GLM 5.2 vs GLM-4: What Changed and Whether the Upgrade Is Worth It

You are running GLM-4 in production and wondering whether GLM 5.2 justifies a migration — or you are starting fresh and need to pick one. The two models sit at completely different points on the capability-cost curve, so the right answer depends almost entirely on what your workload actually demands.

The short version: GLM 5.2 is a 753B Mixture-of-Experts model scoring 89% on GPQA Diamond and 62.1% on SWE-bench Pro, running at 158 tokens per second with a 1M token context window at $1.40/M input tokens. GLM-4 covers a range of smaller dense models — 9B open-weight up to a proprietary Plus tier — with much lower benchmark scores, 128K context as the standard ceiling, and input prices starting at $0.14/M for the Air variant. The gap in reasoning and coding capability is large; the gap in price is also real.

Choose GLM 5.2 if your tasks involve complex multi-step reasoning, long document analysis, agentic terminal workflows, or production-grade coding assistance where correctness matters more than cost per token.

Choose GLM-4 if you are running very high volume at relaxed quality requirements, your prompts are short and classification-style, or you need the absolute lowest per-token price and GLM-4-Air's ~$0.14/M input fits your budget constraints.

Quick Comparison

Benchmark Performance

The GPQA Diamond score is the clearest signal of the reasoning gap. GPQA tests graduate-level science problems in chemistry, biology, and physics — domains where pattern-matching fails and genuine multi-step deduction is required. GLM-4's 9B parameter count simply cannot carry the representational capacity needed to reliably solve these questions. GLM 5.2 at 89% sits alongside the strongest frontier models on this benchmark.

SWE-bench Pro measures an even more operationally relevant capability: resolving real GitHub issues in real codebases with real test suites. GLM-4 variants were not competitive on this benchmark in a meaningful way. GLM 5.2's 62.1% score puts it in practical territory for code review automation, bug triage, and developer agent workflows.

Terminal-Bench v2.1 at 78% is a new benchmark category entirely — it tests the ability to execute multi-step terminal tasks, manage file systems, run commands, and recover from errors in an agentic loop. GLM-4 models were not designed for or tested in this mode. If your use case involves any autonomous agent execution, this benchmark is the decisive one.

HumanEval remains a useful baseline for function-level coding. GLM-4-9B at approximately 72% is a competent but not excellent coder for isolated tasks. GLM 5.2's 90%+ closes most of the remaining gap to top-tier coding models, making it viable for production code generation where the output is actually executed.

Pricing Breakdown

At face value, GLM-4-Air is 10x cheaper on input than GLM 5.2. For a workload processing 100M input tokens per month, that difference is roughly $1,260 versus $140. That delta matters.

The relevant question is whether GLM-4 actually produces usable output for your task. For simple extraction, classification, or short summarization tasks with clear templates, GLM-4-Air can absolutely carry the load at a fraction of the price. For reasoning chains, coding tasks, or anything requiring the model to plan across many steps, quality failures in GLM-4 often cost more in downstream rework than the token savings are worth.

The cache hit price for GLM 5.2 at $0.26/M input is worth noting for high-volume users with repeated system prompts or document contexts. If a large portion of your input tokens are cache hits, the effective input cost drops significantly — potentially below $0.50/M for workloads with high cache reuse.

GLM-4-Long offers 1M context at ~$0.56/M input, which makes it a potential middle path for long-document tasks where reasoning depth is less critical than document length. However, the gap in reasoning quality still applies — long documents with complex questions are exactly where the GPQA-level reasoning gap becomes visible.

See GLM 5.2 pricing for a deeper breakdown of cost scenarios.

Speed and Latency

GLM 5.2's 158 tokens per second throughput is the counterintuitive result here. A 753B total parameter model should be slow — but the Mixture-of-Experts architecture only activates 40B parameters per forward pass. This means the effective compute per token is closer to a 40B dense model, while the representational capacity is closer to a full 753B model. The result is frontier-level quality at near-mid-range latency.

