xAI

xAI AI

xAI AI

xAI AI

xAI AI

xAI AI

xAI AI

xAI AI

xAI AI

xAI AI

xAI AI

z.ai debuts faster, cheaper GLM-5 Turbo model for agents and 'claws' — but it's not open-source

Chinese AI startup Z.ai, known for its powerful, open source GLM family of large language models (LLMs), has introduced GLM-5-Turbo, a new, proprietary variant of its open source GLM-5 model aimed at agent-driven workflows, with the company positioning it as a faster model tuned for OpenClaw-style tasks such as tool use, long-chain execution and persistent automation. It's available now through Z.ai's application programming interface (API) on third-party provider OpenRouter with roughly a 202.8K-token context window, 131.1K max output, and listed pricing of $0.96 per million input tokens and $3.20 per million output tokens. That makes it about $0.04 cheaper per total input and output cost (at 1 million tokens) than its predecessor, according to our calculations. Model Input Output Total Cost Source Grok 4.1 Fast $0.20 $0.50 $0.70 xAI Gemini 3 Flash $0.50 $3.00 $3.50 Google Kimi-K2.5 $0.60 $3.00 $3.60 Moonshot GLM-5-Turbo $0.96 $3.20 $4.16 OpenRouter GLM-5 $1.00 $3.20 $4.20 Z.ai Claude Haiku 4.5 $1.00 $5.00 $6.00 Anthropic Qwen3-Max $1.20 $6.00 $7.20 Alibaba Cloud Gemini 3 Pro $2.00 $12.00 $14.00 Google GPT-5.2 $1.75 $14.00 $15.75 OpenAI GPT-5.4 $2.50 $15.00 $17.50 OpenAI Claude Sonnet 4.5 $3.00 $15.00 $18.00 Anthropic Claude Opus 4.6 $5.00 $25.00 $30.00 Anthropic GPT-5.4 Pro $30.00 $180.00 $210.00 OpenAI Second, Z.ai is also adding the model to its GLM Coding subscription product, which is its packaged coding assistant service. That service has three tiers: Lite at $27 per quarter, Pro at $81 per quarter, and Max at $216 per quarter. Z.ai’s March 15 rollout note says Pro subscribers get GLM-5-Turbo in March, while Lite subscribers get the base GLM-5 in March and must wait until April for GLM-5-Turbo. The company is also taking early-access applications for enterprises via a Google Form, which suggests some users may get access ahead of that schedule depending on capacity. z.ai describes GLM-5-Turbo as designed for “fast inference” and “deeply optimized for real-wor

Elon Musk pushes out more xAI founders as AI coding effort falters

<a href="https:&#x2F;&#x2F;archive.ph&#x2F;rP4cb" rel="nofollow">https:&#x2F;&#x2F;archive.ph&#x2F;rP4cb</a> (text at bottom)<p><a href="https:&#x2F;&#x2F;x.com&#x2F;elonmusk&#x2F;status&#x2F;2032201568335044978" rel="nofollow">https:&#x2F;&#x2F;x.com&#x2F;elonmusk&#x2F;status&#x2F;2032201568335044978</a>, <a href="https:&#x2F;&#x2F;xcancel.com&#x2F;elonmusk&#x2F;status&#x2F;2032201568335044978" rel="nofollow">https:&#x2F;&#x2F;xcancel.com&#x2F;elonmusk&#x2F;status&#x2F;2032201568335044978</a><p><a href="https:&#x2F;&#x2F;economictimes.indiatimes.com&#x2F;tech&#x2F;artificial-intelligence&#x2F;musk-ousts-more-xai-founders-as-ai-coding-effort-falters-ft-reports&#x2F;articleshow&#x2F;129560405.cms" rel="nofollow">https:&#x2F;&#x2F;economictimes.indiatimes.com&#x2F;tech&#x2F;artificial-intelli...</a><p><a href="https:&#x2F;&#x2F;futurism.com&#x2F;artificial-intelligence&#x2F;elon-musk-screwed-up-xai-rebuilding" rel="nofollow">https:&#x2F;&#x2F;futurism.com&#x2F;artificial-intelligence&#x2F;elon-musk-screw...</a>

Artificial Analysis Intelligence Index and cost benchmarks are useful decision/guidance determinants for which models to use. Analysis for top models.

