Kimi K2 (by Moonshot AI) and Llama 4 (by Meta) are each state-of-the-art open massive language fashions (LLMs) based mostly on Combination-of-Specialists (MoE) structure. Every mannequin focuses on completely different areas and is geared toward superior use circumstances, with completely different strengths and philosophies. Until every week in the past, Llama 4 was the undisputed king of the open-source LLMs, however now lots of people are saying that Kimi’s newest mannequin is giving Meta’s greatest a run for its cash. On this weblog, we are going to check these two fashions for varied duties to seek out which of Kimi K2 vs Llama 4 is the very best open-source mannequin. Let the battle of the very best start!
Kimi K2 vs Llama 4: Mannequin Comparability
Kimi K2 by Moonshot AI is an open-source, combination of consultants (MoE) mannequin with 1 trillion complete parameters, with 32 B lively parameters. The mannequin comes with a 128K token context window. The mannequin is skilled with the Muon optimizer and excels at duties like coding, reasoning, and agentic duties like device integration and multi-step reasoning.
Llama 4 by Meta AI is a household of mixture-of-experts-based multimodal fashions that had been launched in three completely different variants: Scout, Maverick, and Behemoth. Scout comes with 17B lively parameters & 10 M token window; Maverick with 17 B lively parameters and 1 M token window, whereas Behemoth (nonetheless in coaching) is claimed to supply 288 B lively parameters with over 2 trillion tokens in complete! The fashions include sturdy context dealing with, improved administration of delicate content material, and decrease refusal charges
| Characteristic | Kimi K2 | Llama 4 Scout | Llama 4 Maverick |
|---|---|---|---|
| Mannequin kind | MoE massive LLM, open-weight | MoE multimodal, open-weight | MoE multimodal, open-weight |
| Lively params | 32 B | 17 B | 17 B |
| Complete params | 1 T | 109 B | 400 B |
| Context window | 128 Ok tokens | 10 million tokens | 1 million tokens |
| Key strengths | Coding, reasoning, agentic duties, open | Light-weight, lengthy context, environment friendly | Coding, reasoning, efficiency rivaling proprietary fashions |
| Accessibility | Obtain and use freely | Public with license constraints | Public with license constraints |
To know extra about these fashions, their benchmarks and efficiency, learn our earlier articles:
Kimi K2 vs Llama 4: Benchmark Comparability
Kimi K2 and Llama 4 each are desk toppers of their efficiency on varied benchmarks. Here’s a temporary breakdown of their efficiency:
| Benchmark | What does this imply? | Kimi K2 | Llama 4 Maverick |
|---|---|---|---|
| GPQA-Diamond | That is to check LLM reasoning in superior Physics | 75.1 % | 67.7 % |
| AIME | That is to check the LLM for mathematical reasoning | 49.5 % | 25.2 % |
| LiveCodeBench | This assessments a mannequin’s real-world coding talents. | 53.7 % | 47.3 % |
| SWE‑bench | This assessments a mannequin’s skill to jot down production-ready code | 65.8 % | 18.4 % |
| OJBench | It measures the mannequin’s problem-solving skill. | 27.1 % | — |
| MMLU‑Professional | An instructional benchmark that assessments normal data and comprehension | — | 79.4 % |
Kimi K2 and Llama 4: The best way to entry?
To check these fashions for various duties, we are going to use the chat interface.
Choose the mannequin from the mannequin drop down current the the highest left aspect of the display screen.
Kimi K2 vs Llama 4: Efficiency Comparability
Now that we’ve seen varied fashions and benchmark comparisons between Kimi K2 and Llama 4, we are going to now check them for varied options like:
- Multimodality
- Agentic Behaviour and Device Use
- Multilingual Capabilities
Process 1: Multimodality
- Llama 4: Natively multimodal (can collectively course of photos and textual content), therefore preferrred for doc evaluation, visible grounding, and data-rich situations.
- Kimi K2: Centered on superior reasoning, coding, and agentic device use, however has much less native multimodal help in comparison with Llama
Immediate: “Extract Contents from this picture”
Output:
Assessment:
The outputs generated by the 2 LLMs are starkly completely different. With Llama 4 it feels prefer it learn by all of the textual content of the picture like a professional. Nonetheless, Kimi K2 states that the handwriting is illegible and may’t be learn. However once you look carefully, the textual content offered by Llama will not be the identical because the textual content that was there within the picture! The mannequin made up textual content at a number of locations (instance – affected person title, even analysis), which is the height degree of LLM hallucination.
On the face it might really feel like we’re getting an in depth picture evaluation, however Llama 4’s output is sure to dupe you. Whereas Kimi K2 – proper from the get go – mentions that it may well’t perceive what’s written, this bitter fact is method higher than a ravishing lie.
