Grok 4.6 vs Kimi K3

Grok 4.6 vs Kimi K3: Quick Verdict

Grok 4.6 is the better default for most hosted coding, agent and professional workflows. Kimi K3 is the better choice for ultra-long coding tasks, 1M-token workloads, video understanding and open-weight deployment.

Artificial Analysis currently scores Grok 4.6 at 61 and Kimi K3 at 60 on its Intelligence Index, making the overall capability gap extremely small. Grok has a much larger advantage in generation speed, producing about 65.8 tokens per second compared with 39.8 for Kimi. Kimi, however, starts responding much sooner and provides more than twice Grok's context capacity.

Pricing is also more complicated than it first appears. For prompts below 200K tokens, Grok costs $2 per million input tokens and $6 per million output tokens, versus Kimi's $3 input and $15 output. But Grok's prices double once a prompt reaches 200K tokens, while Kimi uses flat pricing throughout its 1M context window.

The simplest recommendation is:

Grok 4.6 vs Kimi K3 at a Glance

Grok's official documentation lists a 500K context window, text and image input, configurable reasoning and built-in web search, X search and code execution. Kimi K3 has a roughly 1.05M-token window, native visual understanding, video input and a 2.8-trillion-parameter open-weight architecture with 104 billion parameters activated during inference.

Benchmark Comparison

There is no benchmark showing that either model simply wins everything.

Artificial Analysis currently gives:

Artificial Analysis' Intelligence Index combines evaluations covering agentic work, tool use, coding, scientific reasoning, knowledge and long-context reasoning. A one-point difference therefore suggests that the models are broadly competitive overall, not that one is categorically smarter.

The individual benchmarks are considerably more interesting.

In the Grok 4.6 model card, Grok leads Kimi on GDPval-AA v2 and AA-Briefcase, while Kimi leads Grok on DeepSWE v1.1 and by a large margin on SWE-Marathon v1.1. These results use benchmark or peer-reported figures, so they should be read individually rather than mashed into one imaginary universal score.

Benchmark verdict

Coding: Kimi Has an Unexpected Advantage

It would be easy to look at Grok's 61 Intelligence Index score and conclude that it is automatically the better coding model.

The actual coding results are messier.

On DeepSWE v1.1, which evaluates end-to-end repository issue resolution, the Grok 4.6 model card reports:

Kimi leads by 3.1 percentage points.

The difference becomes far larger on SWE-Marathon v1.1, a benchmark designed for engineering tasks that can involve multi-hour trajectories and millions of tokens:

Kimi's 48.1% result is more than 16 percentage points higher.

That matters because many comparisons use the phrase “coding model” as if writing a React component and autonomously modifying a huge repository over several hours were the same job.

They are not.

Moonshot specifically designed Kimi K3 for long-horizon coding. Its documentation says the model can sustain long-running engineering tasks, work with large codebases and coordinate terminal tools with limited supervision.

Where Grok still makes sense for coding

Grok remains highly attractive for:

Its faster output generation and lower short-context output price can make repeated coding loops considerably more responsive and economical.

Where Kimi is more compelling

Kimi has the stronger case for:

Coding winner

For normal agentic coding: close, with Grok often the more practical hosted choice.

For ultra-long autonomous software engineering: Kimi K3.

AI Agents and Knowledge Work

The comparison reverses when we move from pure software engineering into professional knowledge work.

GDPval-AA v2

GDPval-AA evaluates economically valuable professional deliverables such as documents, analyses and other work products.

That gives Grok a meaningful lead.

AA-Briefcase

AA-Briefcase evaluates long-horizon professional projects involving deliverables such as spreadsheets, presentations, memos, financial models and PDFs.

Again, Grok leads.

The Grok model card also reports 57.5% for Grok versus 55.4% for Kimi on APEX-Agents, another evaluation of long-horizon professional agent tasks. Kimi, however, leads Grok on the Vals Index, 74.7% to 71.1%, showing why single-benchmark declarations should be treated with suspicion rather than engraved onto stone tablets.

