explainx / blog
Jie Tang asked what GLM-5.3 must include. 3,400+ replies, vision dominates — screenshots, PDFs, UI designs. GLM-5.2 tops coding benchmarks; multimodal is the gap vs Opus 4.8.
Jun 15, 2026
The U.S. pulled Fable 5 on June 12. Within 48 hours, two Chinese labs had released models that beat it on key benchmarks — fully open source, at a fraction of the cost. Here is what GLM-5.2 is, what it can do, and what the timing means.
Jun 29, 2026
Google DeepMind's Gemma 4 31B hits 1,851 TPS on Cerebras — first multimodal model at wafer-scale speed. Haiku 4.5-class intelligence, 18× faster, public preview now.
Jun 19, 2026
Kilo Code pitted GLM-5.2 against Claude Fable 5 on a genuinely hard planning task — turning vague requirements into a spec another model can build without guessing. The result: Fable scored 9.1, GLM-5.2 scored 9.0. Both made the same architectural decisions. One costs roughly a tenth of the other.
Update — July 1, 2026: Zhipu has not announced GLM-5.3 shipping dates or confirmed vision will launch. Community poll remains the best public signal on roadmap priorities. GLM-5.2 guide · Fable 5 status. Last updated: July 1, 2026.
On June 29, 2026, Jie Tang — Tsinghua professor and founder of Zhipu AI / Z.ai — posted a simple question on X:
"Any new features we must have in the next version of glm?"
The thread hit 466,000+ views, 3,400+ likes, and 1,400+ replies within days. The answer was not subtle: vision.
While GLM-5.2 has closed much of the text-only gap with proprietary frontier models — topping open-source coding benchmarks like SWE-bench Pro at 62.1% in Zhipu's reporting — it remains text-in, text-out. Developers building agent workflows around screenshots, PDFs, and UI mockups still pipe images through Qwen-VL or similar before forwarding descriptions to GLM.
That two-hop workflow works. It is also exactly what the community wants GLM-5.3 to eliminate.
| Priority | Community ask | Why it matters |
|---|---|---|
| #1 Vision | Native screenshots, PDFs, UI designs, error messages | Opus 4.8 multimodal; GLM-5.2 text-only |
| Shorter thinking | Reduce default reasoning loop length | Speed + cost for agent harnesses |
| Smaller variants | Qwen-style 27B–35B MoE runnable on normal hardware | GLM-5.2 frontier scale excludes many self-hosters |
| Inference stack day-one | Official llama.cpp, vLLM, SGLang support at release | Sentdex and others tired of community trial-and-error ports |
| Computer use | Agent sees and interacts with UI | Matches Claude computer-use trajectory |
| Math + research | Papers with figures/charts (Jeremy Howard's ask) | Vision + reasoning for scientific workloads |
Zhipu has not confirmed GLM-5.3 features or a release date. This is community signal, not a product announcement.
GLM-5.2 shipped June 13, 2026 — one day after the US export-control suspension of Fable 5. Tang framed that release partly as proof that "frontier intelligence belongs to everyone" when Washington cut global access to Anthropic's top models.
Three weeks later, GLM-5.2 sits in a strange position:
The June 29 poll is Zhipu closing the loop with users before the next major version — a community-driven roadmap ask at the moment text-only GLM is strongest.
Zixuan Li (@ZixuanLi_, Z.ai lead) replied: "Looks like 'vision' is taking over the comment section." He invited replies on specialized capabilities beyond vision and token efficiency — but vision clearly won the thread.
Yum (@yuhasbeentaken) summarized the workflow pain:
"glm needs vision — we want glm to understand screenshots, pdfs, ui designs and error messages without sending them through a second model first. right now, the best workflow is running screenshots through qwen vision and forwarding the descriptions to glm. that works... but..."
That describes the hybrid pipeline many Fable 5 alternative teams run today:
Screenshot / PDF → Qwen-VL (describe) → GLM-5.2 (reason + code) → output
Teortaxes (@teortaxesTex) pushed further: "Good vision that's integrated with reasoning, to actually be a plug and play Opus replacement" — plus cutting error rates and shortening reasoning loops with more RL.
Jeremy Howard (@jeremyphoward): "Vision. Then we can read papers, math texts, etc, and the model can see the figures and charts too."
Saïd Aitmbarek, Arunoda Susiripala, Sentdex, and dozens of others echoed vision in one word.
zR (@zRdianjiao): multimodal and "shorter thinking length comes up a lot too."
0xSero: "Vision + reducing thinking length."
GLM-5.2's extended reasoning modes help hard tasks but burn tokens and latency in agent loops. Harness builders want adaptive thinking — deep when needed, fast by default. Sajad (@neuralbroker) listed "adaptive thinking modes instead of fixed effort" on a structured wishlist that also included 1M-token context with less degradation and stronger long-horizon agents.
