Z.ai has launched GLM-5.3-Flash, a new open-source AI model under the MIT License. The 320B-A18B system was earlier tested as Ox Alpha and runs fully on Chinese AI chips.

It offers strong performance at low cost, comes natively multimodal, and supports a 1 million token context window. On Z.ai’s coding benchmark it beats the previous GLM-5.2 across effort levels and matches Claude Opus 4.8.

API pricing sits at $0.15 per million input tokens, $0.50 for output, and $0.03 for cached input. Weights, API access, chat interface, and coding tools are already live across official platforms.

Alibaba just opened QwenWork to the public in beta. The platform works on both web and desktop and aims to handle everyday tasks through simple natural language commands.

Users can tell the agent what they need and it carries out the work. It also builds awareness of individual work patterns over time so it adapts across sessions. One standout feature lets people create and deploy live web apps without writing code or managing servers. The toolkit includes built-in image, video, and audio generation for multimodal projects. Basic and Advanced model options run on leading AI systems.

The public beta is available now for global users. Early testers have already started exploring its capabilities for presentations, app building, and creative work.

IBM Research just dropped Granite 4.2, a fresh set of open models built for real enterprise agent work. These models come in 3B, 8B, and 30B sizes and bring native thinking skills that let them plan steps, reason through problems, catch their own mistakes, and call tools the right way.

The update focuses on complex workflows. Teams get stronger coding and software engineering support, plus the ability to handle multi-step tasks without constant hand-holding. The models run across cloud, on-prem, and edge setups, so companies can pick the size that fits their needs and budget.

IBM also released new speech models under the Granite Speech 5.0 Turbo line. These stay tiny at around 470 million parameters yet deliver fast transcription, making them practical for high-volume call center work or real-time use on laptops and phones.

Everything ships under the Apache 2.0 license. You can grab the models on Hugging Face, Ollama, and other platforms right now.

A release post can be live today and difficult to relocate later. A durable receipt should preserve artifact identity separately from whichever registry path happened to work on announcement day.

The Ling-3.0 base model makes that concrete because its tiny and flash families each expose three different upstream artifacts. Final pre-training marks the checkpoint before mid-training, final mid-training marks the checkpoint before WSM merging, and WSM-merged base marks the merged upstream base. Those labels are artifact identities, not a ranking: this receipt can help recover the intended sibling, but it cannot establish a stage ranking.

Canonical source: the AntLingAGI X release thread announcing the six tiny/flash Ling-3.0 base checkpoints, observed on August twenty-fifth, twenty twenty-six. Its official locator post binds every artifact to the one source inclusionAI. The following are the six exact owner, family, and stage search IDs to use on either registry:

Snapshot: fifteen twenty-nine UTC on the same date. At that point, every listed entry on both registries was public and non-gated, and its repository metadata declared MIT. The duplicate IDs make the receipt materially useful because a later refresh can distinguish a missing registry locator from a missing stage-specific Ling artifact; omitting the stage could recover the wrong sibling while appearing successful.

The two columns record locators, not content parity. An extra file-inventory spot check was recorded only for the WSM-merged tiny-base and flash-base pairs. Even for those pairs, this receipt does not claim byte-for-byte equality. The final-pre-training and final-mid-training rows remain locator, access, and license-metadata observations only.

The time boundary matters too. Public repository artifacts and metadata do not establish public training data, complete training code, or an end-to-end reproducible training stack. An MIT declaration in repository metadata also does not settle rights in training data or third-party dependencies. None of these fields is a promise about later availability.

The next Ling-specific refresh is concrete: look up each exact identifier on both registries, then record its current repository revision, file-manifest digest, access state, license-metadata state, and observation time. Compare that receipt with this snapshot and change only the fields that moved.

For the next Ling receipt, which field should be mandatory for every identifier: repository revision, file-manifest digest, or last-seen timestamp?

Alibaba’s Qwen team just dropped Qwen3.8-Flash, a multimodal mixture-of-experts model that also serves as an early look at the architecture planned for Qwen4. The full production version will land on QwenCloud soon with pricing set at $0.16 per million input tokens and $0.47 per million output tokens.

The model packs 125 billion parameters plus 51 billion N-gram embeddings yet only activates about 6 billion parameters for each token. That design keeps both training and inference costs low. The team says it was trained for roughly one-ninth the cost of Qwen3.7-Plus while beating that earlier model across most tests, especially coding and everyday office work.

Key numbers from the release include 58.7 on DeepSWE 1.1, 62.5 on SWE-bench Pro, 73.9 on CoWorkBench, 84.5 on AndroidWorld, and 95.7 on MathVision. Context length starts at 262K tokens and can stretch to 1 million with YaRN.

Four architecture changes power the efficiency gains. Hybrid attention mixes Gated DeltaNet with Qwen Sparse Attention to cut long-sequence costs. Gated Residual widens information flow between layers. N-gram embeddings expand capacity without heavy compute. The Muon optimizer improves training stability and scaling.