GLM 5.2 vs Grok 3: Speed, Pricing, and Benchmark Comparison

When you are choosing between GLM 5.2 and Grok 3 for a real deployment, the benchmark gap is real—but so is the cost and speed gap. Grok 3 outscores GLM 5.2 by 17 points on the Artificial Analysis Intelligence Index, yet its output tokens cost 3.4x more and its responses arrive 3–4x slower. Knowing which tradeoff your workload can tolerate is everything.

The short version. Grok 3 scores ~68 on the AA Intelligence Index; GLM 5.2 sits at 51. On raw capability measured by a composite of standard benchmarks, Grok 3 wins clearly. But GLM 5.2 is the third-fastest frontier model available today at 158 t/s, while Grok 3 runs at roughly ~45 t/s. Output pricing is $4.40/M tokens for GLM 5.2 versus $15.00/M for Grok 3—a 70% reduction. GLM 5.2 also ships with a 1M-token context window, eight times Grok 3's 131K, and is available as open weights under an MIT license, so you can self-host. Grok 3 counters with image understanding, real-time X/Twitter data, and noticeably stronger reasoning scores.

Choose GLM 5.2 if you need throughput, cost efficiency, long-document analysis, open-source flexibility, or on-premise deployment without vendor lock-in.

Choose Grok 3 if you need the highest benchmark accuracy on reasoning tasks, multimodal image input, or live social and news data pulled from X.

Quick Comparison

Benchmark Performance

GLM 5.2 publishes strong numbers on coding and science tasks. On GPQA Diamond—a graduate-level science reasoning benchmark widely used to stress-test frontier models—GLM 5.2 reaches 89%. SWE-bench Pro, which evaluates real-world software engineering on GitHub issues, comes in at 62.1%. Terminal-Bench v2.1, a systems-level coding benchmark, lands at 78%. HumanEval exceeds 90%, putting GLM 5.2 in the top tier for code generation despite its moderate position on the composite AA index.

Grok 3's ~68 on the AA Intelligence Index reflects stronger performance on the aggregate suite of reasoning, math, and knowledge benchmarks that Artificial Analysis tracks. The 17-point gap over GLM 5.2's 51 is meaningful: it represents a real difference in complex multi-step reasoning, not just statistical noise. xAI has not published a full breakdown of Grok 3's per-benchmark numbers at the level of detail that THUDM provides for GLM 5.2.

If your workload sits squarely in software engineering or long-context science tasks, GLM 5.2's benchmark numbers are competitive with models that score higher on composite indices. If you need strong performance across all reasoning categories—especially math olympiad-style problems or multi-hop knowledge retrieval—Grok 3's higher index score likely translates to meaningfully better results.

Pricing Breakdown

The pricing gap between these two models is large enough to change the economics of a product.

At 1 million output tokens per day—a reasonable volume for a mid-size product—GLM 5.2 costs $4.40 versus Grok 3's $15.00. That is a $10.60 daily difference, or roughly $3,870 per year, before accounting for input tokens. At 10 million output tokens per day the gap becomes $38,700 per year. Cache hit pricing at $0.26/M for GLM 5.2 further reduces costs for repeated prompts in agentic loops or RAG pipelines where the system prompt is reused across many calls.

GLM 5.2 is priced at Z.ai with model identifier glm-5.2, and it is also available through OpenRouter under z-ai/glm-5.2. For teams already using the OpenAI SDK, pointing base_url at Z.ai requires no code changes beyond the model name and key. See the full GLM 5.2 pricing breakdown for a worked example with different traffic patterns.

Speed and Latency

Speed is where GLM 5.2 has its clearest structural advantage.

GLM 5.2's 158 tokens per second makes it the third-fastest frontier model tracked by Artificial Analysis. Grok 3 runs at roughly ~45 t/s, putting GLM 5.2 at approximately 3.5x the throughput. For streaming UIs, the difference is immediately noticeable—a 2,000-token response arrives in about 13 seconds from GLM 5.2 versus roughly 44 seconds from Grok 3.

TTFT tells a different story: Grok 3 starts streaming faster at ~1.0 s versus GLM 5.2's 1.54 s. If perceived responsiveness is critical—for example, in a real-time chat interface where users are watching the cursor before the first word appears—Grok 3's lower TTFT is a genuine UX advantage. For batch workloads, agentic pipelines, or document processing where total throughput matters more than first-byte latency, GLM 5.2's 3.5x speed advantage dominates.

GLM 5.2's speed stems from its Mixture of Experts architecture: 753B total parameters but only 40B active per forward pass across 8 of 256 experts per layer. This keeps compute per token low while retaining the representational capacity of a much larger dense model.

Context Window

GLM 5.2 supports a 1,048,576-token context window—commonly referred to as 1M tokens. Grok 3 tops out at 131K tokens. The practical difference is substantial.

A 1M context window fits roughly:

• A 750-page technical book in a single prompt
• An entire codebase of ~30,000–50,000 lines without chunking
• Hundreds of research papers concatenated for a meta-analysis task
• An extended conversation history spanning days of a multi-agent session

Grok 3's 131K context covers approximately 100 pages of text or a moderately sized codebase. It handles most individual document tasks comfortably, but cross-document analysis, full-repository code review, or long-running agent memory requires chunking strategies that add latency and engineering complexity.

For RAG pipelines, GLM 5.2's 1M window enables a different architecture: instead of retrieving and re-ranking chunks, you can sometimes embed the entire knowledge base directly in context and let the model attend to it all at once. Whether this beats retrieval-augmented approaches depends on the task, but the option is architecturally significant.

