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.

Kimi K3.1 ProMoonShot AI Remove
Perceptron Mk1.5Perceptron Remove
Qwen3.8 2.4T A95BAlibaba Remove
Gemini 4 ArgonGoogle Remove

4 is the maximum. Remove one to add another.

kimi-k3.1-pro vs perceptron-mk1.5 vs qwen3.8-2.4t-a95b vs gemini-4-argon
Attribute	Kimi K3.1 Prokimi-k3.1-pro	Perceptron Mk1.5perceptron-mk1.5	Qwen3.8 2.4T A95Bqwen3.8-2.4t-a95b	Gemini 4 Argongemini-4-argon
Pricing
Input	$2.00 / 1M	$0.15 / 1M	$1.80 / 1M	$2.00 / 1M
Output	$10.00 / 1M	$1.50 / 1M	$5.40 / 1M	$10.00 / 1M
Cache Write (5m)	$2.00 / 1M	$0.15 / 1M	$1.80 / 1M	$2.00 / 1M
Cache Write (1h)	$2.00 / 1M	$0.15 / 1M	$1.80 / 1M	$2.00 / 1M
Cache Read	$2.00 / 1M	$0.15 / 1M	$1.80 / 1M	$2.00 / 1M
Web Search	$0 / 1M	$0 / 1M	$0 / 1M	$0 / 1M
Context
Max context	1M	36.9K	262K	1M
Max output	N/A	N/A	N/A	N/A
Capabilities
Vision	Yes	No	Yes	Yes
Function Calling	Yes	No	Yes	Yes
JSON Mode	Yes	No	Yes	Yes
Streaming	Yes	Yes	Yes	Yes
Catalogue
Provider	MoonShot AI	Perceptron	Alibaba	Google
Category	chat	chat	chat	chat
Charge type	Pay As You Go	Pay As You Go	Pay As You Go	Pay As You Go
Released	—	2026-09-25	—	—
Description
Summary	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.	Perceptron Mk1.5 chat model that accepts audio input. Audio and text input are billed per token.	Qwen3.8 2.4T A95B is Qwen's open-weight sparse Mixture-of-Experts (MoE) model and the open-weight counterpart to Qwen3.8 Max. It features 2.4T total parameters with 95B activated per token, combining frontier-scale capacity with efficient sparse inference. Designed for coding, research, complex reasoning, and agentic workflows, the model is well suited for demanding long-horizon tasks and advanced autonomous systems while providing the flexibility and customization benefits of open weights.	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.