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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 4 ArgonGoogleRemove
  2. Kimi K3.1 ProMoonShot AIRemove
  3. Mistral Embed 2312Mistral AIRemove
gemini-4-argon vs kimi-k3.1-pro vs mistral-embed-2312
AttributeGemini 4 Argongemini-4-argonKimi K3.1 Prokimi-k3.1-proMistral Embed 2312mistral-embed-2312
Pricing
Input$2.00 / 1M$2.00 / 1M$0.125 / 1M
Output$10.00 / 1M$10.00 / 1M— Not priced per output token
Cache Write (5m)$2.00 / 1M$2.00 / 1M$0.125 / 1M
Cache Write (1h)$2.00 / 1M$2.00 / 1M$0.125 / 1M
Cache Read$2.00 / 1M$2.00 / 1M$0.125 / 1M
Web Search$0 / 1M$0 / 1M—
Context
Max context1M1M8.2K
Max outputN/AN/AN/A
Capabilities
VisionYesYesNo
Function CallingYesYesNo
JSON ModeYesYesNo
StreamingYesYesNo
Catalogue
ProviderGoogleMoonShot AIMistral AI
Categorychatchatembedding
Charge typePay As You GoPay As You GoPay As You Go
Released———
Description
SummaryGemini 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.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.Mistral Embed is Mistral AI's text embedding model, built for semantic search and RAG workflows. It generates 1024-dimensional vectors that capture meaningful relationships between pieces of text.