# AI Intelligence and Benchmarking Cost (Feb 2026) As per the **Artificial Analysis Intelligence Index v4.0** (February 2026), the scoring ceiling is set by **Claude Opus 4.6 (max) at 53**. ## Adjusted Score Formula The "Adjusted Score" follows a quadratic penalty formula: ``` Adjusted Score = 53 × (1 - (53 - Intel Score)² / 53²) ``` This creates a steeper penalty for performance gaps compared to a linear scale. ## Model Comparison Table | Lab | Model | Intel Score | Adjusted Score | Benchmark Cost | Intel Ratio (Score/Cost) | Adj. Ratio (Adj/Cost) | |-----------|-------|-------------|----------------|----------------|--------------------------|----------------------| | Anthropic | Claude Opus 4.6 (max) | 53 | 53 | $2,486.45 | 0.021 | 0.021 | | OpenAI | GPT-5.2 (xhigh) | 51 | 49 | $2,304.00* | 0.022 | 0.021 | | Zhipu AI | GLM-5 (Reasoning) | 50 | 47 | $384.00* | 0.130 | 0.122 | | Google | Gemini 3 Pro | 48 | 43 | $1,179.00* | 0.041 | 0.036 | | MiniMax | MiniMax-M2.5 | 42 | 31 | $124.58 | 0.337 | 0.249 | | DeepSeek | DeepSeek V3.2 (Reasoning) | 42 | 31 | $70.64 | 0.595 | 0.439 | | xAI | Grok 4 (Reasoning) | 41 | 29 | $1,568.34 | 0.026 | 0.018 | *\*Benchmark costs for proprietary models are based on Artificial Analysis evaluation token counts (typically 12M–88M depending on verbosity) multiplied by current API rates.* ## Key Insights 1. **High token reasoning models**: Grok 4 and Claude Opus 4.6 use a high number of tokens during reasoning, up to **88M tokens**. This results in low Intel-to-Cost ratios despite high scores. 2. **DeepSeek V3.2 is the most efficient**: It provides an adjusted intelligence ratio that is roughly **20 times better** than the proprietary frontier. 3. **Cost efficiency comparison**: MiniMax-M2.5 and DeepSeek V3.2 share a score of 42. DeepSeek is almost **twice as cost-effective** due to lower API pricing and higher token efficiency. ## Visual Summary ``` Intel Score vs Cost Efficiency (Adjusted Ratio) ───────────────────────────────────────────────── DeepSeek V3.2 ████████████████████████████ 0.439 MiniMax-M2.5 ███████████████ 0.249 GLM-5 ███████ 0.122 Gemini 3 Pro ██ 0.036 Claude Opus 4.6 █ 0.021 GPT-5.2 █ 0.021 Grok 4 █ 0.018 ``` --- *Source: Artificial Analysis Intelligence Index v4.0, February 2026* google AI mode made analysis, GLM 5 formatted and added cute graph. this combines the intelligence score and cost to run the intelligence benchmark from https://artificialanalysis.ai/?endpoints=openai_gpt-5-2-codex%2Cazure_kimi-k2-thinking%2Camazon-bedrock_qwen3-coder-480b-a35b-instruct%2Camazon-bedrock_qwen3-coder-30b-a3b-instruct%2Ctogetherai_minimax-m2-5_fp4%2Ctogetherai_glm-5_fp4%2Ctogetherai_qwen3-next-80b-a3b-reasoning%2Cgoogle_gemini-3-pro_ai-studio%2Cgoogle_glm-4-7%2Cmoonshot-ai_kimi-k2-thinking_turbo%2Cnovita_glm-5_fp8 look at intelligence vs cost graph for further insight. You can add much smaller models for comparison to LLMs you might run locally. The adjusted intelligence/cost metric is a useful heuristic for "how much would you pay extra to get top score". Choosing non-open models requires a much higher penalty than 2x the difference/comparison to highest score. Quantized versions don't seem to score lower. This site provides good base info to make your own model of "score deficit", model size, tps as a combined score relative to tokens/cost to get a benchmark score. I was originally researching how grok 4.2 approach would inflate costs vs performance, but it is not yet benchmarked.