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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. MAI-Transcribe 2MicrosoftRemove
  2. GPT-6.1 TerraOpenAIRemove
  3. Gemini 4 ArgonGoogleRemove
mai-transcribe-2 vs gpt-6.1-terra vs gemini-4-argon
AttributeMAI-Transcribe 2mai-transcribe-2GPT-6.1 Terragpt-6.1-terraGemini 4 Argongemini-4-argon
Pricing
Input— Not priced per input token$1.00 / 1M$2.00 / 1M
Output— Not priced per output token$5.00 / 1M$10.00 / 1M
Cache Write (5m)Not applicable$1.00 / 1M$2.00 / 1M
Cache Write (1h)Not applicable$1.00 / 1M$2.00 / 1M
Cache ReadNot applicable$1.00 / 1M$2.00 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max contextN/A1M1M
Max outputN/AN/AN/A
Capabilities
VisionNoYesYes
Function CallingNoYesYes
JSON ModeNoYesYes
StreamingNoNoYes
Catalogue
ProviderMicrosoftOpenAIGoogle
Categoryvoicechatchat
Charge typePay As You GoPay As You GoPay As You Go
Released2026-09-03——
Description
SummaryMicrosoft MAI-Transcribe-2 speech-to-text. Billed per second of audio.GPT-6.1 Terra is OpenAI’s mid-tier model in the GPT-6.1 series, designed to balance capability, speed, and cost efficiency. Positioned below GPT-6.1 Sol, it delivers strong performance across everyday agentic workflows, coding, and professional writing, with particular strengths in screenshot understanding and on-screen content analysis. Optimized for high-volume production workloads, GPT-6.1 Terra is well suited for coding assistants, visual interface analysis, workflow automation, and scalable agentic applications that require reliable performance at an accessible cost.Gemini 4 Argon is Google's flagship reasoning model, built for complex software engineering, deep mathematical and scientific problem solving, and autonomous multi-step agents. Google trained it to keep context across long, multi-step tasks and to turn long documents mixing text, tables, charts and images into a single grounded output. It leads the Vals Index across finance, legal, coding and tax work, and supports long decode of up to 1M output tokens in a single response.