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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. Gemini 3.5 TranscribeGoogleRemove
  2. GPT-6 LunaOpenAIRemove
  3. Perceptron Mk1.5PerceptronRemove
  4. MiMo-V2-FlashXiaomiRemove

4 is the maximum. Remove one to add another.

gemini-3.5-transcribe vs gpt-6-luna vs perceptron-mk1.5 vs mimo-v2-flash
AttributeGemini 3.5 Transcribegemini-3.5-transcribeGPT-6 Lunagpt-6-lunaPerceptron Mk1.5perceptron-mk1.5MiMo-V2-Flashmimo-v2-flash
Pricing
Input— Not priced per input token$0.10 / 1M$0.15 / 1M$0.09 / 1M
Output— Not priced per output token$0.50 / 1M$1.50 / 1M$0.29 / 1M
Cache Write (5m)Not applicable$0.10 / 1M$0.15 / 1M$0.09 / 1M
Cache Write (1h)Not applicable$0.10 / 1M$0.15 / 1M$0.09 / 1M
Cache ReadNot applicable$0.10 / 1M$0.15 / 1M$0.09 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context98.3K1.1M36.9K262.1K
Max outputN/AN/AN/AN/A
Capabilities
VisionYesYesNoYes
Function CallingYesYesNoYes
JSON ModeNoYesNoYes
StreamingNoYesYesYes
Catalogue
ProviderGoogleOpenAIPerceptronXiaomi
Categoryvoicechatchatchat
Charge typePay As You GoPay As You GoPay As You GoPay As You Go
Released2026-09-25—2026-09-25—
Description
SummaryGoogle Gemini 3.5 Transcribe speech-to-text. Billed per input and output token.GPT-6 Luna is the fast, cost-efficient model in OpenAI's GPT-6 series, optimized for high-volume and latency-sensitive workloads such as chat, classification, and lightweight agentic tasks. It combines low-cost, responsive inference with the GPT-6 family’s improvements in factual reliability and clear, concise communication. At higher reasoning effort, GPT-6 Luna can also handle complex software engineering and computer-use workflows that previously required a Sol-tier model, making it a versatile choice for scalable production applications that need to balance speed, cost, and capability.Perceptron Mk1.5 chat model that accepts audio input. Audio and text input are billed per token.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.