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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. Mistral Small 4Mistral AIRemove
  2. Muse Spark OpenMetaRemove
  3. Nemotron 3.5 Super VLNVIDIARemove
mistral-small-2603 vs muse-spark-open vs nemotron-3.5-super-vl
AttributeMistral Small 4mistral-small-2603Muse Spark Openmuse-spark-openNemotron 3.5 Super VLnemotron-3.5-super-vl
Pricing
Input$0.15 / 1M$1.25 / 1M$0.10 / 1M
Output$0.60 / 1M$4.25 / 1M$0.40 / 1M
Cache Write (5m)$0.15 / 1M$1.25 / 1M$0.10 / 1M
Cache Write (1h)$0.15 / 1M$1.25 / 1M$0.10 / 1M
Cache Read$0.15 / 1M$1.25 / 1M$0.10 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context262.1K1M1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderMistral AIMetaNVIDIA
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released———
Description
SummaryMistral Small 4 is the latest release in the Mistral Small family, unifying capabilities from multiple flagship models into a single system. It integrates strong reasoning (Magistral), multimodal understanding (Pixtral), and agentic coding capabilities (Devstral), enabling a versatile, all-in-one model. Designed to handle complex analysis, software development, and visual tasks within the same workflow, Mistral Small 4 is well suited for integrated agentic applications and end-to-end problem solving across domains.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.