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Read about our latest product features, solutions, and updates.
Kimi K3 vs DeepSeek V3: Cost, Speed, and Coding Benchmarks
DeepSeek V3 costs $1.10/M output tokens versus Kimi K3's $15/M — 13× cheaper. Kimi K3 scores ~10 points higher on the AA Intelligence Index. Here is when each open-weight-adjacent model is the right call.
Kimi K3 vs Gemini 2.5 Pro: Performance, Price, and Context Window
Gemini 2.5 Pro scores significantly higher on the AA Intelligence Index than Kimi K3 and costs $10/M output tokens versus K3's $15/M — 33% cheaper for a stronger model. Here is how these 1M-context models compare.
GLM 5.2 API: Endpoints, Authentication, and Python Integration Guide
GLM 5.2 uses the OpenAI SDK format with a different base_url and model name. Here is the complete guide to endpoints, authentication, streaming, and Python integration.
GLM 5.2 Architecture: 753B Parameters, MoE Design, and How It Works
GLM 5.2 uses Mixture-of-Experts with 753B total parameters but only 40B active per token. Here is how its architecture works and what it means for cost, speed, and capability.
GLM 5.2 Context Window: What 1 Million Tokens Actually Means
GLM 5.2 supports 1,048,576 tokens — 8x GPT-4o's 128K. Here is what that capacity enables for codebases, documents, and long agent sessions, and when it matters.
GLM 5.2 for Coding: Benchmarks, Best Prompts, and IDE Integration
GLM 5.2 scores 62.1% on SWE-bench Pro and 78% on Terminal-Bench. Here is how to use it as a coding assistant, what prompt patterns work best, and how to integrate it with VS Code and Cursor.
GLM 5.2 for Data Analysis: Structured Output, SQL Generation, and Python Workflows
GLM 5.2's 1M context window and JSON mode make it practical for large-scale data analysis. Here is how to use it for SQL generation, CSV analysis, structured extraction, and Python data workflows.
GLM 5.2 Function Calling: Tool Use, Parallel Calls, and Agentic Workflows
GLM 5.2 supports OpenAI-compatible function calling with parallel tool calls. Here is how to define tools, handle responses, build multi-step agents, and run real agentic loops.
GLM 5.2 License: MIT Open Weights and What It Means for Commercial Use
GLM 5.2 weights are MIT-licensed with no commercial restrictions. Here is what you can do — fine-tune, redistribute, build products — and how MIT compares to Llama and Apache licenses.
GLM 5.2 on OpenRouter: Access, Pricing, and Integration Guide
GLM 5.2 is available on OpenRouter as z-ai/glm-5.2. Here is how to access it, how OpenRouter pricing compares to Z.ai direct, and how to switch from any other model with one line of code.
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 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 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 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-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 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 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.
GLM 5.2 vs Qwen 3: Chinese Open-Weight AI Models Compared
Qwen 3 235B-A22B costs 73% less per output token than GLM 5.2, but GLM 5.2 has 8x larger context and stronger agentic benchmark results. Here is how these open-weight leaders compare.
How to Fine-Tune GLM 5.2: GPU Requirements & LoRA
GLM 5.2 MIT license allows fine-tuning. Full 753B fine-tuning requires 16+ H100s. LoRA on quantized versions is feasible on 2-4 A100s. Here is what works and what does not.
How to Use GLM 5.2 API with Python: Complete Integration Guide
GLM 5.2 is OpenAI SDK-compatible. Change base_url and model name and your existing Python code works. Here is the step-by-step guide with streaming, function calling, and async examples.
Kimi K3 Pricing: API Costs, Context Window, and Value Compared
Kimi K3 costs $3.00/M input and $15.00/M output tokens. That is 3.4x more expensive than GLM 5.2 per output token for a 6-point AA Intelligence advantage. Here is the full breakdown.
Kimi K3 vs GPT-4o: Benchmarks, Speed, and When to Choose Each
Kimi K3 and GPT-4o both score around 57 on the AA Intelligence Index but take opposite positions on cost and context. K3 output tokens cost 50% more; GPT-4o has audio input but only 128K context.
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 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.
What Is Kimi K3? Moonshot AI's 2.8T Open-Weight Model Explained
Kimi K3 is Moonshot AI's 2.8-trillion-parameter reasoning model with 1M context and image input, scoring 57 on the AA Intelligence Index — on par with Claude Opus 4.8. Released July 16, 2026.
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.
How to Use GLM 5.2 for Free: 4 Methods That Work in 2026
GLM 5.2 weights are MIT-licensed but self-hosting needs 240GB+ RAM. The real free options are browser chat, NVIDIA NIM credits, and Cloudflare Workers AI (10k neurons/day). Honest breakdown of all four.
Is GLM 5.2 Multimodal? Vision Capabilities Explained
GLM 5.2 is text-only: no image input or vision. The real multimodal model from Z.ai is GLM-5V-Turbo. What each does, how to add vision, and what's coming.
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.