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GLM-5.3 vs GLM-5.2
GLM-5.3 scores higher than GLM-5.2 on all 3 public leaderboards that rate both. On the ArtificialWatch Index, which takes the median of each model's percentile across the boards that score it, GLM-5.3 is at 48.1 and GLM-5.2 at 14.8 (out of 100). On each maker's API, GLM-5.3 costs $1.40 / $4.40 per 1M tokens (input / output) and GLM-5.2 $1.40 / $4.40.
Scores as published by each board; table composed November 10, 2026.
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Leaderboard by leaderboard
| Board | GLM-5.3 | GLM-5.2 | Higher |
|---|---|---|---|
| LMArena Elo (community votes) | 1,622.2 | 1,607.8 | GLM-5.3 |
| SimpleBench AVG@5 % (private set) | 66.2 | 58.8 | GLM-5.3 |
| Epoch AI Epoch Capabilities Index (ECI) · many benchmarks on one scale | 156.9 | 153 | GLM-5.3 |
Raw scores are each board's own scale, so compare within a row, not across rows. Full table: the bench index.
For coding
On the ArtificialWatch coding index, GLM-5.3 is at 51.5 (#20 of 47) and GLM-5.2 at 30.6 (#37). GLM-5.3 scores higher on both coding boards that rate them.
| Coding board | GLM-5.3 | GLM-5.2 |
|---|---|---|
| LMArena · Coding | 1,499 | 1,488 |
| Vals Vibe Code Bench | 78.1 | 64 |
GLM-5.3 at a glance
- Developer
- Zhipu AI
- Status
- Live since August 14, 2026
- API price
- $1.40 input / $4.40 output per 1M tokens · Z.AI, glm-5.3
- Context window
- 1M tokens
Live - same 743B base as 5.2, every gain from post-training · tops CyberGym · open weights on Hugging Face (zai-org/GLM-5.3, 141 safetensors files, public when we read it Sep 25; repo created Aug 25) · Anthropic research (Sep 29): builds end-to-end exploits near Claude Mythos Preview's rate (ExploitBench 50 of 410 vs 56 of 410), and its safeguards fell to simple bypasses 64-100% of the time · Epoch AI report (Oct 29): built one-click file-reading exploits for all six Firefox releases Sep 17-Oct 26 from published-but-unshipped fixes, about $1,000 each, with no refusals at any provider.
Epoch AI: open-weight GLM 5.3 built working Firefox exploits from security fixes that had not shipped yet, for about $1,000 each
2026-10-30 · An open-weight model can now turn browser security fixes that are public but not yet shipped into working exploits on its own, according to a report Epoch AI published October 29.
Anthropic: open-weights GLM-5.3 nearly matches Claude Mythos Preview at building exploits, and its safeguards are easy to strip
2026-09-29 · Anthropic published research on September 29 testing Z.ai's GLM-5.3, an open-weights model anyone can download, on offensive cyber tasks. Its finding: GLM-5.3 can build working end-to-end exploits at close to the rate of Claude Mythos Preview, the model Anthropic released only to vetted defenders.
GLM-5.2 at a glance
- Developer
- Zhipu AI
- Status
- Live since June 16, 2026
- API price
- $1.40 input / $4.40 output per 1M tokens · Z.AI, glm-5.2
- Context window
- 1M tokens
Live — $0.40/$2.52 per 1M · no longer the current shipped GLM: 5.3 landed Aug 14 and 5.3 Flash Aug 26.
GLM-5.3 vs GLM-5.2: quick answers
Which is better, GLM-5.3 or GLM-5.2?
It depends on what you measure. GLM-5.3 scores higher than GLM-5.2 on all 3 public leaderboards that rate both. GLM-5.3's widest lead is on Epoch AI. On the ArtificialWatch Index, which takes the median of each model's percentile across the boards that score it, GLM-5.3 is at 48.1 and GLM-5.2 at 14.8 (out of 100).
Which is newer, GLM-5.3 or GLM-5.2?
GLM-5.3. It went live on August 14, 2026, 59 days after GLM-5.2 (June 16, 2026).
Which is cheaper, GLM-5.3 or GLM-5.2?
They cost the same per token. On each vendor's own API, GLM-5.3 is $1.40 input / $4.40 output per million tokens and GLM-5.2 is $1.40 input / $4.40 output per million tokens. Prices as published in the models.dev catalog, as of 2026-11-10.
Which has the bigger context window, GLM-5.3 or GLM-5.2?
Neither. Both take 1M tokens.
How do GLM-5.3 and GLM-5.2 compare for coding?
On the ArtificialWatch coding index, GLM-5.3 is at 51.5 (#20 of 47) and GLM-5.2 at 30.6 (#37). GLM-5.3 scores higher on both coding boards that rate them.
Who makes GLM-5.3 and GLM-5.2?
Both are from Zhipu AI.
Where do these numbers come from?
Each score is the leaderboard's own published figure, read by ArtificialWatch (table composed November 10, 2026). Boards measure different things, so raw scores are only comparable within a row.
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Also: the bench index · API pricing · what a workload costs on each · the wire