Kimi K3 vs Kimi K2: Full Comparison (2026)

Kimi K3 isn’t an incremental update to Kimi K2 — Moonshot AI calls it a new generation, and the numbers back that up. Here’s the complete side-by-side comparison, plus honest guidance on when K2 might still make sense. New to K3? Start with the complete Kimi K3 guide.

Spec-by-spec comparison

Spec	Kimi K2	Kimi K3
Total parameters	1.0T	2.8T
Architecture	MLA MoE	KDA + Attention Residuals + Stable LatentMoE
Experts	384	896 (16 active / token)
Active parameters	32B	~104B
Context window	256K tokens	1,048,576 tokens (1M)
Vision / video input	—	Native
Long-context decode	baseline	Up to 6.3× faster
Scaling efficiency	baseline	~2.5× better
Frontend Code Arena	#18 (K2.6)	#1
Weights license	Modified MIT	Custom Kimi K3 License (released July 27, 2026)

The five differences that actually matter

1. Context window: 256K → 1M tokens

This is the headline upgrade. K2’s 256K window was already generous; K3’s 1,048,576 tokens means entire codebases, full-length books, or hours of transcripts in a single prompt — with no chunking or retrieval hacks.

2. Speed at long context: up to 6.3× faster

A big context window is useless if responses crawl. K2’s attention slowed sharply as prompts grew. K3’s Kimi Delta Attention (KDA) keeps decoding fast even at hundreds of thousands of tokens — up to 6.3× faster than K2 at long contexts. In practice: K3’s 1M window is one you’ll actually use.

3. Intelligence: 2.8T params with 896 experts

K3 nearly triples total parameters and more than doubles the expert count (384 → 896), while keeping just 16 routed experts active per token (~104B active parameters, versus K2’s 32B). Combined with ~2.5× better scaling efficiency from Attention Residuals, the capability jump is larger than the parameter jump suggests. On Artificial Analysis’s independent ranking, K3 placed #4 of 189 models at launch (previous index version); on the current Intelligence Index v4.3.2 it scores 44, #3 of 115 large open-weight models.

4. Native vision and video

K2 was text-only. K3 understands images and video natively, which powers its agentic wins — like the #1 BrowseComp score (91.2) — and enables use cases (UI analysis, video Q&A, visual debugging) that simply weren’t possible on K2.

5. Coding: #18 → #1

On the Frontend Code Arena, K2.6 ranked #18. K3 ranks #1. It’s also #1 on Program Bench (77.8) and SWE Marathon (42.0), beating GPT-5.6 Sol and Claude Opus 4.8 on the latter. If you write code with AI, this alone justifies the switch.

Benchmarks: K3 vs the frontier (context K2 never reached)

Benchmark	Kimi K3	Note
Program Bench	77.8 (#1)	Edges GPT-5.6 Sol (77.6)
SWE Marathon	42.0 (#1)	Beats Claude Opus 4.8 (40.0)
BrowseComp	91.2 (#1)	Beats GPT-5.6 Sol (90.4)
Terminal-Bench 2.1	88.3 (#2)	0.5 behind GPT-5.6 Sol
Artificial Analysis (v4.3.2)	44 (#3 open-weight)	At launch, on the older index: #4 / 189

Pricing

K3’s API pricing ($0.30 cache-hit / $3.00 cache-miss input, $15.00 output per 1M tokens) works out to $0.94 per average benchmark task — roughly half the per-task cost of Claude Opus 4.8 or GPT-5.5. Since launch Moonshot has added cache-write pricing ($3/M for the default 5-minute TTL, $6/M for 1-hour), and K3 is also on Amazon Bedrock at the same $3/$15 list (all providers). The Kimi app still has a free tier, but Moonshot lists K3 access only on paid plans (Plus and above since the September 18 relaunch; legacy Moderato and above).

Should you switch to K3?

Switch to K3 if:

• You work with long documents or large codebases (the 1M window + 6.3× speed is transformative)
• You code with AI — #1 Frontend Code Arena, #1 SWE Marathon
• You need image or video understanding
• You want a top-tier open-weight model (#3 among large open-weight models on Artificial Analysis’s current index)

K2 might still make sense if:

• You’re self-hosting on limited hardware — K2’s 1.0T weights are far easier to run than K3’s 2.8T
• You have fine-tunes or pipelines built on K2 and its 256K context covers your workload
• Your tasks are short-context and cost-sensitive at scale

FAQ

Is Kimi K3 better than Kimi K2? Yes, across the board — 2.8T vs 1.0T parameters, 1M vs 256K context, native vision, up to 6.3× faster long-context decoding, and #18 → #1 on the Frontend Code Arena.

Should I upgrade from Kimi K2 to K3? Almost certainly yes — K3 beats K2 on every metric. Note that confirmed K3 access in the Kimi app needs a paid plan (Plus or above, legacy Moderato or above), and the API needs a $1 top-up. The main reason to stay on K2 is self-hosting with limited hardware, since K3’s 2.8T weights need far more infrastructure.

Is Kimi K3 more expensive than K2? On the API, yes per token: K3 costs $0.30 cache-hit / $3.00 input / $15.00 output per 1M tokens, versus $0.95 input / $4 output for K2.7 Code. Per completed task, Moonshot’s launch data still puts K3 at roughly half the cost of comparable frontier models. In the Kimi app, the free tier lets you try Kimi, but confirmed K3 access requires a paid plan.

Does K3 support images and video like K2? K2 was text-only. K3 adds native image and video understanding for the first time.

The verdict

For almost everyone, Kimi K3 is simply the better model — bigger, faster where it counts, multimodal, and the first open-weight model to genuinely pressure the closed frontier. K2 remains relevant mainly for self-hosters constrained by hardware.

Ready to try it? Kimi’s free tier on kimi.com is enough for a first look, but confirmed K3 access needs a paid plan (tiers explained in our Kimi Code K3 setup guide), and the API unlocks after a $1 top-up. For setup options, see how to download Kimi K3 on PC or the full specifications breakdown.