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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. Universal-3.5 ProAssemblyAIRemove
  2. MiMo-V2-FlashXiaomiRemove
  3. GPT-6 Luna ProOpenAIRemove
universal-3-5-pro vs mimo-v2-flash vs gpt-6-luna-pro
AttributeUniversal-3.5 Prouniversal-3-5-proMiMo-V2-Flashmimo-v2-flashGPT-6 Luna Progpt-6-luna-pro
Pricing
Input— Not priced per input token$0.09 / 1M$0.10 / 1M
Output— Not priced per output token$0.29 / 1M$0.50 / 1M
Cache Write (5m)Not applicable$0.09 / 1M$0.10 / 1M
Cache Write (1h)Not applicable$0.09 / 1M$0.10 / 1M
Cache ReadNot applicable$0.09 / 1M$0.10 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max contextN/A262.1K1.1M
Max outputN/AN/AN/A
Capabilities
VisionNoYesYes
Function CallingNoYesYes
JSON ModeNoYesYes
StreamingNoYesYes
Catalogue
ProviderAssemblyAIXiaomiOpenAI
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.MiMo-V2-Flash is an open-source Mixture-of-Experts (MoE) foundation model developed by Xiaomi, featuring 309B total parameters with 15B activated per token and a hybrid attention architecture. It supports a 256K context window and a hybrid thinking mode toggle, enabling flexible trade-offs between speed and reasoning depth. The model excels in reasoning, coding, and agentic workflows, ranking #1 globally among open-source models on benchmarks such as SWE-bench Verified and SWE-bench Multilingual. With performance comparable to leading proprietary models like Claude Sonnet 4.5 at a fraction of the cost, MiMo-V2-Flash is well suited for efficient, high-performance deployments.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.