The 1.54 second time-to-first-token is acceptable for most interactive use cases and very workable for batch processing. If your application is streaming responses to users in real time, GLM 5.2's throughput of 158 t/s means long responses complete quickly even if the first token arrives after 1.5 seconds.

GLM-4-Air will still win on raw TTFT for applications where the first token latency is the dominant user experience metric — small classification responses or very short chat completions. For anything generating paragraphs of output, GLM 5.2's throughput advantage closes the perceived speed gap significantly.

Context Window

Standard GLM-4 models ship with 128K context. GLM-4-Long extends this to 1M, but at a premium price and with the reasoning limitations that apply across the GLM-4 family. GLM 5.2 offers 1M context (1,048,576 tokens precisely) as the standard context window, not an add-on tier.

What does 1M tokens enable in practice? A rough calibration: 1M tokens is approximately 750,000 words, or roughly 1,500 pages of a technical document. This means you can load entire codebases, full legal contracts, complete research corpora, or multi-year email threads into a single context without chunking or retrieval.

The practical implication for RAG workflows is significant. Many RAG architectures exist primarily to work around context limits — chunking documents, embedding them, retrieving relevant chunks, and hoping the retrieval step did not miss critical cross-document relationships. With a 1M context window, a substantial class of these retrieval problems becomes a direct context problem instead. You load the documents, ask your question, and the model answers across the full corpus.

This only works if the model can actually reason over a long context without losing coherence — which is where GLM 5.2's reasoning benchmarks matter again. A model with weak reasoning will produce low-quality answers even when the relevant information is in context. The combination of 1M tokens and 89% GPQA reasoning is what makes long-context use cases tractable with GLM 5.2.

Architecture and Open Weights

One of the most underappreciated differences between GLM-4 and GLM 5.2 is the licensing and distribution model. GLM-4-9B carries an MIT license and is available on Hugging Face (THUDM/GLM-4). GLM 5.2 also carries an MIT license and is available at THUDM/GLM-5.2 on Hugging Face.

This means GLM 5.2 is genuinely open — you can download the weights, run inference on your own infrastructure, fine-tune it, and deploy it without per-token API costs. For organizations with data privacy requirements, regulated environments, or very high volume that makes self-hosting cost-effective, this is a material advantage over the proprietary GLM-4-Air and GLM-4-Plus API tiers.

The architecture shift also deserves attention. GLM-4-9B is a standard dense Transformer. GLM 5.2 uses a Transformer decoder with 78 layers, 256 experts per layer, 8 experts activated per token — a Mixture-of-Experts design (MoE). This architecture choice explains both the parameter count (753B total, 40B active) and the speed (only active experts contribute to compute). MoE at this scale is the same approach used by several top frontier models, and it produces a different quality-cost curve than scaling a dense model.

One important limitation: GLM 5.2 is text only. It does not accept image or audio inputs. If your GLM-4 workflow includes multimodal inputs, GLM 5.2 is not a drop-in replacement for those specific capabilities.

When to Choose GLM-4

• You are running bulk classification, extraction, or template-filling tasks at very high volume where $0.14/M input is a meaningful budget constraint
• Your prompts are short, well-defined, and the output quality from a 9B model is sufficient for your quality bar
• You need a self-hosted model for a low-resource environment (GLM-4-9B runs on consumer hardware that GLM 5.2 cannot)
• Your use case involves image inputs, which GLM 5.2 does not support
• You are prototyping or experimenting and want zero API cost with local weights
• Latency to first token is the primary constraint and even 1.54 seconds is too slow for your user experience

When to Choose GLM 5.2

• Your tasks require multi-step reasoning, graduate-level domain knowledge, or chained logic (GPQA Diamond 89%)
• You need production-grade coding assistance — code generation, bug fixing, code review — where correctness is more important than cost (SWE-bench Pro 62.1%, HumanEval 90%+)
• You are building agentic workflows that execute terminal commands, manage files, or run automated pipelines (Terminal-Bench 78%)
• Your documents exceed 128K tokens and you need to reason across the full content, not just retrieve chunks
• You want MIT-licensed open weights with frontier-level quality for a self-hosted deployment
• Your organization has data privacy requirements and cannot send data to proprietary API endpoints for the GLM-4 tiers

Frequently Asked Questions

Is GLM 5.2 backwards compatible with GLM-4 API calls?