Thus, in the case of picture evaluation, each Kimi K2 and Llama 4 nonetheless wrestle and are unable to learn advanced photos correctly.
Process 2: Agentic Habits and Device Use
- Kimi K2: Particularly post-trained for agentic workflows – can execute intentions, independently run shell instructions, construct apps/web sites, name APIs, automate information science, and conduct multi-step workflows out-of-the-box.
- Llama 4: Though good in logic, imaginative and prescient, and evaluation, its agentic conduct will not be as sturdy or as open (largely multimodal reasoning).
Immediate: “Discover the highest 5 shares on NSE at this time and inform me what their share value was on 12 January 2025?”
Output:
Assessment:
Llama 4 will not be up for this activity. It lacks agentic capabilities, and therefore, it may well’t entry the online search device to entry the insights wanted for the immediate. Now, coming to Kimi K2, on the primary look, it might seem that Kimi K2 has finished the job! However a better evaluate is required right here. It’s able to utilizing completely different instruments based mostly on the duty, however it didn’t perceive the duty accurately. It was anticipated to verify for the highest inventory performers for at this time, and provides their costs for 12 Jan 2025; as an alternative, it simply gave an inventory of high performers of 12 Jan 2025. Agentic – Sure! However Sensible – not a lot – Kimi K2 is simply okay.
Process 3: Multilingual Capabilities
- Llama 4: Skilled on information for 200 completely different languages, together with strong multi-lingual and cross-lingual abilities.
- Kimi K2: International help, however particularly sturdy in Chinese language and English (highest scores on Chinese language language benchmarks).
Immediate: “Translate the contents of the pdf to Hindi.PDF Hyperlink“
Notice: To check Llama 4 for this immediate, you may also take a picture of the PDF and share it as a lot of the free LLM suppliers don’t permit importing paperwork of their free plan.
Output:
Assessment:
At this activity, each fashions carried out equally nicely. Each Llama 4 and Kimi K2 effectively translate French into Hindi. Each the fashions recognised the supply of the poem, too. The response generated by each fashions was the identical and proper. Thus, in the case of multilingual help, Kimi K2 is nearly as good as Llama 4.
Open-source nature and value
Kimi K2: Absolutely open-source, may be deployed domestically, weights and API can be found to everybody, prices for inference and API are considerably decrease ($0.15- $0.60/1M enter tokens, $2.50/1M output tokens).
Llama 4: solely obtainable underneath a neighborhood license (restrictions could happen by area), barely larger infrastructure necessities because of context measurement, and is typically much less versatile for self-hosted, manufacturing use circumstances.
Ultimate Verdict:
| Process | Kimi K2 | Llama 4 |
|---|---|---|
| Multimodality | ✅ | ❌ |
| Agentic conduct & Device use | ✅ | ❌ |
| Multilingual Capabilities | ❌ | ✅ |
- Use Kimi K2: If you would like high-end coding, reasoning, and agentic automation, significantly when valuing full open-source availability, extraordinarily low value, and native deployment. Kimi K2 is at the moment forward on key measures if you’re a developer making high-end instruments, workflows, or utilizing LLMs on a finances.
- Use Llama 4: In the event you want extraordinarily massive context reminiscence, nice understanding of language, and open supply availability. It stands out in visible evaluation, doc processing, and cross-modal analysis/enterprise duties.
Conclusion
To say, Kimi K2 is best than Llama 4 would possibly simply be an overstatement. Each fashions have their professionals and cons. Llama 4 may be very fast, whereas Kimi K2 is kind of complete. Llama 4 is extra liable to make issues up, whereas Kimi K2 would possibly draw back from even making an attempt. Each are nice open-source fashions and provide customers a variety of options similar to these by closed-source fashions like GPT 4o, Gemini 2.0 Flash, and extra. To choose one out of the 2 is barely tough, however you may take the decision based mostly in your activity.
Or possibly attempt them each and see which one you want higher?
Knowledge Scientist | AWS Licensed Options Architect | AI & ML Innovator
As a Knowledge Scientist at Analytics Vidhya, I specialise in Machine Studying, Deep Studying, and AI-driven options, leveraging NLP, laptop imaginative and prescient, and cloud applied sciences to construct scalable functions.
With a B.Tech in Pc Science (Knowledge Science) from VIT and certifications like AWS Licensed Options Architect and TensorFlow, my work spans Generative AI, Anomaly Detection, Faux Information Detection, and Emotion Recognition. Keen about innovation, I attempt to develop clever techniques that form the way forward for AI.
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