GLM 5 Blog
Blog
Read about our latest product features, solutions, and updates.
GLM 5.2 Structured Output: JSON Mode, Schema Validation, and Reliable Extraction
Master GLM 5.2 structured output: enable JSON mode, define output schemas, validate responses, and build reliable data extraction pipelines with Python.
GLM 5.2 Subscription Plans: API Pricing, Token Bundles, and Enterprise Options
Compare GLM 5.2 subscription and pricing plans: pay-as-you-go API rates, token bundles, enterprise contracts, and how to choose the right plan for your usage.
GLM 5.2 System Prompt Guide: Templates for Coding, Analysis, and Writing
The system prompt sets the frame for everything GLM 5.2 outputs. Here are proven templates for code generation, document analysis, and structured extraction, with token-efficiency tips.
GLM 5.2 Temperature and Sampling: How to Control Output Quality
GLM 5.2's temperature, top-p, and top-k parameters control randomness and creativity. Here are the recommended settings for coding, analysis, creative writing, and structured extraction — with concrete examples.
GLM 5.2 Thinking Mode: How Extended Reasoning Works
GLM 5.2 supports an extended thinking mode that exposes the model's reasoning chain before the final answer. Here is how thinking mode works, when to enable it, and how it affects cost and response quality.
GLM 5.2 Tokenizer: Token Counting, Vocabulary Size, and Cost Estimation
Understand how GLM 5.2's tokenizer handles Chinese, English, and code — and how to estimate token counts accurately to control API costs.
GLM 5.2 Vision API: Image Understanding, Analysis, and Multimodal Use Cases
A complete guide to GLM 5.2's vision capabilities: how to send images via the API, what tasks it handles well, and real Python code examples for multimodal apps.
GLM 5.2 vs Claude Opus 4.8: Coding, Compared
GLM 5.2 vs Claude Opus 4.8—which really wins for coding? Both tested on 40 real PRs: benchmarks, 1M context, open weights. See the verdict and try GLM 5.2 free.
GLM 5.2 vs Claude Sonnet 5: Cost, Benchmarks, and Open vs Closed
Claude Sonnet 5 costs $15/M output tokens versus GLM 5.2's $4.40 — 3.4× more expensive. Sonnet 5 leads on reasoning and coding benchmarks, but GLM 5.2 offers MIT open weights and 1M-token context at significantly lower cost.
GLM 5.2 vs Command R+: Enterprise AI for RAG and Tool Use Compared
GLM 5.2 vs Cohere Command R+ — compare pricing, context length, RAG performance, tool use, multilingual support, and which model fits your enterprise AI workload.
GLM 5.2 vs DeepSeek R1: General LLM vs Reasoning Specialist Compared
GLM 5.2 vs DeepSeek R1 — benchmark scores, pricing, context length, thinking mode, and which model to choose for coding, math, reasoning, or general tasks.
GLM 5.2 vs DeepSeek V3: Benchmarks, Pricing, and Use Cases
DeepSeek V3 costs 75% less per output token than GLM 5.2 but scores 4 points lower on the AA Intelligence Index and has 8x less context. Here is when each model is the right call.
GLM 5.2 vs DeepSeek V4 Pro: Chinese Open-Source AI Head-to-Head
GLM 5.2 vs DeepSeek V4 Pro — compare benchmark scores, pricing, context window, open-source licensing, and which Chinese frontier model fits your workload.
GLM 5.2 vs Fable 5: Open Source vs Closed
GLM 5.2 ranks #1 on Design Arena (Elo 1360) and costs 7× less than Fable 5. Fable 5 leads on vision tasks. Benchmark table, pricing, and a decision guide inside.
GLM 5.2 vs Gemini 2.5 Flash: Cost, Speed, and Benchmark Comparison
Gemini 2.5 Flash costs $0.30/M output tokens versus GLM 5.2's $4.40 — 15× cheaper. Flash is optimized for high-volume tasks. GLM 5.2 scores higher on complex coding and offers open weights. Here is when each model is the right call.
GLM 5.2 vs Gemini 2.5 Pro: Performance, Price, and When to Choose Each
Gemini 2.5 Pro scores ~75 on the AA Intelligence Index versus GLM 5.2's 51, but output tokens cost 2.3x more and it is closed source. GLM 5.2 is 3x faster. Here is the full breakdown.
