with vLLM at `tensor-parallel-size=4`, `num_speculative_tokens=3`, and `--enforce-eager`. Greedy draft sampling (`topk=1`).
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View details ↗**Inferact/MiniMax-M3-EAGLE3-GQA** is a **grouped-query-attention (GQA)** EAGLE3 draft model for accelerating inference of MiniMax-M3, served with **vLLM** and trained with **TorchSpec**. It is **retrained on the same datasets** as the multi-head-attention version Inferact/MiniMax-M3-EAGLE3 — **kimi-mtp, OpenCodeInstruct, SWE-bench, and SWE-bench-Pro** — with the draft's attention changed from **MHA to GQA** (`num_key_value_heads: 64 → 4`) for **inference efficiency** (16× smaller draft KV cache) and **compatibility with the target model**. The draft is a 1-layer dense Llama (`LlamaForCausalLMEagle3`) on MiniMax-M3's `hidden_size=6144` / `vocab_size=200064`; at serve time it shares the target's embedding and LM head (EAGLE3). See `config.json` for the full architecture.
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View details ↗> [!Note] > This repository contains model weights and configuration files for the post-trained model in the Hugging Face Transformers format. > > These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, TokenSpeed, etc. > [!Tip] > For users seeking managed, scalable inference without infrastructure maintenance, the official Qwen API service is provided by Qwen Cloud. > > In particular, **Qwen3.8-Flash** is the official version based on Qwen3.8-Flash-Next with more production features, e.g., 1M context length by default, official built-in tools. For more information, please refer to the Qwen3.8-Flash Overview. As the frontier of foundation models pushes toward ever-larger parameter counts and ever-longer context windows, the question is no longer just how much we can scale, but how efficiently we can do so. Sustainable progress toward artificial general intelligence (AGI) that benefits everyone demands architectural innovation. Today, we are sharing a concrete step in that direction: Qwen3.8-Flash-Next.
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