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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 TranscribeOpenAIRemove
  2. Kimi K3.1 ProMoonShot AIRemove
  3. Gemini 4 ArgonGoogleRemove
gpt-transcribe vs kimi-k3.1-pro vs gemini-4-argon
AttributeGPT Transcribegpt-transcribeKimi K3.1 Prokimi-k3.1-proGemini 4 Argongemini-4-argon
Pricing
Input— Not priced per input token$2.00 / 1M$2.00 / 1M
Output— Not priced per output token$10.00 / 1M$10.00 / 1M
Cache Write (5m)Not applicable$2.00 / 1M$2.00 / 1M
Cache Write (1h)Not applicable$2.00 / 1M$2.00 / 1M
Cache ReadNot applicable$2.00 / 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
StreamingNoYesYes
Catalogue
ProviderOpenAIMoonShot AIGoogle
Categoryvoicechatchat
Charge typePay As You GoPay As You GoPay As You Go
Released2026-08-05——
Description
SummaryOpenAI GPT Transcribe speech-to-text. Billed per second of audio.Kimi K3.1 Pro is Moonshot AI's flagship open-weight multimodal reasoning model, building on Kimi K3 with enhanced post-training for advanced reasoning and autonomous task execution. It features a 1M-token context window and native image and video understanding, enabling complex, long-context multimodal workflows. Optimized for long-horizon coding, 3D creation, financial research, and end-to-end agentic knowledge work, Kimi K3.1 Pro is well suited for autonomous software engineering, multimodal analysis, and complex professional workflows that require sustained reasoning and reliable execution.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.