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 | MiniMax M2.5 (Lightning)minimax-m2.5-lightning | Muse Spark Openmuse-spark-open | Gemini 4 Argongemini-4-argon |
|---|---|---|---|
| Pricing | |||
| Input | $0.30 / 1M | $1.25 / 1M | $2.00 / 1M |
| Output | $2.20 / 1M | $4.25 / 1M | $10.00 / 1M |
| Cache Write (5m) | $0.30 / 1M | $1.25 / 1M | $2.00 / 1M |
| Cache Write (1h) | $0.30 / 1M | $1.25 / 1M | $2.00 / 1M |
| Cache Read | $0.30 / 1M | $1.25 / 1M | $2.00 / 1M |
| Web Search | $0 / 1M | $0 / 1M | $0 / 1M |
| Context | |||
| Max context | 128K | 1M | 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 | MiniMax | Meta | |
| Category | chat | chat | chat |
| Charge type | Pay As You Go | Pay As You Go | Pay As You Go |
| Released | — | — | — |
| Description | |||
| Summary | MiniMax-M2.5-Lightning is the high-speed variant of the M2.5 series, optimized for low latency, real-time responsiveness, and high-frequency workloads. It retains the core planning and execution strengths of M2.5 while further improving inference efficiency and response speed, making it ideal for interactive applications, rapid coding assistance, and workflow automation. With enhanced cost efficiency and reduced latency, M2.5-Lightning is particularly well suited for high-throughput, always-on deployments and production environments where speed and scalability are critical. | Muse Spark Open is an open-weight, coding-optimized model designed for agentic software engineering workflows. It delivers strong first-attempt accuracy, reliable tool calling, and deep codebase understanding, enabling effective development across real-world repositories. Optimized for autonomous coding agents, complex code navigation, and tool-driven development, Muse Spark Open is well suited for software engineering automation, debugging, code generation, and multi-step coding workflows where accuracy and reliable execution are essential. | Gemini 4 Argon is Google's flagship reasoning model, built for complex software engineering, deep mathematical and scientific problem solving, and autonomous multi-step agents. Google trained it to keep context across long, multi-step tasks and to turn long documents mixing text, tables, charts and images into a single grounded output. It leads the Vals Index across finance, legal, coding and tax work, and supports long decode of up to 1M output tokens in a single response. |