Output tokens cost $0.24 per million on Qwen/Qwen2.5-VL-32B-Instruct and $6.00 per million on Grok 4.5. Input tokens cost $0.24 per million on Qwen/Qwen2.5-VL-32B-Instruct and $2.00 per million on Grok 4.5. Cached input tokens are billed at $0.24 per million on Qwen/Qwen2.5-VL-32B-Instruct and $0.50 per million on Grok 4.5.
Qwen/Qwen2.5-VL-32B-Instruct
Grok 4.5Qwen2.5-VL-32B-Instruct is an advanced multimodal model from the Tongyi Qianwen team that can recognize objects, analyze text and graphics in images, operate tools, locate objects in images, and generate structured outputs. Through reinforcement learning, it has improved mathematics and problem-solving capabilities, with a more concise and natural response style.
Grok 4.5 was trained on datasets spanning knowledge in coding, science, engineering, and math. With both intelligent and efficient reasoning, Grok 4.5 excels at real engineering tasks and exceeds comparable leading models at these tasks.
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.
Qwen/Qwen2.5-VL-32B-Instruct: $0.24/M output tokens; Grok 4.5: $6.00/M. Use the cost calculator above to estimate your own workload.
Qwen/Qwen2.5-VL-32B-Instruct accepts text, image and video input and supports tool calling, function calling and structured outputs; Grok 4.5 accepts text and image input and supports thinking, tool calling, function calling, structured outputs and long context.
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