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.
| Attribute | DeepSeek V4.1 Flashdeepseek-v4.1-flash | Ministral 3 8B 2512ministral-8b-2512 | Muse Spark 1.3muse-spark-1.3 |
|---|---|---|---|
| Pricing | |||
| Input | $0.30 / 1M | $0.15 / 1M | $1.25 / 1M |
| Output | $1.20 / 1M | $0.15 / 1M | $4.25 / 1M |
| Cache Write (5m) | $0.30 / 1M | $0.15 / 1M | $1.25 / 1M |
| Cache Write (1h) | $0.30 / 1M | $0.15 / 1M | $1.25 / 1M |
| Cache Read | $0.30 / 1M | $0.15 / 1M | $1.25 / 1M |
| Web Search | $0 / 1M | $0 / 1M | $0 / 1M |
| Context | |||
| Max context | 1M | 262.1K | 1M |
| Max output | N/A | N/A | N/A |
| Capabilities | |||
| Vision | Yes | Yes | Yes |
| Function Calling | Yes | Yes | Yes |
| JSON Mode | Yes | Yes | Yes |
| Streaming | Yes | Yes | Yes |
| Catalogue | |||
| Provider | DeepSeek | Mistral AI | Meta |
| Category | chat | chat | chat |
| Charge type | Pay As You Go | Pay As You Go | Pay As You Go |
| Released | — | — | — |
| Description | |||
| Summary | 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. | Ministral 3 8B is the balanced model in the Ministral 3 series — a compact, efficient language model that still delivers strong performance and includes vision capabilities. | 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. |