Key Differentiators

Modalities. GLM 5.2 is text-only. It accepts no image, audio, or video input. Grok 3 accepts both text and image input, making it the only choice when visual understanding is required—product image analysis, document parsing with diagrams, or screenshot-based debugging.

License and self-hosting. GLM 5.2 ships under an MIT license with open weights available on Hugging Face at THUDM/GLM-5.2. Any team with sufficient GPU infrastructure can run it on-premise, air-gapped, or in a private VPC with no dependency on an external API. This matters for healthcare, legal, government, and financial applications where data residency or compliance requirements prohibit sending sensitive content to third-party APIs.

Grok 3 is closed-source, operated by xAI. There is no self-hosting option, and data sent to the Grok 3 API is processed on xAI's infrastructure under their terms.

Real-time data. Grok 3 integrates with X (formerly Twitter) and can surface real-time information from the platform, including live posts, trending topics, and current events. This is a unique capability with no equivalent in GLM 5.2. For applications that need live market sentiment, breaking news awareness, or social signal monitoring, Grok 3 has a capability that GLM 5.2 simply does not offer.

Architecture. GLM 5.2 is built on a Transformer decoder with 78 layers, 256 experts per layer, 8 activated per forward pass, and uses Dynamic Sparse Attention (DSA). The MoE design is the main reason it can sustain 158 t/s at frontier-model accuracy levels. Grok 3's architecture is not publicly disclosed by xAI.

When to Choose GLM 5.2

• You are processing large volumes of text and output cost is a key variable in your unit economics
• Your application needs to handle documents, codebases, or conversation histories longer than 131K tokens
• You want to self-host to meet data residency, compliance, or latency requirements
• You are building an agentic pipeline where total throughput determines how many tasks complete per second
• You want to run fine-tuning experiments on an open-weights base model
• Your workload is code generation or science reasoning, where GLM 5.2's per-benchmark scores are competitive with higher-index models
• You need cached prompt pricing ($0.26/M) to reduce costs in RAG or multi-turn pipelines

When to Choose Grok 3

• You need the highest available composite benchmark score and are willing to pay the price premium
• Your application requires image understanding—product analysis, visual QA, diagram interpretation
• You need access to real-time X/Twitter data, social signals, or live news context
• First-byte latency matters more than overall throughput for your user experience
• You are building on xAI's ecosystem and value tight integration with X platform features
• Your data volume is low enough that the 3.4x output price difference is not meaningful

Frequently Asked Questions

Is GLM 5.2 open source? Yes. GLM 5.2 is released under an MIT license with open weights published on Hugging Face at THUDM/GLM-5.2. You can download and run it on your own hardware, fine-tune it, or integrate it into commercial products without royalty obligations. Grok 3 is closed source with no self-hosting option.

How much cheaper is GLM 5.2 than Grok 3? Output tokens cost $4.40/M for GLM 5.2 versus $15.00/M for Grok 3—a 71% reduction. Input tokens cost $1.40/M for GLM 5.2 versus $3.00/M for Grok 3, a 53% reduction. At high output volumes the savings compound quickly: 10M output tokens per day costs $44 with GLM 5.2 and $150 with Grok 3.

Does GLM 5.2 support image input? No. GLM 5.2 is a text-only model. It does not accept image, audio, or video inputs. If your use case requires visual understanding, Grok 3 or a multimodal model is the appropriate choice.

What does the AA Intelligence Index measure? The Artificial Analysis Intelligence Index is a composite score that aggregates performance across a suite of reasoning, coding, math, and knowledge benchmarks. GLM 5.2 scores 51 and Grok 3 scores ~68. A higher score reflects stronger average performance across these tasks, though individual benchmark results vary—GLM 5.2 scores 89% on GPQA Diamond despite its lower composite index.

Can I use GLM 5.2 with the OpenAI Python SDK? Yes. Point base_url to https://api.z.ai/v1, set your Z.ai API key, and use model glm-5.2. No other code changes are required for basic completions. GLM 5.2 is also available on OpenRouter under z-ai/glm-5.2 if you prefer a unified API endpoint for multiple models.

Which model is faster, GLM 5.2 or Grok 3? GLM 5.2 is significantly faster on output throughput at 158 t/s versus Grok 3's ~45 t/s—roughly 3.5x faster. Grok 3 has a lower time to first token (~1.0 s versus 1.54 s for GLM 5.2), so it starts streaming sooner. For long outputs, GLM 5.2 finishes much faster overall. For short responses where TTFT dominates perceived speed, Grok 3 may feel more responsive.

Can GLM 5.2 handle an entire codebase in context? With a 1,048,576-token context window, GLM 5.2 can hold roughly 30,000–50,000 lines of code in a single prompt depending on average line length. For most real-world repositories that is the entire working codebase. Grok 3's 131K context handles smaller projects or selected files but requires chunking for large monorepos. For full-repository code review or refactoring tasks, GLM 5.2's 1M context is a structural advantage.

Bottom Line

GLM 5.2 and Grok 3 are built for different buyers. Grok 3 is the right call when benchmark accuracy, image understanding, or real-time X data access is non-negotiable and your volume is modest enough that the 3.4x output price premium fits your budget. GLM 5.2 is the right call when you are optimizing for cost, speed, context length, or open-source flexibility—especially at scale, where its 71% output price reduction and 158 t/s throughput create compounding advantages that offset the 17-point benchmark gap for most production workloads.

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

Sources

• Artificial Analysis — GLM 5.2 Model Profile
• Artificial Analysis — Grok 3 Model Profile
• GLM 5.2 on Hugging Face (THUDM/GLM-5.2)
• Z.ai API Documentation
• xAI Grok — Official Model Page