Mercury is the first diffusion large language model (dLLM). Applying a breakthrough discrete diffusion approach, the model runs 5-10x faster than even speed optimized models like GPT-4.1 Nano and Claude 3.5 Haiku while matching their perfor…
Model details →Inception: Mercury vs Qwen: Qwen3 32B
Qwen: Qwen3 32B wins on 4 of 6 axes — pricing and capability skew in its favour for most workloads.
Qwen3-32B is a dense 32.8B parameter causal language model from the Qwen3 series, optimized for both complex reasoning and efficient dialogue. It supports seamless switching between a "thinking" mode for...
Model details →Side-by-side comparison
| Capability | Inception: Mercury | Qwen: Qwen3 32B | Winner |
|---|---|---|---|
| Context window Maximum number of input tokens the model can attend to in a single request. | 128K | 131K | 🏆 Qwen: Qwen3 32B |
| Input price (per 1M) Cost per million input tokens billed by the provider. | $0.25 | $0.08 | 🏆 Qwen: Qwen3 32B |
| Output price (per 1M) Cost per million output tokens billed by the provider. | $0.75 | $0.28 | 🏆 Qwen: Qwen3 32B |
| Tool / function calling First-class support for emitting structured tool calls. | Yes | Yes | Tie |
| Vision input Accepts image inputs alongside text. | — | — | Tie |
| Reasoning mode Internal chain-of-thought / extended-thinking support. | — | Yes | 🏆 Qwen: Qwen3 32B |
Frequently asked questions
Is Inception: Mercury better than Qwen: Qwen3 32B?
Qwen: Qwen3 32B wins on 4 of 6 axes — pricing and capability skew in its favour for most workloads.
What's the price difference between Inception: Mercury and Qwen: Qwen3 32B?
Input: $0.25 vs $0.08 per 1M tokens. Output: $0.75 vs $0.28 per 1M tokens.
What context windows do Inception: Mercury and Qwen: Qwen3 32B support?
Inception: Mercury supports up to 128K tokens. Qwen: Qwen3 32B supports up to 131K tokens.
Do both Inception: Mercury and Qwen: Qwen3 32B support tool calling?
Inception: Mercury: yes. Qwen: Qwen3 32B: yes.