Kimi K3 is the best model ever made (sometimes)
Summary
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The video reviews Kimi K3, an open-weight AI model, highlighting its performance in coding and reasoning tasks, its advanced architecture, and its competitive pricing. It also discusses limitations and practical considerations for users.
The presenter reviews Kimi K3, an open-weight AI model, highlighting its impressive performance on coding and reasoning benchmarks, positioning it as a strong competitor in the AI landscape. The video delves into Kimi K3's architecture, mentioning its use of Delta Attention (KDA) and Attention Residuals (AttnRes) with a Stable Latent Mixture of Experts (MoE) framework, which allows for efficient scaling and reduced computation. Benchmark results are presented across various tasks, including DeepSWE, FrontierSWE, and Kimi Code Bench 2.0, showing Kimi K3's competitive performance, particularly in areas like coding and its cost-effectiveness compared to other leading models. The presenter also discusses the model's limitations, such as its potential for excessive proactiveness and a noticeable gap in user experience compared to closed-weight models like Claude Fable 5 and GPT-5.6 Sol. The video also touches upon Kimi K3's use in creative applications like game development and chip design, showcasing its multimodal capabilities and the potential for future developments. Finally, it briefly covers pricing options for Kimi K3 through subscription and API access, noting its competitive edge in cost-efficiency for certain use cases. The presenter concludes by expressing enthusiasm for the model's capabilities despite its current limitations.
Verdict
Kimi K3 is a highly competitive open-weight AI model, excelling in coding and reasoning tasks, offering a good balance of performance and cost, and demonstrating strong multimodal capabilities, though it has some limitations in user experience and requires significant hardware for optimal performance.
Pros
- Strong performance in coding and reasoning tasks.
- Efficient architecture with Mixture of Experts (MoE) leading to significant scaling and performance improvements.
- Cost-effective compared to many leading models, especially for certain use cases.
- Native multimodal capabilities (image and text) and flexible deployment options.
Cons
- Can exhibit excessive proactiveness and make unexpected decisions.
- Noticeable gap in user experience compared to closed-weight models like Claude Fable 5 and GPT 5.6 Sol.
- Pricing is higher than some open-weight peers, despite comparable or better performance.
- Requires significant hardware (64+ accelerators) for optimal performance.
Specs
| Parameter Count | 2.8T | |
| Context Window | 1M | |
| Release Date | July 27, 2026 | |
| Architecture | Kimi Delta Attention (KDA) and Attention Residuals (AttnRes) with Stable LatentMoE | |
| Model Type | Open Weights |
Compared to
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Claude Fable 5
Kimi K3 is cheaper and performs better on some metrics, but Fable 5 offers a better user experience.
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GPT-5.6 Sol
Kimi K3 is cheaper and performs better on some metrics, but GPT-5.6 Sol excels in reasoning.
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Opus 4.8
Kimi K3 is significantly cheaper than Opus 4.8 on a cost-per-task basis, though more expensive than other open-weight peers.
Best for
Not for
Claims & arguments
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Kimi K3 Intelligence Score
Kimi K3 scores 57 on the Artificial Analysis Intelligence Index, placing it behind Fable 5 and GPT-5.6 Sol but ahead of Opus 4.8 and GPT-5.5.
- Kimi K3 scores 57 on the Artificial Analysis Intelligence Index.
- Intelligence is comparable to Opus 4.8 and GPT-5.5 but remains behind Fable 5 and GPT-5.6 Sol.
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Kimi K3 Parameter Count
Moonshot AI has plans to release the 2.8T parameter model's weights, which would make it the leading open weights model.
- Moonshot AI has expressed plans to release the 2.8T parameter model's weights, which would make it the leading open weights model.
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Kimi K3 Performance vs. Open Weights Peers
Kimi K3 significantly outperforms other open weights models in the Intelligence Index, despite being more expensive.
- Kimi K3 would clearly lead other open weights models including GLM-5.2 (51) and DeepSeek V4 Pro (44), however, at 2.8T parameters, it is significantly larger than its open weights peers (eg. GLM-5.2 at 753B params and DeepSeek V4 Pro at 1.6T), as well as the Kimi K2 to K.6 models (1T params).
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Kimi K3 Cost vs. Performance
Kimi K3 offers a competitive cost per task, similar to GPT-5.6 Sol and cheaper than Opus 4.8, positioning it favorably against open weights peers.
- Cost per task ($0.94) is similar to GPT-5.6 Sol ($1.04), ~1/2 the price of Opus 4.8 ($1.80) and higher than open weights peers.
- This positions the model as cheaper on a cost per task basis than Opus 4.8, similar to GPT-5.6 Sol ($1.04) and more expensive than open weights peers, GLM-5.2 ($0.32) and DeepSeek V4 Pro ($0.04).
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Kimi K3 Multimodality
Kimi K3 possesses native multimodal capabilities, allowing it to process image and text input, and is positioned as a leading open weights model with these capabilities.
- Native multimodal capabilities: Kimi K3, like K2.6, is released with native image and text multimodal input.
- If weights are released, this will position Kimi K3 as one of the leading open weights models with multimodal input capabilities.
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Kimi K3 Deployment
Kimi K3 can be deployed through subscription (kimi.com) or API access (platform.kimiai).
- Kimi K3 is available today on kimi.com, Kimi Work, Kimi Code, and the Kimi API.
- At launch, Kimi K3 will use max thinking effort by default, with low- and high-effort modes to be introduced in subsequent updates.
Key Points
- 0:59 Introduction of Kimi K3 as a powerful open-weight AI model, highlighting its strengths in coding and reasoning tasks.
- 25:08 Explanation of Kimi K3's architecture: Delta Attention (KDA) and Attention Residuals (AttnRes) with a Mixture of Experts (MoE) framework for efficient scaling.
- Presentation of benchmark results from various sources (DeepSWE, FrontierSWE, Kimi Code Bench 2.0, etc.) showing Kimi K3's competitive performance, especially in coding.
- Comparison of Kimi K3's performance and cost-effectiveness against leading open and closed-weight models.
- Discussion of Kimi K3's multimodal capabilities, including native image and text input, and its potential for future developments.
- Overview of Kimi K3's pricing models: subscription via kimi.com and API access via platform.kimiai.
- Showcase of Kimi K3's application in game development and digital creation, demonstrating its 3D reasoning, coding, and vision capabilities.
- Overview of the chip design K3 built for nano model, highlighting its performance and efficiency.
- Discussion of K3's coding performance, including its ability to handle long-horizon tasks and its use of screenshots and visuals.
- Analysis of K3's performance on benchmarks like the AA-Omniscience Index, showing strong results in intelligence and speed but trailing in cost per task compared to some peers.
- Mention of K3's limitations, including excessive proactiveness and a gap in user experience compared to closed models.
- Concluding remarks on Kimi K3's strengths and potential, highlighting its strong performance and competitive pricing.
Worth watching if: Anyone interested in the latest developments in open-weight AI models, particularly for coding, reasoning, and creative tasks. It's especially relevant for developers and researchers evaluating AI models for performance, cost, and potential applications.
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