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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. Llama 3.2 1b InstructMetaRemove
  3. Gemini 4 ArgonGoogleRemove
kimi-k3.1-pro vs llama-3.2-1b-instruct vs gemini-4-argon
AttributeKimi K3.1 Prokimi-k3.1-proLlama 3.2 1b Instructllama-3.2-1b-instructGemini 4 Argongemini-4-argon
Pricing
Input$2.00 / 1M$0.0025 / 1M$2.00 / 1M
Output$10.00 / 1M$0.005 / 1M$10.00 / 1M
Cache Write (5m)$2.00 / 1M$0.0025 / 1M$2.00 / 1M
Cache Write (1h)$2.00 / 1M$0.0025 / 1M$2.00 / 1M
Cache Read$2.00 / 1M$0.0025 / 1M$2.00 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1MN/A1M
Max outputN/AN/AN/A
Capabilities
VisionYesNoYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderMoonShot AIMetaGoogle
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released———
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.Llama 3.2 1B is a lightweight 1-billion-parameter model built for efficient NLP tasks like summarization, conversation, and multilingual analysis. It runs well in low-resource environments, supports eight core languages (and can be fine-tuned for more), making it a good fit for developers who need capable, multilingual AI without heavy compute costs.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.