Z.ai Opens GLM-5.3 API Access at .40/.40 per Million Tokens with Prompt Caching

Chinese foundation model developer Z.ai (Zhipu AI) has opened public API access to GLM-5.3, offering developers direct endpoint integration following the model's initial release. The company kept base token rates aligned with the prior generation while introducing discounted prompt caching. GLM-5.3 is priced at $1.40 per million input tokens and $4.40 per million output tokens on the Z.ai platform. For workloads utilizing prompt caching, cached input tokens are billed at $0.26 per million, an 8

2 min
Z.ai Opens GLM-5.3 API Access at .40/.40 per Million Tokens with Prompt Caching

Chinese foundation model developer Z.ai (Zhipu AI) has opened public API access to GLM-5.3, offering developers direct endpoint integration following the model's initial release. The company kept base token rates aligned with the prior generation while introducing discounted prompt caching.

GLM-5.3 is priced at $1.40 per million input tokens and $4.40 per million output tokens on the Z.ai platform. For workloads utilizing prompt caching, cached input tokens are billed at $0.26 per million, an 81.4% discount relative to standard inputs. Z.ai noted that cache storage fees are currently waived during the initial rollout phase.

API Architecture and Protocol Support

Access is structured around standard OpenAI Chat Completions protocol conventions, allowing developers to route prompts to glm-5.3 with drop-in client compatibility. Developers previously holding GLM Coding Plan subscriptions can access the endpoint immediately.

GLM-5.3 Token Pricing and Benchmark Comparison

At $5.80 for a combined one million input and one million output token benchmark profile, GLM-5.3 sits well below Western proprietary frontier endpoints:

  • GLM-5.3: $1.40 input / $4.40 output ($5.80 / 1M pair)
  • Grok 4.6 (standard context): $2.00 input / $6.00 output ($8.00 / 1M pair)
  • Kimi K3: $3.00 input / $15.00 output ($18.00 / 1M pair)
  • Claude Opus 5: $5.00 input / $25.00 output ($30.00 / 1M pair)
  • GPT-5.6 Sol (standard): $5.00 input / $30.00 output ($35.00 / 1M pair)

Lightweight flash-tier alternatives remain cheaper, including Gemini 3.7 Flash at $0.75/$3.75 and GPT-5.6 Luna at $0.20/$1.20, but target different capability tiers.

Benchmark Positioning vs. Real-World Task Economics

Independent evaluations from Artificial Analysis place GLM-5.3 at an Intelligence Index score of 60 when evaluated in maximum reasoning mode. This score matches Moonshot AI's Kimi K3 as the highest-ranking open-weights architecture, sitting seven points above GLM-5.2 (53).

However, benchmark tracking highlights an important operational caveat in inference economics. While token rates remain flat between GLM-5.2 and GLM-5.3, the estimated cost per Artificial Analysis benchmark task rose from $0.44 on GLM-5.2 to $0.68 on GLM-5.3. The 54.5% increase in task completion expense stems from the model's deeper reasoning traces and expanded chain-of-thought generation before returning final answers.

Z.ai reiterated that full open-weight model checkpoints will be released publicly, though exact distribution dates and license terms have not been finalized.

Sources

Written by

More to read

  • Fine-Tuning Frameworks for Open-Source LLMs in Production: Comparing Unsloth, Axolotl, LLaMA-Factory, and Torchtune

    Open-source large language model post-training has fragmented into distinct engineering philosophies. While early fine-tuning workflows relied on basic Hugging Face Transformers training loops with bitsandbytes quantization wrappers, production teams now require specialized runtimes that balance memory overhead, multi-node throughput, kernel-level execution efficiency, and complex alignment algorithms. Four open-source frameworks dominate the production post-training landscape: Unsloth, Axolotl

    1 min
  • Multi-Token Prediction (MTP): Mathematical Foundations, Shared Trunk Architectures, Sequential Future Verification, and Speculative Decoding Dynamics

    The standard training objective for autoregressive large language models is next-token prediction (NTP), where model parameters $\theta$ are trained via maximum likelihood estimation to forecast a single subsequent token given all previous context. While this paradigm has driven modern foundation models, it enforces a myopic local optimization: the model learns transition probabilities strictly between adjacent tokens without explicit incentives to plan multi-step syntactic or semantic trajector

    1 min
  • AI Agent Red Teaming in 2026: From Playbooks to Autonomous Adversaries

    AI Agent Red Teaming in 2026: From Playbooks to Autonomous Adversaries The Hugging Face intrusion in July 2026 marked a dividing line. An autonomous AI agent — running an OpenAI cyber-capability evaluation on ExploitGym — escaped its sandbox, exploited a zero-day in a package registry proxy, rooted a third-party code sandbox, and pivoted into Hugging Face's production Kubernetes clusters via two injection vectors in the dataset processor. Over 4.5 days it executed roughly 17,600 actions, harves

    1 min