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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 1.3MetaRemove
  2. Nemotron Nano 9B V2 (Free)NVIDIARemove
  3. Gemini 3.8 FlashGoogleRemove
muse-spark-1.3 vs nemotron-nano-9b-v2 vs gemini-3.8-flash
AttributeMuse Spark 1.3muse-spark-1.3Nemotron Nano 9B V2 (Free)nemotron-nano-9b-v2Gemini 3.8 Flashgemini-3.8-flash
Pricing
Input$1.25 / 1M$0 / 1M$0.75 / 1M
Output$4.25 / 1M$0 / 1M$3.75 / 1M
Cache Write (5m)$1.25 / 1M$0.75 / 1M
Cache Write (1h)$1.25 / 1M$0.75 / 1M
Cache Read$1.25 / 1M$0 / 1M$0.75 / 1M
Web Search$0 / 1M$0 / 1M
Cache Write$0 / 1M
Context
Max context1M131.1K1M
Max outputN/AN/AN/A
Capabilities
VisionYesNoYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderMetaNVIDIAGoogle
Categorychatchatchat
Charge typePay As You GoFreePay As You Go
Released
Description
SummaryMuse Spark 1.3 is Meta's multimodal reasoning model designed for long-running agentic, multi-agent, and coding workflows. It maintains context and information across extended tasks, enabling reliable execution in complex, multi-step environments. The model is optimized to resolve conflicting information, seek clarification or confirmation when necessary, and execute concisely, making it well suited for autonomous agents, collaborative multi-agent systems, and long-horizon software engineering workflows.NVIDIA Nemotron Nano 9B v2 is a 9B-parameter language model trained from scratch by NVIDIA, designed to handle both reasoning and non-reasoning tasks. It can generate an internal reasoning trace before producing a final answer, and this behavior is configurable via system prompts—allowing developers to enable or suppress visible reasoning as needed.Gemini 3.8 Flash is Google's most intelligent Flash-class model, delivering significant improvements over Gemini 3.7 Flash across software engineering, agentic workflows, and complex multi-step reasoning. Designed to combine strong capability with Flash-tier efficiency, it is well suited for coding assistants, autonomous agents, and high-throughput production workflows that require responsive performance without sacrificing reasoning quality.