The API endpoint and authentication differ. GLM-4 variants are served through ZhipuAI's API. GLM 5.2 is served through Z.ai (base URL https://api.z.ai/v1, model name glm-5.2) and is also available on OpenRouter as z-ai/glm-5.2. You will need to update your base_url and model parameter, but the OpenAI-compatible API format means the rest of your client code typically does not change.

Can I self-host GLM 5.2 the same way I self-host GLM-4-9B?

Both carry MIT licenses, but the hardware requirements are vastly different. GLM-4-9B can run on a single high-end GPU or even CPU inference at reduced speed. GLM 5.2 at 753B total parameters requires significant multi-GPU infrastructure. The MoE design means only 40B parameters activate per token, but you still need to hold all 753B in memory across your GPU cluster. Self-hosting GLM 5.2 is feasible for well-resourced teams — the weights are on Hugging Face at THUDM/GLM-5.2 — but it is not a laptop deployment.

Does GLM 5.2 support vision and multimodal inputs like some GLM-4 variants?

No. GLM 5.2 is text only. It accepts text input and produces text output. Some GLM-4 variants include vision capabilities. If your workflow includes image understanding or multimodal prompts, this is a genuine limitation of GLM 5.2 and a reason to evaluate whether GLM-4's vision tiers meet your needs.

How does the 1M context window of GLM 5.2 compare to GLM-4-Long?

Both offer 1M token context windows. The difference is reasoning quality over that context. GLM 5.2 operates at 89% GPQA Diamond, meaning it can reliably reason through complex long-context problems. GLM-4-Long extends context for GLM-4's architecture, but the underlying reasoning capacity is substantially lower. For tasks like "summarize this 500-page report" GLM-4-Long may suffice; for "answer this complex question that requires integrating information from 20 different sections of this document" the reasoning gap matters.

What is the Artificial Analysis Intelligence Index score for GLM 5.2?

GLM 5.2 scores 51 on the Artificial Analysis Intelligence Index. This composite benchmark aggregates performance across reasoning, coding, and knowledge tasks. It provides a useful single-number comparison point across frontier models.

Is the $0.26/M cache hit price for GLM 5.2 automatic or do I need to configure it?

Cache hits apply automatically when repeated content appears at the beginning of your context — typically your system prompt or a repeated document prefix. You do not need to explicitly enable caching, but you should structure your prompts so that the cacheable content (system prompt, document context) comes before the variable content (user query). If most of your requests share a large system prompt, cache hit pricing significantly reduces your effective input cost.

When did GLM 5.2 release relative to GLM-4?

GLM-4 was the prior generation flagship from THUDM and ZhipuAI, with the 9B open-weight model and the commercial API tiers. GLM 5.2 represents a generational jump in scale — the 84x parameter increase from 9B to 753B total, combined with the MoE architecture — that corresponds to a major capability threshold crossing, not an incremental update.

Bottom Line

GLM 5.2 and GLM-4 are not competing on the same tier — they solve different problems. GLM-4 remains a cost-effective choice for high-volume simple tasks, particularly at the Air pricing point. GLM 5.2 is the clear choice anywhere the task requires genuine reasoning, coding accuracy, agentic capability, or long-document analysis. The MIT license and open weights apply to both, making self-hosting viable for either depending on your hardware budget. If your current GLM-4 deployment is producing quality failures on complex tasks, GLM 5.2 is the direct path to fix them.

Try GLM 5.2 — no API key needed: glm5.app/chat.

Sources

• Artificial Analysis — GLM 5.2 Model Card
• THUDM/GLM-5.2 on Hugging Face
• THUDM/GLM-4 on Hugging Face
• Z.ai API Documentation
• OpenRouter — z-ai/glm-5.2