Compare models
Put up to 4 models beside each other — token prices, context windows, capabilities and provider, from the same catalogue the model pages read.
- MiniMax M3MiniMaxRemove
- Muse Spark 1.3MetaRemove
- Hy4 previewTencentRemove
- DeepSeek V4.1 FlashDeepSeekRemove
4 is the maximum. Remove one to add another.
| Attribute | MiniMax M3minimax-m3 | Muse Spark 1.3muse-spark-1.3 | Hy4 previewhy4-preview | DeepSeek V4.1 Flashdeepseek-v4.1-flash |
|---|---|---|---|---|
| Pricing | ||||
| Input | $0.30 / 1M | $1.25 / 1M | $0.834 / 1M | $0.30 / 1M |
| Output | $1.20 / 1M | $4.25 / 1M | $2.50 / 1M | $1.20 / 1M |
| Cache Write (5m) | $0.30 / 1M | $1.25 / 1M | $0.834 / 1M | $0.30 / 1M |
| Cache Write (1h) | $0.30 / 1M | $1.25 / 1M | $0.834 / 1M | $0.30 / 1M |
| Cache Read | $0.30 / 1M | $1.25 / 1M | $0.834 / 1M | $0.30 / 1M |
| Web Search | $0 / 1M | $0 / 1M | $0 / 1M | $0 / 1M |
| Context | ||||
| Max context | 1M | 1M | 1M | 1M |
| Max output | N/A | N/A | N/A | N/A |
| Capabilities | ||||
| Vision | Yes | Yes | Yes | Yes |
| Function Calling | Yes | Yes | Yes | Yes |
| JSON Mode | Yes | Yes | Yes | Yes |
| Streaming | Yes | Yes | Yes | Yes |
| Catalogue | ||||
| Provider | MiniMax | Meta | Tencent | DeepSeek |
| Category | chat | chat | chat | chat |
| Charge type | Pay As You Go | Pay As You Go | Pay As You Go | Pay As You Go |
| Released | — | — | — | — |
| Description | ||||
| Summary | MiniMax-M3 is a multimodal foundation model from MiniMax, supporting text, image, and video inputs with text output and a 1M-token context window. It is designed for long-horizon agentic workflows, coding, and tool-driven task execution, enabling sustained reasoning across complex tasks. Built on MiniMax Sparse Attention (MSA), the model dramatically improves long-context efficiency by replacing full attention with KV-block selection, reducing compute costs at 1M-token contexts while maintaining strong performance. Trained as a native multimodal model and optimized for multi-turn, production-style collaboration, MiniMax-M3 excels at extended, multi-step workflows rather than single-turn interactions. | Muse Spark 1.3 is Meta's multimodal reasoning model designed for long-running agentic, multi-agent, and coding workflows. It maintains context and information across extended tasks, enabling reliable execution in complex, multi-step environments. The model is optimized to resolve conflicting information, seek clarification or confirmation when necessary, and execute concisely, making it well suited for autonomous agents, collaborative multi-agent systems, and long-horizon software engineering workflows. | Tencent Hy4 Preview is a Mixture-of-Experts (MoE) model from Tencent, featuring 770B total parameters with 49B activated per token. It is designed for coding agents, complex tool-driven workflows, and professional productivity tasks that require strong planning and reliable execution. Optimized for context continuity and sustained multi-step work, Hy4 Preview is well suited for long-horizon coding, agentic automation, tool orchestration, and complex real-world workflows. | DeepSeek V4.1 Flash is a cost-efficient sparse Mixture-of-Experts (MoE) model in DeepSeek's V4.1 family, optimized for coding, reasoning, and agentic workflows. Despite its efficiency-focused positioning, DeepSeek reports that it surpasses the previous V4 Pro in performance, inference speed, and overall task completion time. The model is particularly strong at long-horizon, multi-step execution, making it well suited for coding agents, complex problem solving, and autonomous workflows that must reliably carry tasks through to completion. |