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Core content summary

Kingy.AI's release-day rundown of GLM-5.3's specs and usage recommendations. The least glamorous but most important fact: this is not a new base model—it shares the GLM-5.2 base, and its gains come from another month of post-training (more executable environments, a greater variety of long-horizon tasks, and more RL compute).

Verdict

• GLM-5.3 is a serious coding and Agent upgrade, especially on tests where 5.2 had room to grow; every 5.2→5.3 comparison row in the release table shows an improvement, with strong consistency in direction.

• Caveat: "Public benchmark" only means that the task set/evaluation project is public; it does not mean that GLM-5.3's score was independently run. The independent DeepSWE leaderboard had not yet included 5.3 at publication; Terminal-Bench 3.0 is public, but Z.ai used its own Claude Code configuration + 3 rollouts per task + relaxed limits; Z.ai Code Bench remains private.

• Kingy.AI did not run inference tests (there was no free trial of the exact model at publication, and it did not use paid credentials).

Release-day recommendations (5 points from the original)

1. Use the API or Coding Plan to try GLM-5.3 for your target coding Agent work.

2. Use max effort for difficult repository tasks; use low when latency and token use matter.

3. Migrate any direct API request that disables thinking before changing the model ID.

4. Do not budget from GLM-5.2 token prices, and do not plan a local deployment until Z.ai publishes the missing 5.3 artifacts.

5. Watch for the weights to be released two weeks later.

Specs table (release-day status)

• Developer: Z.ai (the international name, formerly Zhipu AI); model ID: glm-5.3.

• Base: same as GLM-5.2; architecture/parameter count: not newly documented for 5.3 (do not treat the 5.2 numbers as confirmation in the 5.3 model card).

• Modality: primarily a text coding model at release; context: Coding Plan documents a 1M route, while Claude Code uses the glm-5.3[1m] suffix + a 1,000,000-token compression window.

• Reasoning: low/high/max (max by default); thinking: required for direct API requests (thinking.type:"disabled" is unsupported).

• Standard API: available (an OpenAI-compatible chat-completions endpoint, model glm-5.3).

• Coding Plan: available on all Lite/Pro/Max tiers; ZCode available.

• Weights/model card/license/local deployment/standard API pricing: all undecided/not released as of publication day—"it is not valid to infer 5.3's terms from GLM-5.2's MIT license."

• Calling it "open source" on release day is premature: the correct wording is "API available, open-source weights committed."

Recalculation of the "50% improvement" (the original verifies the math)

• High tier: GLM-5.3 31.4% vs GLM-5.2 20.9% → (31.4−20.9)/20.9 × 100 = 50.2% relative improvement, an absolute improvement of 10.5 percentage points.

• Max tier: 34.5% vs 23.4% → absolute +11.1 points, relative +47.4%; average output tokens fell from approximately 96,000 to approximately 75,000 (−21.9%).

• In other words, "50%" is a relative percentage (relative improvement), not an absolute score difference; it is still a private-vendor benchmark (evaluated in Claude Code 2.1.207), with the tasks, grader, and run artifacts undisclosed.

• Closed-source comparison: Fable 5 still leads on the Max tier (39.5% vs 34.5%); on the High tier, 5.3's 31.4% exceeds Opus 4.8's 29.5%, while using less than half its output tokens.

Complete 5.2→5.3 delta table (partial; recalculated from the official release table)

Key quotes from the original

"Use max effort for difficult repository tasks; use low when latency and token use matter."

"Migrate any direct API request that disables thinking before changing the model ID."

"Do not budget from GLM-5.2 token prices or plan a local deployment until Z.ai publishes the missing 5.3 artifacts."