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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. Gemini 3.5 TranscribeGoogleRemove
  2. Kimi K3.1 ProMoonShot AIRemove
  3. Qwen3.8 27BAlibabaRemove
gemini-3.5-transcribe vs kimi-k3.1-pro vs qwen3.8-27b
AttributeGemini 3.5 Transcribegemini-3.5-transcribeKimi K3.1 Prokimi-k3.1-proQwen3.8 27Bqwen3.8-27b
Pricing
Input— Not priced per input token$2.00 / 1M$0.45 / 1M
Output— Not priced per output token$10.00 / 1M$3.20 / 1M
Cache Write (5m)Not applicable$2.00 / 1M$0.45 / 1M
Cache Write (1h)Not applicable$2.00 / 1M$0.45 / 1M
Cache ReadNot applicable$2.00 / 1M$0.45 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context98.3K1M262K
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeNoYesYes
StreamingNoYesYes
Catalogue
ProviderGoogleMoonShot AIAlibaba
Categoryvoicechatchat
Charge typePay As You GoPay As You GoPay As You Go
Released2026-09-25——
Description
SummaryGoogle Gemini 3.5 Transcribe speech-to-text. Billed per input and output token.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.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.