Belcebuu (@Belcebuu1) asked for Qwen-style smaller MoE variants — models people can run "without spending 20k in Macs with 512mb or 4 dgx spark."
GLM-5.2 frontier scale is a strength for API users and a barrier for sovereignty-minded self-hosters comparing against Kimi K2.7 and Qwen downloads.
Sentdex (@Sentdex) — "Loving glm5.2" — asked that release features be PR'd into llama.cpp, vLLM, and SGLang officially instead of leaving ports to community trial-and-error.
That matters for teams following how to run GLM-5.2 on agent harnesses and Unsloth local setups. Vision adds weight-format complexity — official stack support at launch reduces the gap between weights drop and production inference.
Not every reply was a feature ask. Jesse Busma reported 429 errors all week on Z.ai's max coding plan with support declining refunds — a reminder that benchmark leadership ≠ API reliability. Vision hype does not fix capacity planning.
BridgeBench replied on Tang's thread:
"GLM 5.2 is not better than Claude Opus 4.8. Open source is a lot closer than it used to be though."
That framing matches hands-on developer rankings in our GLM-5.2 vs Fable 5 analysis:
Fable > Kimi-K2.7 > Opus-4.8 ≈ GLM-5.2 > GPT-5.5
GLM-5.2 beats Fable on BridgeBench Reasoning in vendor-reported suites and matches Opus on broad coding for many workloads — but Opus still leads holistically, especially where multimodal context matters.
Vision in GLM-5.3 is not a nice-to-have emoji feature. It is the feature that turns GLM from "best text-only open coder" into "Opus-class agent backbone" for the screenshot-heavy workflows that define modern coding agents.
Until GLM ships native vision, the community's pragmatic stack looks like this:
| Step | Model | Role |
|---|---|---|
| 1 | Qwen-VL (or similar) | OCR screenshots, describe UI, parse PDF pages |
| 2 | GLM-5.2 | Reason, plan, write code, tool-call |
| 3 | Harness (Claude Code patterns, OpenCode, etc.) | Orchestrate |
Cloud alternative (June 29): Gemma 4 31B on Cerebras runs native multimodal at 1,851 TPS — single-model vision + reasoning at wafer-scale speed, Apache 2.0.
Failure modes in the bridge:
GLM-5.3 with vision integrated into the same reasoning stack collapses steps 1–2 — the same architectural move proprietary labs made years ago with GPT-4V and Claude 3.
Day 19 (July 1, 2026): Fable 5 remains offline globally. Leaked Claude app strings suggest US restore may require ID verification + usage credits — not a global reopening.
International developers already routing around Fable use:
GLM-5.3 with vision would address the multimodal agent slice those stacks still hand off to US models or Qwen-VL bridges — without export-control friction.
Zhipu also reported GLM matching Claude Mythos on security benchmarks in late June. Vision + security + coding in one open stack is the combination enterprise security teams watching the Fable ban have been asking for.
Based on community signal and Zhipu release patterns, plausible GLM-5.3 directions:
| Feature | Confidence | Rationale |
|---|---|---|
| Native vision encoder | High | Overwhelming poll dominance |
| Shorter default thinking | Medium-high | Repeated across replies |
| Smaller GLM-5.x variant | Medium | Hardware accessibility pressure |
| Official vLLM/SGLang day-one | Medium | Sentdex + enterprise self-host demand |
| Computer-use tooling | Medium | Multiple explicit asks |
| 1M context quality pass | Low-medium | Niche but vocal researchers |
Do not treat this table as a roadmap. Zhipu may prioritize internal benchmarks over poll winners, or ship vision in a separate GLM-V line as Qwen does with Qwen-VL.
Keep the Qwen-VL → GLM-5.2 bridge until GLM-5.3 ships. Document where description loss hurts you — that list becomes your eval suite when vision drops.
GLM-5.2 on Z.ai API or local via Unsloth already covers most Fable-class coding without vision.
Watch Zhipu's GitHub and Hugging Face orgs for weight drops. Vision models are heavier — plan HBM and VRAM accordingly before GLM-5.3 lands.
Monitor 429 capacity reports. Benchmark leadership does not guarantee inference headroom during viral demand spikes.
Is GLM-5.3 confirmed?
No. Jie Tang asked; the community answered vision. Zixuan Li acknowledged it. Nothing in the thread is a release commit.
Does the poll matter?
Yes. Zhipu has historically shipped community-aligned features in the GLM line. When the founder posts a 466K-view thread and vision wins by landslide, ignoring it would waste free product research.
Will vision make GLM an Opus replacement?
Partially. Text-only GLM-5.2 is already close on coding and reasoning. Integrated vision + shorter thinking + reliable API is the remaining triangle for plug-and-play agent replacement — exactly what Teortaxes, Sentdex, and hundreds of replies asked for.
Community poll data reflects Jie Tang's June 29, 2026 X thread and replies as summarized through July 1, 2026. Zhipu AI has not officially confirmed GLM-5.3 features or timing.