Compare GLM 5.2 from Z.AI and Llama 4 Scout from Llama on key metrics including benchmarks, price, context length, and other model features. Access both models and hundreds of others through the AIHubMix API.
GLM 5.2
Llama 4 ScoutGLM-5.2 is Z.ai’s flagship model for the era of long-horizon tasks. With a truly usable 1M-token context window, it can handle project-level engineering context, execute long-running tasks more reliably, follow engineering standards more consistently, and complete the full development workflow from requirements to multi-platform deployment in a single task.
Llama 4 Scout is a highly efficient Mixture-of-Experts (MoE) model from Meta, activating 17B out of 109B total parameters per inference. It natively supports multimodal input (text and image) and multilingual output (text and code) across 12 languages. Designed for assistant-style interaction and visual reasoning, Scout features a massive 10-million-token context window. It is instruction-tuned for tasks like multilingual chat and image understanding and is released under the Llama 4 Community License for local or commercial deployment.
Prices are per million tokens. Time to First Token and throughput are rolling averages measured on AIHubMix.
Promotional prices show the discounted rate; see each model page for promotion windows.
Daily traffic served through AIHubMix — how demand for each model is trending.
Measured on real AIHubMix traffic, hourly buckets. Gaps mean no traffic in that hour.
LMArena ratings by capability (Bradley-Terry, commonly called Elo). Higher is better.
Source: LMArena (arena.ai) leaderboard, imported by AIHubMix. Models without published ratings are omitted per chart.
Estimate your monthly bill for the same workload on each model.
Monthly = daily × 30. Discounted rates applied where a promotion is active.
Llama 4 Scout: $0.20/M output tokens; GLM 5.2: $3.94/M. Use the cost calculator above to estimate your own workload.
GLM 5.2: 1505; Llama 4 Scout: 1362 (LMArena coding leaderboard).
Llama 4 Scout: 0.3s time to first token measured on AIHubMix; see the live performance charts above for how each model behaves across the day.
GLM 5.2 accepts 1,000,000 and Llama 4 Scout accepts 131,000 input tokens. Maximum output per request is 128,000 tokens on GLM 5.2 and 131,000 tokens on Llama 4 Scout.
Llama 4 Scout at 2637.0 tok/s and GLM 5.2 at 34.2 tok/s, measured as output throughput on AIHubMix — a separate metric from time to first token.
GLM 5.2 accepts text input and supports thinking, tool calling, function calling and structured outputs; Llama 4 Scout accepts text and image input and supports tool calling, function calling and structured outputs.
Yes. AIHubMix serves every model on this page behind one OpenAI-compatible endpoint, so switching between them is a one-line change to the model field — no second account, key or SDK.
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