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 | Muse Spark 1.2muse-spark-1.2 | DeepSeek V4.1 Flashdeepseek-v4.1-flash | GPT-6 Astra Progpt-6-astra-pro |
|---|---|---|---|
| Pricing | |||
| Input | $1.25 / 1M | $0.30 / 1M | $10.00 / 1M |
| Output | $4.25 / 1M | $1.20 / 1M | $50.00 / 1M |
| Cache Write (5m) | $1.25 / 1M | $0.30 / 1M | $10.00 / 1M |
| Cache Write (1h) | $1.25 / 1M | $0.30 / 1M | $10.00 / 1M |
| Cache Read | $1.25 / 1M | $0.30 / 1M | $10.00 / 1M |
| Web Search | $0 / 1M | $0 / 1M | $0 / 1M |
| Context | |||
| Max context | 1M | 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 | Meta | DeepSeek | OpenAI |
| Category | chat | chat | chat |
| Charge type | Pay As You Go | Pay As You Go | Pay As You Go |
| Released | — | — | — |
| Description | |||
| Summary | Muse Spark 1.2 is Meta's multimodal reasoning model designed for complex agentic and software engineering workflows. It supports text, image, video, audio, and PDF inputs with text output, and features a 1M-token context window for sustained reasoning across large, multi-stage tasks. Built for flexible multi-agent execution, Muse Spark 1.2 can serve as either a coordinating main agent or a parallel task-focused subagent. With configurable reasoning effort, structured outputs, parallel function calling, and broad coding-harness compatibility, it is well suited for multi-file refactoring, extended debugging, whole-repository generation, and long-horizon development 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. | GPT-6 Astra Pro uses the same underlying model as GPT-6 Astra, but runs with reasoning.mode set to pro for higher-quality responses on complex tasks. Optimized for deeper reasoning, greater accuracy, and more reliable multi-step execution, it is well suited for demanding coding, analysis, and agentic workflows where solution quality takes priority over speed and cost. |