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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. Transcribe 1 ProFish AudioRemove
  2. GPT-6 Luna ProOpenAIRemove
  3. MiniMax M2MiniMaxRemove
  4. Perceptron Mk1.5PerceptronRemove

4 is the maximum. Remove one to add another.

transcribe-1-pro vs gpt-6-luna-pro vs minimax-m2 vs perceptron-mk1.5
AttributeTranscribe 1 Protranscribe-1-proGPT-6 Luna Progpt-6-luna-proMiniMax M2minimax-m2Perceptron Mk1.5perceptron-mk1.5
Pricing
Input— Not priced per input token$0.10 / 1M$0.15 / 1M$0.15 / 1M
Output— Not priced per output token$0.50 / 1M$0.45 / 1M$1.50 / 1M
Cache Write (5m)Not applicable$0.10 / 1M$0.15 / 1M$0.15 / 1M
Cache Write (1h)Not applicable$0.10 / 1M$0.15 / 1M$0.15 / 1M
Cache ReadNot applicable$0.10 / 1M$0.15 / 1M$0.15 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max contextN/A1.1M196.6K36.9K
Max outputN/AN/AN/AN/A
Capabilities
VisionNoYesYesNo
Function CallingNoYesYesNo
JSON ModeNoYesYesNo
StreamingNoYesYesYes
Catalogue
ProviderFish AudioOpenAIMiniMaxPerceptron
Categoryvoicechatchatchat
Charge typePay As You GoPay As You GoPay As You GoPay As You Go
Released2026-09-24——2026-09-25
Description
SummaryFish Audio Transcribe 1 Pro speech-to-text with speaker labels; transcripts include speaker tags such as <|speaker:0|>. 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.MiniMax-M2 is a compact, high-efficiency model with 10B active (230B total) parameters, optimized for coding and agentic workflows. It delivers near-frontier reasoning and tool use, excels at multi-file coding tasks and compile-run-fix loops, and performs strongly on benchmarks like SWE-Bench and Terminal-Bench. It also handles long-horizon planning and recovery in agent evaluations, ranking among the top open models across reasoning domains. With fast inference and low cost, it’s ideal for large-scale agents and developer assistants — and works best when reasoning is preserved across turns.Perceptron Mk1.5 chat model that accepts audio input. Audio and text input are billed per token.