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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. Kimi K3.1 ProMoonShot AIRemove
  2. Gemini 3.5 TranscribeGoogleRemove
  3. GLM 5.3Z.AIRemove
kimi-k3.1-pro vs gemini-3.5-transcribe vs glm-5.3
AttributeKimi K3.1 Prokimi-k3.1-proGemini 3.5 Transcribegemini-3.5-transcribeGLM 5.3glm-5.3
Pricing
Input$2.00 / 1M— Not priced per input token$1.40 / 1M
Output$10.00 / 1M— Not priced per output token$4.40 / 1M
Cache Write (5m)$2.00 / 1MNot applicable$1.40 / 1M
Cache Write (1h)$2.00 / 1MNot applicable$1.40 / 1M
Cache Read$2.00 / 1MNot applicable$1.40 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M98.3K1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesNo
Function CallingYesYesYes
JSON ModeYesNoYes
StreamingYesNoYes
Catalogue
ProviderMoonShot AIGoogleZ.AI
Categorychatvoicechat
Charge typePay As You GoPay As You GoPay As You Go
Released—2026-09-25—
Description
SummaryKimi 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.Google Gemini 3.5 Transcribe speech-to-text. Billed per input and output token.GLM-5.3 is Z.ai's large-scale reasoning model designed for complex software engineering and long-horizon agentic workflows. It supports text input and output with a 1M-token context window, enabling sustained reasoning across large codebases and extended multi-step tasks. Building on GLM-5.2, it delivers stronger coding performance while improving the balance between capability and token efficiency, making it well suited for autonomous coding agents, large-scale engineering workflows, and complex task execution.