GLM-5.3-Flash
320B-A18B, 1M context, MIT weights
Z.ai shipped it on August 26 — it is the model that ran anonymously on OpenRouter as Ox Alpha. It costs a tenth of GLM-5.3, but it is not a "Flash you can run on a laptop": 320B total parameters, 181GB minimum to host locally.
Four numbers that matter
Parameters
320B total, 18B active MoE with hybrid sparse and linear attention. "Flash" refers to inference cost, not model size.
Context window
Official and Z.ai docs both state 1,048,576 with 131,072 max completion. ⚠️ Artificial Analysis lists 400K — the two disagree.
Launch price (50% off)
List is $0.15 / $0.50 per million tokens; the half-price window closes 2026-09-09 24:00 (UTC+8). Cache read $0.03 ($0.015 during the promo).
AA Intelligence Index
Same tier as Claude Opus 4.8. ⚠️ Not the Fable 5 tier — the 80% figure that circulated came from a 10-task sample.
What GLM-5.3-Flash is
The first natively multimodal model in Z.ai's GLM-5 line, released on 26 August 2026 with open weights under MIT (already on Hugging Face). It is a 320B-total / 18B-active MoE using hybrid sparse and linear attention, accepting text, image and video input, producing text, with tool calling and visual coding. Before launch it spent a week on OpenRouter under the codename Ox Alpha; Z.ai says that week ran entirely on domestic AI accelerator clusters, served roughly 100 trillion tokens a day, and that their in-house SGLang-based engine delivered about a 3x end-to-end speedup.
What happened in the first 24 hours
On reveal day OpenRouter pulled the anonymous slug stealth/ox-alpha from its production catalog and replaced it with z-ai/glm-5.3-flash, ending the free preview. Novita and AIHubMix listed it the same day; SiliconFlow, Fireworks and Together had not followed as of 27 August. Three questions dominate the discussion: what the real benchmark number is, how to access it now that the free endpoint is gone, and whether it can be run locally. The third is by far the loudest thread on r/LocalLLaMA — the "Flash" name led many to assume a small model.
Timeline
GLM-5.3 ships (same base, post-training only); API goes live 19 August at GLM-5.2 pricing.
An anonymous model codenamed Ox Alpha appears on OpenRouter and OpenCode as a free preview.
Z.ai reveals Ox Alpha is GLM-5.3-Flash, opens the weights, ships the API, and the anonymous slug is retired.
Confirmed vs commonly misread
Confirmed
Release date, the 320B-A18B architecture, MIT licensing with weights on Hugging Face, $0.15/$0.50 list pricing and the 2026-09-09 promo deadline, the AA Intelligence Index of 57, and the Ox Alpha identity — all verifiable in official announcements or vendor documentation.
Commonly misread
The "DeepSWE 80%" figure came from a 10-task sample during the stealth period. Official DeepSWE v1.1 is 63.4%; a 113-task community single run landed around 58.4%. "Comparable to Fable 5" does not hold either — an AA index of 57 puts it in the Opus 4.8 tier. And "it's called Flash so it must run locally" is wrong: this is a 320B-parameter model.
The other side of being 40x cheaper
Input and output really are cheap
At the promo rate of $0.075/$0.25 it is roughly a twentieth of GLM-5.3 and a fortieth of Claude Opus 4.8. For short turns and batch work the cost advantage is real.
Cache reads are not
Cache read is $0.03 ($0.015 during the promo) — one developer measured that at about 4x DeepSeek V4 Flash's off-peak rate. For agent loops that replay a long context repeatedly, cache charges can outweigh the headline saving. Run the numbers against your own cache hit rate first.
How to use it
The official model code is glm-5.3-flash, served over Z.ai's or bigmodel.cn's OpenAI-compatible endpoint. Third-party ids differ: OpenRouter uses z-ai/glm-5.3-flash, Novita uses zai-org/glm-5.3-flash. For local hosting, budget the memory first: FP8 weights are about 306 GiB, BF16 about 585 GB, and the smallest NVFP4 quant currently around 181 GB, with Unsloth's GGUF still marked work-in-progress. If you drive it from a client like Claude Code, note that suffixes such as [1m] are client-side markers that your gateway must handle rather than pass upstream verbatim.
On QCode
The Z.ai models currently routable on QCode are GLM-5.3, GLM-5.2 and GLM-5.1, billed at official rates times a service multiplier, switchable from one key on one endpoint. GLM-5.3-Flash shipped on 26 August and is not connected yet — this page is reference material and does not claim the model is callable on QCode. If you want to run a Chinese model today, start with GLM-5.3 or the DeepSeek V4 family.
FAQ
Is GLM-5.3-Flash free?
No. What was free was the stealth/ox-alpha endpoint during the anonymous preview, and that slug was removed from OpenRouter's production catalog on reveal day with no alias. There is no :free endpoint for it on OpenRouter either — the one with a free tier is the previous-generation GLM-5.2. New accounts on Z.ai's platform get trial credit, which is not the same as permanently free.
Is the context 1M or 400K?
Official and Z.ai documentation both say 1,048,576 with a 131,072 max completion. Artificial Analysis lists 400K and marks the model proprietary, which contradicts the MIT release. The two sources disagree; the likely explanation is that AA captured a serving-side limit. Treat the official docs as authoritative, but if you depend on very long context, measure it on the specific provider you plan to use.
Is DeepSWE 63.4% or 80%?
Official DeepSWE v1.1 is 63.4%. The 80% figure came from a single 10-task run during the stealth period and did not reproduce at larger sample sizes; a 113-task community run scored about 58.4%. Use 63.4% when comparing models.
Can I run it locally?
Only with serious memory. FP8 weights are about 306 GiB, BF16 about 585 GB, and the smallest community NVFP4 quant is around 181 GB. A single consumer GPU is out of the question; a 256GB unified-memory workstation is the most discussed option, and Unsloth's GGUF quant is still listed as in progress. For most people the API is the cheaper path.
GLM-5.3 or GLM-5.3-Flash?
GLM-5.3 is the same-generation flagship post-train and is stronger on complex coding. Flash is the low-cost tier of that generation at roughly a tenth of the price, and it adds native multimodality. Use Flash for batch work, cost-sensitive workloads and anything involving images; stay on GLM-5.3 for long-horizon coding. In Z.ai's Coding Plan, Flash quota is roughly 3x that of GLM-5.3.
When does the discount end?
2026-09-09 at 24:00 (UTC+8). After that it returns to list price: $0.15 input / $0.50 output per million tokens, with cache read going from $0.015 back to $0.03.
Sources
Z.ai and bigmodel.cn official documentation, the Z.ai launch blog and official social announcements (2026-08-26), OpenRouter and Novita model pages, the Hugging Face model card, the Artificial Analysis model page, and r/LocalLLaMA and V2EX community threads (captured 2026-08-26 to 27).
Chinese models, one key for all of them
GLM-5.3, DeepSeek V4, Kimi K3 and Qwen switch on the same QCode endpoint at official rates times a service multiplier — no separate top-up on each platform.
Related reading
GLM-5.3 complete guide
Capabilities, pricing and access for the same-generation flagship.
GLM-5.3 vs GLM-5.2
What the post-training actually bought.
AI Model Radar 2026
Pricing and availability tracking across every family.
Figures are a 2026-08-27 snapshot and change as vendors update. QCode is not affiliated with Z.ai. GLM-5.3-Flash is not connected on QCode.