GLM-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.
GLM 5.2 vs Qwen3.5 122B A10B
Output tokens cost $3.94 per million on GLM 5.2 and $0.90 per million on Qwen3.5 122B A10B. Input tokens cost $1.13 per million on GLM 5.2 and $0.11 per million on Qwen3.5 122B A10B. Cached input tokens are billed at $0.28 per million on GLM 5.2 and $0.11 per million on Qwen3.5 122B A10B. Context lengths are 1,000,000 tokens on GLM 5.2 and 991,000 tokens on Qwen3.5 122B A10B. Time to first token (TTFT) measured on AIHubMix is 1.1s on GLM 5.2 and 1.5s on Qwen3.5 122B A10B. Measured output throughput is 37.9 tok/s on GLM 5.2 and 61.8 tok/s on Qwen3.5 122B A10B. On the LMArena coding leaderboard GLM 5.2 scores 1505 and Qwen3.5 122B A10B scores 1459.
GLM 5.2The Qwen 3.5 native vision-language Plus model is built on a hybrid architecture that integrates linear attention mechanisms with sparse mixture-of-experts models, achieving higher inference efficiency. In multiple task evaluations, the 3.5 series has demonstrated outstanding performance comparable to current leading frontier models, with leapfrog improvements over the 3 series in both pure-text and multimodal capabilities. This model version is functionally equivalent to the snapshot model qwen3.5-plus-2026-02-15.
Pricing & Specifications
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.
Activity Past 30 Days
Daily traffic served through AIHubMix — how demand for each model is trending.
Tokens / day
Requests / day
Performance Past 3 Days
Measured on real AIHubMix traffic, hourly buckets. Gaps mean no traffic in that hour.
Throughput (tok/s)
TTFT (s)
Uptime (%)
LMArena Benchmarks
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.
Cost calculator
Estimate your monthly bill for the same workload on each model.
Monthly = daily × 30. Discounted rates applied where a promotion is active.
FAQ
Which is cheaper: GLM 5.2, Qwen3.5 122B A10B?
Qwen3.5 122B A10B: $0.90/M output tokens; GLM 5.2: $3.94/M. Use the cost calculator above to estimate your own workload.
How do their coding arena scores compare?
GLM 5.2: 1505; Qwen3.5 122B A10B: 1459 (LMArena coding leaderboard).
Which responds faster?
GLM 5.2: 1.1s time to first token measured on AIHubMix; see the live performance charts above for how each model behaves across the day.
How large is each context window?
GLM 5.2 accepts 1,000,000 and Qwen3.5 122B A10B accepts 991,000 input tokens. Maximum output per request is 128,000 tokens on GLM 5.2 and 64,000 tokens on Qwen3.5 122B A10B.
Which one generates tokens faster?
Qwen3.5 122B A10B at 61.8 tok/s and GLM 5.2 at 37.9 tok/s, measured as output throughput on AIHubMix — a separate metric from time to first token.
What inputs and capabilities does each model support?
GLM 5.2 accepts text input and supports thinking, tool calling, function calling and structured outputs; Qwen3.5 122B A10B accepts text, image and video input and supports tool calling, function calling, structured outputs, web search, long context and thinking.
Can I call GLM 5.2 and Qwen3.5 122B A10B with the same API key?
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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