Kimi K3 vs Kimi K2: What Actually Changed

This is the upgrade most existing Kimi users are actually deciding about, and it is a bigger jump than a version number suggests. K3 nearly triples the parameter count, quadruples the context window, adds native vision and video, and switches the attention architecture. It also costs several times more per token — which is the part that decides it for high-volume workloads.

Kimi K3 vs Kimi K2 at a glance

	Kimi K3	Kimi K2
Total parameters	2.8 trillion	1 trillion
Active per token	104 billion	32 billion
Context window	1,048,576 tokens (1M)	262,144 (256K)
Max output	131,072 tokens (up to 1M)	100,352 tokens
Modality	Text, images, video	Text only
Attention	Kimi Delta Attention (KDA) with Gated MLA	Standard MoE attention
Precision	MXFP4 / MXFP8	INT4 native
Reasoning	Always on, three effort levels	Thinking variant available separately
Price (per 1M tokens)	$3.00 in / $15.00 out	Roughly $0.60 in / $2.50 out
Open weights	Yes — Kimi K3 License	Yes

The K2 generation spans several releases (K2, K2 Thinking, and the K2.x refreshes). The figures above are for Kimi K2 Thinking, the most directly comparable reasoning model in that line. K2 pricing varies by provider — the figures shown are representative rather than a single official rate. We deliberately do not compare published intelligence-index scores across the two generations: those indices are revised over time and are not version-comparable.

Choose Kimi K3 if

• You need vision. K2 is text-only, so any workload involving screenshots, scanned documents, charts, or video is not a K2 workload at all.
• Your context requirement sits between 256K and 1M tokens — the window is four times larger.
• The work is hard: multi-step agentic runs, deep research, difficult code. This is where the parameter jump and always-on reasoning show up.
• You want one model rather than choosing between a base and a thinking variant.

Choose Kimi K2 if

• High-volume, well-specified work. K2 is several times cheaper per token, and on routine tasks the gap in output quality is much smaller than the gap in price.
• You self-host. 1T parameters is dramatically more tractable than 2.8T — and it is the difference between a large single-node deployment and a multi-node one.
• Latency-sensitive paths where always-on reasoning is a cost rather than a benefit.
• Your pipeline is tuned and validated on K2 and nothing in your roadmap needs vision or a bigger window.

The verdict

Upgrade the hard end of your workload, keep the cheap end on K2. This is a routing decision, not a migration: K3 for the tasks where a wrong answer is expensive, K2 for volume. Teams that move everything to K3 at once usually discover that most of their traffic did not need it.

Whichever way you lean, run the decision on your own workload rather than a benchmark table. Twenty representative tasks from your real queue will tell you more than any published score — and because K3 is OpenAI- and Anthropic-compatible, setting up that comparison is a base URL change rather than a project.

More on Kimi K3

Start with the complete Kimi K3 guide for the overview, or go deeper:

• Kimi K3 Alternatives: 5 Models Worth Comparing
• Kimi K3 vs Claude Opus 5: Open Weights at 60% of the Price
• Kimi K3 Architecture: 2.8T Parameters, 104B Active