Llama 4 Maverick 17B Instruct (128E) is a high-capacity multimodal language model from Meta, built on a mixture-of-experts (MoE) architecture with 128 experts and 17 billion active parameters per forward pass (400B total). It supports multi…
Model details →Meta: Llama 4 Maverick vs Qwen: Qwen-Plus
Meta: Llama 4 Maverick wins on 4 of 6 axes — pricing and capability skew in its favour for most workloads.
Qwen-Plus, based on the Qwen2.5 foundation model, is a 131K context model with a balanced performance, speed, and cost combination.
Model details →Side-by-side comparison
| Capability | Meta: Llama 4 Maverick | Qwen: Qwen-Plus | Winner |
|---|---|---|---|
| Context window Maximum number of input tokens the model can attend to in a single request. | 1.0M | 1M | 🏆 Meta: Llama 4 Maverick |
| Input price (per 1M) Cost per million input tokens billed by the provider. | $0.15 | $0.26 | 🏆 Meta: Llama 4 Maverick |
| Output price (per 1M) Cost per million output tokens billed by the provider. | $0.60 | $0.78 | 🏆 Meta: Llama 4 Maverick |
| Tool / function calling First-class support for emitting structured tool calls. | Yes | Yes | Tie |
| Vision input Accepts image inputs alongside text. | Yes | — | 🏆 Meta: Llama 4 Maverick |
| Reasoning mode Internal chain-of-thought / extended-thinking support. | — | — | Tie |
Frequently asked questions
Is Meta: Llama 4 Maverick better than Qwen: Qwen-Plus?
Meta: Llama 4 Maverick wins on 4 of 6 axes — pricing and capability skew in its favour for most workloads.
What's the price difference between Meta: Llama 4 Maverick and Qwen: Qwen-Plus?
Input: $0.15 vs $0.26 per 1M tokens. Output: $0.60 vs $0.78 per 1M tokens.
What context windows do Meta: Llama 4 Maverick and Qwen: Qwen-Plus support?
Meta: Llama 4 Maverick supports up to 1.0M tokens. Qwen: Qwen-Plus supports up to 1M tokens.
Do both Meta: Llama 4 Maverick and Qwen: Qwen-Plus support tool calling?
Meta: Llama 4 Maverick: yes. Qwen: Qwen-Plus: yes.