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. MiniMax M2MiniMaxRemove
  2. GPT-6 LunaOpenAIRemove
  3. Gemini 3.5 TranscribeGoogleRemove
minimax-m2 vs gpt-6-luna vs gemini-3.5-transcribe
AttributeMiniMax M2minimax-m2GPT-6 Lunagpt-6-lunaGemini 3.5 Transcribegemini-3.5-transcribe
Pricing
Input$0.15 / 1M$0.10 / 1M— Not priced per input token
Output$0.45 / 1M$0.50 / 1M— Not priced per output token
Cache Write (5m)$0.15 / 1M$0.10 / 1MNot applicable
Cache Write (1h)$0.15 / 1M$0.10 / 1MNot applicable
Cache Read$0.15 / 1M$0.10 / 1MNot applicable
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context196.6K1.1M98.3K
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesNo
StreamingYesYesNo
Catalogue
ProviderMiniMaxOpenAIGoogle
Categorychatchatvoice
Charge typePay As You GoPay As You GoPay As You Go
Released——2026-09-25
Description
SummaryMiniMax-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.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.Google Gemini 3.5 Transcribe speech-to-text. Billed per input and output token.