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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. Kimi K3.1 ProMoonShot AIRemove
  2. Gemini 3.5 TranscribeGoogleRemove
  3. Qwen3.8 27BAlibabaRemove
kimi-k3.1-pro vs gemini-3.5-transcribe vs qwen3.8-27b
AttributeKimi K3.1 Prokimi-k3.1-proGemini 3.5 Transcribegemini-3.5-transcribeQwen3.8 27Bqwen3.8-27b
Pricing
Input$2.00 / 1M— Not priced per input token$0.45 / 1M
Output$10.00 / 1M— Not priced per output token$3.20 / 1M
Cache Write (5m)$2.00 / 1MNot applicable$0.45 / 1M
Cache Write (1h)$2.00 / 1MNot applicable$0.45 / 1M
Cache Read$2.00 / 1MNot applicable$0.45 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M98.3K262K
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesNoYes
StreamingYesNoYes
Catalogue
ProviderMoonShot AIGoogleAlibaba
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.Qwen3.8 27B is an open-weight dense vision-language model from Qwen, designed for coding, professional knowledge work, research, and multimodal interaction. It combines strong text and visual understanding with capabilities optimized for sustained, real-world agentic tasks. The model supports flexible thinking modes that can be enabled for deeper reasoning or disabled for faster execution, making it well suited for long-running agents, multimodal workflows, coding assistants, and cost-conscious self-hosted deployments.