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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. Qwen3.8 2.4T A95BAlibabaRemove
  2. Muse Spark OpenMetaRemove
  3. Nemotron 3.5 Super VL (Free)NVIDIARemove
qwen3.8-2.4t-a95b vs muse-spark-open vs nemotron-3.5-super-vl:free
AttributeQwen3.8 2.4T A95Bqwen3.8-2.4t-a95bMuse Spark Openmuse-spark-openNemotron 3.5 Super VL (Free)nemotron-3.5-super-vl:free
Pricing
Input$1.80 / 1M$1.25 / 1M$0 / 1M
Output$5.40 / 1M$4.25 / 1M$0 / 1M
Cache Write (5m)$1.80 / 1M$1.25 / 1M—
Cache Write (1h)$1.80 / 1M$1.25 / 1M—
Cache Read$1.80 / 1M$1.25 / 1M$0 / 1M
Web Search$0 / 1M$0 / 1M—
Cache Write——$0 / 1M
Context
Max context262K1M1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderAlibabaMetaNVIDIA
Categorychatchatchat
Charge typePay As You GoPay As You GoFree
Released———
Description
SummaryQwen3.8 2.4T A95B is Qwen's open-weight sparse Mixture-of-Experts (MoE) model and the open-weight counterpart to Qwen3.8 Max. It features 2.4T total parameters with 95B activated per token, combining frontier-scale capacity with efficient sparse inference. Designed for coding, research, complex reasoning, and agentic workflows, the model is well suited for demanding long-horizon tasks and advanced autonomous systems while providing the flexibility and customization benefits of open weights.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.NVIDIA Nemotron 3.5 Super VL is an open multimodal Mixture-of-Experts (MoE) model featuring 120B total parameters with 12B activated per token. Built on a hybrid Mamba–Transformer architecture with multi-token prediction, it supports text, images, documents, GUI screenshots, and video inputs, along with a 1M-token context window for complex, long-horizon workflows. Optimized for multimodal reasoning, visual understanding, and agentic tool use, the model supports configurable reasoning modes and token budgets. It is well suited for document intelligence, web research, coding validation, customer service, GUI analysis, and video understanding, enabling efficient, long-running multimodal agent applications.