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Compare models

Put up to 4 models beside each other — token prices, context windows, capabilities and provider, from the same catalogue the model pages read.

  1. Universal-3.5 ProAssemblyAIRemove
  2. GPT-6 Luna ProOpenAIRemove
  3. MiMo-V2-ProXiaomiRemove
universal-3-5-pro vs gpt-6-luna-pro vs mimo-v2-pro
AttributeUniversal-3.5 Prouniversal-3-5-proGPT-6 Luna Progpt-6-luna-proMiMo-V2-Promimo-v2-pro
Pricing
Input— Not priced per input token$0.10 / 1M$1.00 / 1M
Output— Not priced per output token$0.50 / 1M$3.00 / 1M
Cache Write (5m)Not applicable$0.10 / 1M$1.00 / 1M
Cache Write (1h)Not applicable$0.10 / 1M$1.00 / 1M
Cache ReadNot applicable$0.10 / 1M$1.00 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max contextN/A1.1M1.0M
Max outputN/AN/AN/A
Capabilities
VisionNoYesYes
Function CallingNoYesYes
JSON ModeNoYesYes
StreamingNoYesYes
Catalogue
ProviderAssemblyAIOpenAIXiaomi
Categoryvoicechatchat
Charge typePay As You GoPay As You GoPay As You Go
Released2026-09-22——
Description
SummaryAssemblyAI Universal-3.5 Pro speech-to-text. Billed per second of audio.GPT-6 Luna Pro uses the same underlying model as GPT-6 Luna, but runs with reasoning.mode set to pro for higher-quality responses on complex tasks. It combines Luna's speed and cost efficiency with deeper reasoning and more reliable multi-step execution, making it well suited for advanced coding, computer use, and agentic workflows where higher solution quality is needed without moving to a larger GPT-6 tier.MiMo-V2-Pro is Xiaomi's flagship foundation model with over 1T parameters and a 1M-token context window, optimized for advanced agentic workflows. It is highly adaptable to general agent frameworks such as OpenClaw, delivering strong performance in complex, real-world task execution. Ranking among the top tier on benchmarks like PinchBench and ClawBench, with performance approaching models like Opus 4.6, MiMo-V2-Pro is designed to act as the core intelligence of agent systems, orchestrating workflows, driving production engineering tasks, and delivering reliable results at scale.