A 12B parameter model with a 128k token context length built by Mistral in collaboration with NVIDIA. The model is multilingual, supporting English, French, German, Spanish, Italian, Portuguese, Chinese, Japanese,...
Model details →Mistral: Mistral Nemo vs Qwen: Qwen3 14B
Mistral: Mistral Nemo wins on 2 of 6 axes — pricing and capability skew in its favour for most workloads.
Qwen3-14B is a dense 14.8B parameter causal language model from the Qwen3 series, designed for both complex reasoning and efficient dialogue. It supports seamless switching between a "thinking" mode for...
Model details →Side-by-side comparison
| Capability | Mistral: Mistral Nemo | Qwen: Qwen3 14B | Winner |
|---|---|---|---|
| Context window Maximum number of input tokens the model can attend to in a single request. | 131K | 131K | Tie |
| Input price (per 1M) Cost per million input tokens billed by the provider. | $0.02 | $0.12 | 🏆 Mistral: Mistral Nemo |
| Output price (per 1M) Cost per million output tokens billed by the provider. | $0.03 | $0.24 | 🏆 Mistral: Mistral Nemo |
| 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 14B |
Frequently asked questions
Is Mistral: Mistral Nemo better than Qwen: Qwen3 14B?
Mistral: Mistral Nemo wins on 2 of 6 axes — pricing and capability skew in its favour for most workloads.
What's the price difference between Mistral: Mistral Nemo and Qwen: Qwen3 14B?
Input: $0.02 vs $0.12 per 1M tokens. Output: $0.03 vs $0.24 per 1M tokens.
What context windows do Mistral: Mistral Nemo and Qwen: Qwen3 14B support?
Mistral: Mistral Nemo supports up to 131K tokens. Qwen: Qwen3 14B supports up to 131K tokens.
Do both Mistral: Mistral Nemo and Qwen: Qwen3 14B support tool calling?
Mistral: Mistral Nemo: yes. Qwen: Qwen3 14B: yes.