GLM 5.2 vs Gemma 3: Open-Source AI from China and Google Compared
GLM 5.2 vs Gemma 3 — benchmark scores, pricing, context length, multimodal support, deployment options, and which open-weights model fits your use case.
GLM 5.2 vs GLM-4: What Changed and Whether the Upgrade Is Worth It
GLM 5.2 has 753B parameters versus GLM-4's 9B-130B range, scores significantly higher on reasoning benchmarks, and offers 1M token context. Here is when the upgrade is worth it.
GLM 5.2 vs GPT-4.1: Benchmarks, Pricing, and When to Choose
GPT-4.1 costs $8/M output tokens versus GLM 5.2's $4.40 — 82% more expensive. GPT-4.1 improves on instruction following and long-context coding, but GLM 5.2 delivers MIT open weights at significantly lower cost for comparable software engineering tasks.
GLM 5.2 vs GPT-4o: Benchmarks, Pricing, and the Real Cost Difference
GLM 5.2 costs 56% less per output token than GPT-4o and runs 3× faster, but GPT-4o adds audio and multimodal input that GLM 5.2 doesn't support. Here's when each model is the right call.
GLM 5.2 vs GPT-5.6 Sol: Benchmarks, Pricing, and the 6.8× Cost Gap
GPT-5.6 Sol costs $30/M output tokens versus GLM 5.2's $4.40 — 6.8× more expensive. Sol leads on reasoning benchmarks, but both score within 2.5 points on SWE-bench Pro. Here's when each model is worth the premium.
GLM 5.2 vs Grok 3: Speed, Pricing, and Benchmark Comparison
Grok 3 scores ~68 on the AA Intelligence Index versus GLM 5.2's 51, but output tokens cost 3.4x more and responses arrive 3x slower. Here is when each model is worth it.
GLM 5.2 vs Claude Haiku 4.5: Speed, Cost, and When Size Matters
Claude Haiku 4.5 costs $4/M output tokens — nearly the same as GLM 5.2's $4.40. But Haiku 4.5 is optimized for speed on simple tasks, while GLM 5.2 excels at complex coding with 1M-token context.
GLM 5.2 vs Kimi K2.5: Benchmarks, Pricing, and When to Choose Each
GLM 5.2 scores 51 on intelligence vs Kimi K2.5's 35, runs 3x faster, and has a 4x larger context window — but Kimi K2.5 is 57% cheaper and supports image and video input.
GLM 5.2 vs Kimi K3: Benchmarks, Pricing, and Which One to Use
Kimi K3 scores 57 on the AA Intelligence Index versus GLM 5.2's 51, but output tokens cost 3.4× more and responses arrive 2.5× slower. Here's when each model is worth it.
GLM 5.2 vs Llama 3.3 70B: Open-Weight Models Compared on Speed, Benchmarks, and Cost
Llama 3.3 70B costs 80% less via Groq than GLM 5.2 direct but scores 9 points lower on the AA Index and has 8x less context. Here is which open-weight model fits your workload.
GLM 5.2 vs LLaMA 4 Maverick: Large Open-Source MoE Models Compared
GLM 5.2 vs LLaMA 4 Maverick — benchmark performance, pricing, 1M context vs 1M context, multimodal, and which large open-weights MoE model to use.
GLM 5.2 vs LLaMA 4 Scout: Open-Source Giants Compared
GLM 5.2 vs LLaMA 4 Scout — benchmark scores, pricing, 10M vs 1M context, multimodal support, deployment options, and which open-weights model to choose.
GLM 5.2 vs MiniMax M1: Comparing Chinese AI Frontier Models
GLM 5.2 vs MiniMax M1 — in-depth comparison of two leading Chinese AI models: benchmark scores, context window, pricing, API access, and which fits your use case.
GLM 5.2 vs Mistral Large 2: Benchmarks, Cost, and Enterprise Fit
Mistral Large 2 costs $6.00/M output tokens versus GLM 5.2's $4.40 and scores lower on most benchmarks. GLM 5.2 also has 8x larger context and MIT-open weights. Here is the full comparison.