LFM2-24B-A2B is the largest model in the LFM2 family of hybrid architectures designed for efficient on-device deployment. Built as a 24B parameter Mixture-of-Experts model with only 2B active parameters per token, it delivers high-quality g…
Model details →LiquidAI: LFM2-24B-A2B vs Qwen: Qwen3 8B
Qwen: Qwen3 8B wins on 3 of 6 axes — pricing and capability skew in its favour for most workloads.
Qwen3-8B is a dense 8.2B parameter causal language model from the Qwen3 series, designed for both reasoning-heavy tasks and efficient dialogue. It supports seamless switching between "thinking" mode for math, coding, and logical inference, …
Model details →Side-by-side comparison
| Capability | LiquidAI: LFM2-24B-A2B | Qwen: Qwen3 8B | Winner |
|---|---|---|---|
| Context window Maximum number of input tokens the model can attend to in a single request. | 33K | 41K | 🏆 Qwen: Qwen3 8B |
| Input price (per 1M) Cost per million input tokens billed by the provider. | $0.03 | $0.05 | 🏆 LiquidAI: LFM2-24B-A2B |
| Output price (per 1M) Cost per million output tokens billed by the provider. | $0.12 | $0.40 | 🏆 LiquidAI: LFM2-24B-A2B |
| Tool / function calling First-class support for emitting structured tool calls. | — | Yes | 🏆 Qwen: Qwen3 8B |
| Vision input Accepts image inputs alongside text. | — | — | Tie |
| Reasoning mode Internal chain-of-thought / extended-thinking support. | — | Yes | 🏆 Qwen: Qwen3 8B |
Frequently asked questions
Is LiquidAI: LFM2-24B-A2B better than Qwen: Qwen3 8B?
Qwen: Qwen3 8B wins on 3 of 6 axes — pricing and capability skew in its favour for most workloads.
What's the price difference between LiquidAI: LFM2-24B-A2B and Qwen: Qwen3 8B?
Input: $0.03 vs $0.05 per 1M tokens. Output: $0.12 vs $0.40 per 1M tokens.
What context windows do LiquidAI: LFM2-24B-A2B and Qwen: Qwen3 8B support?
LiquidAI: LFM2-24B-A2B supports up to 33K tokens. Qwen: Qwen3 8B supports up to 41K tokens.
Do both LiquidAI: LFM2-24B-A2B and Qwen: Qwen3 8B support tool calling?
LiquidAI: LFM2-24B-A2B: not advertised. Qwen: Qwen3 8B: yes.