What this means in practice

Grok looks stronger for workflows such as:

Kimi becomes more attractive when those workflows require huge amounts of context or visual information.

Agent and knowledge-work winner: Grok 4.6

The margin is not enormous, but Grok currently has the more consistent evidence across major professional-work benchmarks.

Context Window: 500K vs 1M

This is one of Kimi K3's clearest advantages.

Kimi can therefore hold more than twice as much context in one request.

That can matter for:

Kimi also uses flat token pricing throughout its 1M context window. Moonshot explicitly says there is no pricing tier based on context length.

Grok behaves differently. Once a Grok 4.6 prompt reaches 200K tokens, its pricing moves from $2 input / $0.50 cached / $6 output to $4 input / $1 cached / $12 output per million tokens.

That pricing change is extremely important for anyone comparing these models specifically for long-context applications.

Context winner: Kimi K3

Not merely because the window is larger. Its pricing model is also friendlier to extremely long prompts.

Pricing and Real Cost per Task

At first glance, Grok is much cheaper.

Grok 4.6 below 200K prompt tokens

Kimi K3

For a shorter-context application processing:

the basic calculation is:

Grok 4.6

10 × $2 + 2 × $6 = $32

Kimi K3

10 × $3 + 2 × $15 = $60

For that workload, Grok costs about 47% less.

But now make the prompts longer than 200K.

Grok's rates become:

Using the same 10M input + 2M output workload:

Long-context Grok

10 × $4 + 2 × $12 = $64

Kimi

10 × $3 + 2 × $15 = $60

Suddenly Kimi is slightly cheaper.

That distinction is far more useful than simply putting $2 vs $3 into a table and declaring that research has occurred.

Cost per completed benchmark task

There is another surprise.

Artificial Analysis currently measures both Grok 4.6 and Kimi K3 at roughly $0.84 per Intelligence Index task, despite Kimi's higher headline token rates. Grok generated about 72M output tokens across the Intelligence Index evaluation, while Kimi generated roughly 130M, but differences in token mix, caching and task behavior result in essentially identical measured cost per weighted benchmark task.

Pricing verdict

Speed: Latency vs Output Throughput

Asking which model is “faster” produces two different answers.

Artificial Analysis currently measures:

Once Grok starts generating, it outputs tokens roughly 65% faster.

But Kimi begins responding almost nine times sooner in the measured first-token test.

That creates a strange but useful distinction:

Kimi feels faster at the beginning

For interactive applications where users are staring at the screen waiting for something to happen, Kimi's much lower first-token latency can improve perceived responsiveness.

Grok generates long answers faster

Once generation begins, Grok's higher throughput can finish long outputs more quickly.

This matters for:

Speed winner

First response: Kimi K3.

Generation throughput: Grok 4.6.

There is no intellectually respectable reason to compress those two different measurements into one word.

Multimodal and Video Capabilities

Both models can understand images.

But Kimi goes further.

Grok 4.6 officially supports:

Kimi K3 supports visual inputs and its API documentation includes direct support for video files as well as images.

That opens Kimi to workflows such as:

Moonshot specifically highlights software engineering combined with visual reasoning, including frontend, gaming and CAD scenarios.

Grok remains multimodal, but its current Grok 4.6 API documentation does not list video as an input modality.

Multimodal winner: Kimi K3

Especially if video is part of the workflow.

Open Weights vs Closed Model

This may be the biggest structural difference between the two models.

Kimi K3 is a 2.8-trillion-parameter Mixture-of-Experts model with 104 billion activated parameters. Its architecture uses 896 routed experts and activates 16 per token, and Moonshot released the full model weights.

Grok 4.6 is proprietary.

That means Kimi gives developers options that Grok does not:

This does not mean running Kimi K3 is easy. A 2.8T-parameter MoE model is not something most teams will casually load onto the machine under someone's desk because it has a reasonably enthusiastic graphics card.

But for organizations with serious infrastructure, open weights fundamentally change the deployment possibilities.

