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Put up to 4 models beside each other — token prices, context windows, capabilities and provider, from the same catalogue the model pages read.

  1. Muse Spark OpenMetaRemove
  2. Kimi K3.1 ProMoonShot AIRemove
  3. DeepSeek V4 FlashDeepSeekRemove
muse-spark-open vs kimi-k3.1-pro vs deepseek-v4-flash
AttributeMuse Spark Openmuse-spark-openKimi K3.1 Prokimi-k3.1-proDeepSeek V4 Flashdeepseek-v4-flash
Pricing
Input$1.25 / 1M$2.00 / 1M$0.14 / 1M
Output$4.25 / 1M$10.00 / 1M$0.28 / 1M
Cache Write (5m)$1.25 / 1M$2.00 / 1M$0.14 / 1M
Cache Write (1h)$1.25 / 1M$2.00 / 1M$0.14 / 1M
Cache Read$1.25 / 1M$2.00 / 1M$0.14 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M1.0M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderMetaMoonShot AIDeepSeek
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released———
Description
SummaryMuse 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.Kimi K3.1 Pro is Moonshot AI's flagship open-weight multimodal reasoning model, building on Kimi K3 with enhanced post-training for advanced reasoning and autonomous task execution. It features a 1M-token context window and native image and video understanding, enabling complex, long-context multimodal workflows. Optimized for long-horizon coding, 3D creation, financial research, and end-to-end agentic knowledge work, Kimi K3.1 Pro is well suited for autonomous software engineering, multimodal analysis, and complex professional workflows that require sustained reasoning and reliable execution.DeepSeek V4 Flash is an efficiency-optimized Mixture-of-Experts (MoE) model with 284B total parameters and 13B activated per token, designed for fast inference and high-throughput workloads. It supports a 1M-token context window, enabling large-scale reasoning and long-context processing. Built with hybrid attention for efficiency, the model maintains strong performance in reasoning and coding while offering configurable reasoning modes. It is well suited for coding assistants, chat systems, and agent workflows where responsiveness and cost efficiency are critical.