Z.ai launched GLM-5.3-Flash on August 26, 2026, as a 320-billion-parameter mixture-of-experts model that activates 18 billion parameters per token and ships with MIT-licensed weights on Hugging Face from day one. The supplied excerpt reproduces Z.ai's announcement text stating it is the first natively multimodal model The article frames GLM-5.3-Flash as not a distilled version of the flagship, noting it is built on a new base rather than post-trained from GLM-5.2. API pricing is reported at $0.15 per million input tokens and $0.50 per million output tokens, compared with $1.40 and $4.40 for GLM-5.3, and the author critiques Z.ai's b

The cheapestinference technical profile (August 31, 2026) compiles GLM-5.3-Flash's headline specifications and applies an independent benchmark lens via Artificial Analysis: the model lands at 57 on the AA Intelligence Index at a blended $0.10 per million tokens ($0.09 per Index task) and sits on AA's intelligence-vs-c For context, the profile reports a 1M-token context window (up to 128K output), reasoning always on for the direct API, MIT-licensed weights on Hugging Face in BF16 and FP8, list pricing of $0.15 input / $0.50 output per million tokens (cached input $0.03) with a 50% launch promo through September 9, 2026, and measured

AI Intel Report's August 27, 2026 launch coverage corroborates the core GLM-5.3-Flash release facts with specific architecture numbers: Z.ai introduced the model on August 26, 2026 as the first natively multimodal entry in the GLM-5 series, equipped with 320 billion total parameters, 18 billion activated parameters, an The article also reconstructs the pre-launch "ox-alpha" testing phase, reporting that Z.ai deliberately deployed the model anonymously on OpenRouter and OpenCode to measure real-world usage without brand-influence bias, with traffic during that phase running entirely on Chinese AI chips to demonstrate ecosystem compati

This comparison explicitly covers GLM-5.3-Flash alongside its sibling GLM-5.3. Per the supplied excerpt, GLM-5.3-Flash is a 320B-total/18B-active model from Z.ai with a 1M-token context, native multimodal input, and reasoning plus tool-use capabilities. It scores 57 on the Artificial Analysis Intelligence Index versus The piece argues that GLM-5.3-Flash is not a trimmed GLM-5.3 but a different model, and that despite a slower generation speed it is the better value for most high-volume applications and multimodal agents. It notes Flash is the only one of the two with publicly available weights, offering 3x usable quota on the Coding

Z.ai's alphaXiv paper introduces GLM-5.3-Flash as the first natively multimodal model in the GLM-5 series, a 320B-total / 18B-active Mixture-of-Experts model that combines sparse and linear attention in a hybrid architecture and adds Manifold-Constrained Hyper-Connections (mHC). It was trained on a 30T-token multimodal The paper documents standard API pricing of $0.15 per million input tokens, $0.50 per million output tokens, and $0.03 per million cached input tokens, and reports an Artificial Analysis Intelligence Index v4.1.1 score of 57 at $0.045 per task. Z.ai states the model outperforms GLM-5.2 across benchmarks and real-world

A Hugging Face community PR opened on August 26, 2026 (and merged approximately six days later) by user SaylorTwift extracts evaluation results from the official zai-org/GLM-5.3-Flash model card's benchmark chart and adds them to the repository's .eval results/ directory. The PR records Terminal-Bench 2.1 at 84.3, Deep The PR is candid about provenance: the numbers were read visually from an embedded benchmark chart image (bench_53.png) in the README rather than reproduced via HF Jobs with inspect-ai, so no verification token accompanies them, and the cited arxiv:2602.15763 is identified as the original GLM-5 technical report from Fe

On August 26, 2026 at 7:42 PM, Z.ai (@Zai_org) publicly revealed GLM-5.3-Flash — the model previously appearing under the "ox-alpha" codename on OpenRouter/OpenCode — and the explainx.ai launch blog documents the reveal in near real time, citing the Z.ai tweet (740K+ views within an hour) and confirming that MIT-licens The explainx.ai piece also flags ecosystem context that supports the GLM-5.3-Flash release narrative: the stealth ox-alpha phase ran entirely on Chinese AI chips, the larger non-Flash GLM-5.3 missed its own August 28 open-weights staged-release target, and Unsloth shipped a Dynamic 3-bit GGUF sized to run on 128GB of R

Z.ai released GLM-5.3-Flash on August 26, 2026, as the first natively multimodal model in the GLM-5 series. Per the supplied excerpt, it is a 320B-parameter mixture-of-experts model that activates only 18B parameters per token, runs natively in FP8, and supports a 1,048,576-token context window. The architecture uses h The deep dive situates GLM-5.3-Flash against GLM-4.6, GLM-5.2, and the GLM-5.3 flagship, noting it is built from a newly trained base rather than a post-train of GLM-5.2. It reports that GLM-5.3-Flash beats GLM-5.2 across six coding and agentic benchmarks at roughly one-tenth of the price and approaches Claude Opus 4.8

Together AI's listing describes GLM-5.3-Flash as Z.ai's first natively multimodal model in the GLM-5 series, built around frontier coding and agentic capability at flash-level inference cost. It is a 320B-parameter Mixture-of-Experts model with only 18B active parameters, introducing a hybrid architecture that combines The listing highlights four capability pillars: frontier coding at flash cost with gains well beyond GLM-5.2, visual intelligence that extends validation from functional correctness to the actual interfaces users see, efficient long context via IndexPool that cuts attention compute 3.0x and KV cache 4.4x versus GLM-5.3