Open-model winner: Kimi K3

For teams that only want a hosted API, this may not matter.

For teams that require infrastructure control, it matters enormously.

Web Search, X Search and Tools

Grok 4.6 has a strong built-in tool stack.

Its official API supports:

X search gives Grok a distinctive advantage for workflows involving:

Kimi also has a substantial tool ecosystem.

Moonshot's Formula tools currently include:

There is, however, an important current caveat: Moonshot's K3 documentation says its web-search functionality is being updated and is not recommended for near-term production use.

Tooling winner: Grok 4.6

Particularly for applications that rely heavily on current web information or native X search.

Where Grok 4.6 Wins

Higher overall independent intelligence score

Artificial Analysis currently gives Grok 61 versus Kimi's 60. The difference is small, but Grok is ahead.

Professional knowledge work

Grok leads Kimi on GDPval-AA v2 and AA-Briefcase, two benchmarks aimed much closer to real professional deliverables than isolated trivia questions.

Output throughput

Grok generates around 65.8 tokens per second versus Kimi's 39.8 in current Artificial Analysis measurements.

Short-context pricing

For prompts below 200K tokens, Grok's $2 input / $6 output pricing is substantially below Kimi's $3 / $15 pricing.

Real-time tools

Native web search, X search and code execution make Grok attractive for research and autonomous agents that need current information.

Best Grok 4.6 use cases

Grok is particularly compelling for:

Where Kimi K3 Wins

Ultra-long coding

Kimi beats Grok on both DeepSWE v1.1 and SWE-Marathon v1.1 in the peer results reported in Grok's own model card, with the SWE-Marathon advantage being especially large.

1M context

Kimi provides more than twice Grok's context capacity.

Long-context pricing

Kimi keeps the same rates across its context window, while Grok doubles its rates at 200K prompt tokens.

First-token latency

Kimi starts responding much sooner in current Artificial Analysis measurements.

Video understanding

Kimi accepts video input directly.

Open weights

Kimi's full model weights are available, creating self-hosting and private-deployment possibilities unavailable with Grok 4.6.

Best Kimi K3 use cases

Kimi is especially compelling for:

Which Should You Choose?

There is no need to turn AI models into football clubs. Choose according to workload.

Choose Grok 4.6 if:

You mainly run coding, research or knowledge-work agents through a hosted API and most prompts stay below 200K tokens.

Grok gives you stronger professional benchmark results, faster output, cheaper short-context inference and a mature set of web, X and code-execution tools.

Choose Kimi K3 if:

Your workload regularly pushes beyond 200K tokens, involves huge repositories or requires hours-long autonomous coding.

Its 1M context, flat pricing, open weights and video understanding become much more important in those scenarios.

Final Verdict

Grok 4.6 wins the overall comparison for most hosted AI workloads, but Kimi K3 wins several categories that matter enormously to advanced developers.

On overall independent intelligence, the models are almost tied: 61 for Grok versus 60 for Kimi. Grok's real advantages are elsewhere. It generates output substantially faster, performs better on major professional knowledge-work evaluations and costs much less when prompts remain below 200K tokens.

That makes Grok 4.6 the stronger default for coding assistants, research agents, professional automation and high-volume hosted applications.

But Kimi K3 should not be treated as merely the open-weight runner-up.

Its 1M context window is more than twice Grok's, it supports video input, its full 2.8T-parameter model weights are available, and it produces significantly stronger results on some long-horizon coding evaluations. On SWE-Marathon v1.1, Kimi reaches 48.1% compared with Grok's 31.9% in the figures reported by the Grok model card.

There is also an important pricing twist. Grok is substantially cheaper for prompts under 200K tokens, but once Grok crosses that threshold its input and output rates double. Kimi keeps flat pricing across the full 1M context window, meaning Kimi can become more economical for extremely long, input-heavy workloads.

So the useful verdict is:

Neither model wins everywhere. That's less dramatic than declaring one model dead, but considerably more useful to anyone who actually has to pay the API bill.