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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. Kimi K3.1 ProMoonShot AIRemove
  2. Muse Spark OpenMetaRemove
  3. Qwen3.5-122B-A10BAlibabaRemove
kimi-k3.1-pro vs muse-spark-open vs qwen3.5-122b-a10b
AttributeKimi K3.1 Prokimi-k3.1-proMuse Spark Openmuse-spark-openQwen3.5-122B-A10Bqwen3.5-122b-a10b
Pricing
Input$2.00 / 1M$1.25 / 1M$0.40 / 1M
Output$10.00 / 1M$4.25 / 1M$3.20 / 1M
Cache Write (5m)$2.00 / 1M$1.25 / 1M$0.40 / 1M
Cache Write (1h)$2.00 / 1M$1.25 / 1M$0.40 / 1M
Cache Read$2.00 / 1M$1.25 / 1M$0.40 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M262.1K
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderMoonShot AIMetaAlibaba
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released———
Description
SummaryKimi 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.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.Qwen3.5-122B-A10B is a native vision-language model built on a hybrid architecture that combines linear attention mechanisms with a sparse Mixture-of-Experts (MoE) design for improved inference efficiency. In overall performance, it ranks just below Qwen3.5-397B-A17B, delivering substantial gains over previous generations. Its text capabilities significantly exceed Qwen3-235B-2507, while its visual performance surpasses Qwen3-VL-235B, making it a strong high-end option for advanced multimodal and agent-driven applications.