Skip to content

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

4 is the maximum. Remove one to add another.

gpt-6-luna vs minimax-m2 vs perceptron-mk1.5 vs transcribe-1-pro
AttributeGPT-6 Lunagpt-6-lunaMiniMax M2minimax-m2Perceptron Mk1.5perceptron-mk1.5Transcribe 1 Protranscribe-1-pro
Pricing
Input$0.10 / 1M$0.15 / 1M$0.15 / 1M— Not priced per input token
Output$0.50 / 1M$0.45 / 1M$1.50 / 1M— Not priced per output token
Cache Write (5m)$0.10 / 1M$0.15 / 1M$0.15 / 1MNot applicable
Cache Write (1h)$0.10 / 1M$0.15 / 1M$0.15 / 1MNot applicable
Cache Read$0.10 / 1M$0.15 / 1M$0.15 / 1MNot applicable
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context1.1M196.6K36.9KN/A
Max outputN/AN/AN/AN/A
Capabilities
VisionYesYesNoNo
Function CallingYesYesNoNo
JSON ModeYesYesNoNo
StreamingYesYesYesNo
Catalogue
ProviderOpenAIMiniMaxPerceptronFish Audio
Categorychatchatchatvoice
Charge typePay As You GoPay As You GoPay As You GoPay As You Go
Released——2026-09-252026-09-24
Description
SummaryGPT-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.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.Fish Audio Transcribe 1 Pro speech-to-text with speaker labels; transcripts include speaker tags such as <|speaker:0|>. Billed per second of audio.