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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. DeepSeek R1T ChimeraTNG TechRemove
  2. Kimi K3.1 ProMoonShot AIRemove
  3. Gemini 4 ArgonGoogleRemove
deepseek-r1t-chimera vs kimi-k3.1-pro vs gemini-4-argon
AttributeDeepSeek R1T Chimeradeepseek-r1t-chimeraKimi K3.1 Prokimi-k3.1-proGemini 4 Argongemini-4-argon
Pricing
Input$0.30 / 1M$2.00 / 1M$2.00 / 1M
Output$1.20 / 1M$10.00 / 1M$10.00 / 1M
Cache Write (5m)$0.30 / 1M$2.00 / 1M$2.00 / 1M
Cache Write (1h)$0.30 / 1M$2.00 / 1M$2.00 / 1M
Cache Read$0.30 / 1M$2.00 / 1M$2.00 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context163.8K1M1M
Max outputN/AN/AN/A
Capabilities
VisionNoYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderTNG TechMoonShot AIGoogle
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released———
Description
SummaryDeepSeek-R1T-Chimera combines DeepSeek-R1 with V3-0324 using a MoE architecture, blending R1’s reasoning strengths with V3's token efficiency. Optimized for general text generation and instruction following, it balances reasoning, efficiency, and usability. The model is MIT-licensed and available for both research and commercial use.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.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.