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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 4 ArgonGoogleRemove
  3. Qwen3 VL 235B A22B InstructAlibabaRemove
kimi-k3.1-pro vs gemini-4-argon vs qwen3-vl-235b-a22b-instruct
AttributeKimi K3.1 Prokimi-k3.1-proGemini 4 Argongemini-4-argonQwen3 VL 235B A22B Instructqwen3-vl-235b-a22b-instruct
Pricing
Input$2.00 / 1M$2.00 / 1M$0.30 / 1M
Output$10.00 / 1M$10.00 / 1M$1.50 / 1M
Cache Write (5m)$2.00 / 1M$2.00 / 1M$0.30 / 1M
Cache Write (1h)$2.00 / 1M$2.00 / 1M$0.30 / 1M
Cache Read$2.00 / 1M$2.00 / 1M$0.30 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M131.1K
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderMoonShot AIGoogleAlibaba
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.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.Qwen3-VL-235B-A22B Instruct is an open-weight multimodal model that combines strong language generation with image and video understanding, aimed at general vision-language tasks like VQA, document parsing, chart/table extraction, and multilingual OCR. It features robust perception, spatial grounding, and long-context visual comprehension, and supports agent-style workflows such as multi-image dialogue, video timeline alignment, GUI control, and visual-to-code assistance. With competitive benchmark performance and strong text-only ability, it's well suited for production uses across document AI, OCR, UI assistance, spatial reasoning, and vision-language research.