Kimi K3 vs K2: What Changed, Should You Switch?

The moment Moonshot’s Kimi K3 landed, most teams already running K2 / K2.x had the same first reaction: is it worth switching? The answer isn’t “upgrade blindly” — it comes down to whether your workload actually taps K3’s new capabilities, and whether you can absorb its steeper output price. Below we lay both generations side by side, then give a verdict per scenario.

Both generations in one table

Dimension	Kimi K2 / K2.x	Kimi K3
Context window	256K tokens	1M (1,048,576) tokens
Vision	None / bolt-on	Native vision (image + video)
Reasoning	K2-thinking as a separate model	Always-on reasoning, controlled by reasoning_effort
Architecture	MoE, ~1T total params	MoE 2.8T, 896 experts, 16 activated
Attention	Standard MoE attention	KDA + Attention Residuals
Scaling efficiency	Baseline	~2.5× vs K2
Output price (official ref)	~¥8–16 / 1M	¥100 / 1M

Where K3 really wins

Context jumps from 256K to 1M. This is the most tangible upgrade. An entire mid-sized codebase, a full long-form contract, dozens of pages of PDF can now fit into a single-turn request — no more painstaking chunking and RAG. The difference is especially clear for long-horizon agents that run long tool-call chains and maintain deep histories.

Native vision becomes a first-class citizen. In the K2 era, seeing images basically meant bolt-on OCR or calling a separate vision model; K3 ingests images and video directly, so tasks like “build the frontend from this design mockup” or “edit code from a game / CAD screenshot” are natively supported. Note that vision input must use an object array via base64 or an ms:// file reference — public image URLs are not supported.

Reasoning is on by default. With K2, deep thinking meant switching to a dedicated model like kimi-k2-thinking; K3 builds reasoning into the main model and tunes the intensity with reasoning_effort (currently only max), so complex reasoning and multi-step planning work out of the box.

Efficiency from the new architecture. A 2.8T MoE paired with KDA (Kimi Delta Attention) + Attention Residuals delivers, per Moonshot, roughly 2.5× the overall scaling efficiency of K2 — in other words, the same compute budget trains / serves more economically, which is exactly what gives it the headroom to push context to 1M.

The cost: output price is noticeably higher

K3 is positioned as the flagship, and it’s priced like one. In the official reference pricing, output runs as high as ¥100 / 1M, with input at ¥20 for a cache miss and ¥2 for a cache hit. Against the few-yuan output price of the K2 series, the cost gap is large for pure high-volume workloads. The upside: K3 enables context caching by default, so long system prompts and fixed documents called repeatedly can compress input down to a tenth — meaning long-horizon agents actually benefit. Prices fluctuate constantly, so check the live rate before you order — the real-time unit price for the entire Kimi lineup on GetModel is on the pricing page.

Verdict by scenario: which one to use

Stay on K2 / K2.x if:

• Pure high-volume, high-concurrency general tasks (classification, extraction, rewriting, support) where you don’t need the absolute capability ceiling — K2’s unit price is far lower;
• Context rarely exceeds 256K and you don’t need vision;
• Cost is a hard constraint.

Upgrade to K3 if:

• You need to swallow an entire codebase / ultra-long document in one go, and 256K is already tight;
• You have vision-to-code / multimodal needs (frontend, games, CAD, design-mockup reproduction);
• You run long-horizon agents that need strong reasoning and stability across deep histories;
• Your prompts are highly repetitive, so caching discounts kick in and amortize the flagship price.

The pragmatic middle ground: run one aggregation gateway with both K2 and K3 attached — send cheap tasks to K2, hard tasks to K3, and switch in code by changing only the model field. With an aggregation interface like GetModel, a single key lets you schedule freely between the two generations, and even compare against GPT and Claude along the way.

Migration essentials (K2 → K3)

If you do upgrade, sidestep three pitfalls in code:

• Drop the thinking parameter — K3 uses reasoning_effort instead (currently only max);
• For multi-turn / tool calls, pass the full assistant message back verbatim — don’t keep only content;
• The context budget is 4× bigger, but don’t blindly fill it — longer means pricier and slower.

For the complete integration code, curl / Python examples, and error troubleshooting, see the Kimi K3 API integration tutorial. To use K2, K3, and other vendors’ models from one codebase, the easiest start is grabbing a key from the GetModel dashboard.