GLM-5.3 achieves 60 on the Artificial Analysis Intelligence Index, on par with Kimi K3 and up 7 points from GLM-5.2. Once the weights are released it will be tied as the leading open weights model

@Zai_org has just launched GLM-5.3, which ties Kimi K3 (60) for the most intelligent open weights models on the Artificial Analysis Intelligence Index (assuming weights are released as per Z AI's guidance). Between Kimi K3 and GLM-5.3, the open weights frontier is closer than ever to the proprietary frontier.

GLM-5.3 keeps GLM-5.2's size at 753B total parameters and 40B active parameters. The Z AI team has shared that GLM-5.3's weights are expected to be released within the next week.

Key Takeaways:

➤ GLM-5.3 sees the largest gain in agentic capabilities, placing it amongst frontier models. The model's Elo rating in GDPval-AA v2, our real-world agentic knowledge work evaluation, rises from 1524 to 1770, a 246-point jump. That places GLM-5.3 second among all models, behind only Claude Opus 5 (1855), and surpassing the previous open weights leader on this evaluation, Kimi K3 (1668), by more than 100 points.

➤ GLM-5.3 is less token efficient than its predecessor. Across the Artificial Analysis Intelligence Index v4.1, GLM-5.3 uses roughly 18,700 output tokens per task, up about 20% from GLM-5.2 (15,700) and 27% more than Kimi K3 (14,700). This would have implications for cost of deployment.

➤ At $0.68 per Intelligence Index task, GLM-5.3 costs 1.5x GLM-5.2's $0.44, but is still cheaper than other models in the same intelligence tier. GLM-5.3 is 19% cheaper per task compared to Kimi K3 ($0.84) and 45% cheaper than GPT-5.6 Sol ($1.23). GLM-5.3's increase in cost is partially driven by a 20% token usage increase compared to its predecessor.

➤ GLM-5.3 makes an improvement in real-world knowledge accuracy, scoring 14 on AA-Omniscience up from 4 for GLM-5.2. GLM-5.3 is now the second-best open weights model on AA-Omniscience, behind only Kimi K3 (20). A more detailed breakdown of where improvements are made reflects a genuine gain in accuracy as opposed simply more cautious abstention, as accuracy rate (24% to 34%) and attempt rate (46% to 55%) both increased. However, GLM-5.3's hallucination rate regressed up slightly, from 26% to 30%.

Additional model details:

➤ Size: 753B total parameters, 40B active (MoE), unchanged from GLM-5.2

➤ Context window: 1M tokens

➤ Pricing: $1.40 per 1M input tokens and $4.40 per 1M output tokens. An 81% cache hit discount is applied ($0.26 per 1M cached input tokens)

➤ License: MIT

➤ Accessibility: GLM-5.3 is currently accessibly through Z AI's first party API. The Z AI team has shared they expect to release the weights shortly

Check out the full analysis of GLM-5.3 on Artificial Analysis: https://artificialanalysis.ai/