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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. Gemini 3.1 Flash Image (Google Vertex AI)GoogleEnterprise & partner accessRemove
  2. Nemotron 3.5 Lightning (Free)NVIDIARemove
  3. Muse Spark OpenMetaRemove
gemini-3.1-flash-image-vertex vs nemotron-3.5-lightning:free vs muse-spark-open
AttributeGemini 3.1 Flash Image (Google Vertex AI)gemini-3.1-flash-image-vertexNemotron 3.5 Lightning (Free)nemotron-3.5-lightning:freeMuse Spark Openmuse-spark-open
Pricing
Request$0.067 / request——
BillingPay Per Request——
Cache Write (5m)Not applicable—$1.25 / 1M
Cache Write (1h)Not applicable—$1.25 / 1M
Cache ReadNot applicable$0 / 1M$1.25 / 1M
Input—$0 / 1M$1.25 / 1M
Output—$0 / 1M$4.25 / 1M
Cache Write—$0 / 1M—
Web Search——$0 / 1M
Context
Max contextN/A1M1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingNoYesYes
JSON ModeNoYesYes
StreamingNoYesYes
Catalogue
ProviderGoogleNVIDIAMeta
Categoryimagechatchat
Charge typePay Per RequestFreePay As You Go
Released———
Description
SummaryGemini 3.1 Flash Image served from Google Vertex AI. Billed per image at US$0.067. Call it through /v1/chat/completions with stream set to false; the image is returned in the message content as a base64 data URI. Maximum output resolution is 2K. A request for a larger size, such as 4K or 4096x4096, returns a 2K image with the same aspect ratio at the same price. When a size is reduced, the response carries an X-Image-Resolution-Capped header with the requested and delivered size, for example 4096x4096->2048x2048.NVIDIA Nemotron 3.5 Lightning is an open Mixture-of-Experts (MoE) model with 30B total parameters and 3B active per token, optimized for high-throughput agentic workloads and efficient inference. Its lightweight active compute and open design make it well suited for specialized agents, domain-specific customization, and scalable production deployments where speed, cost efficiency, and adaptability are key.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.