The Race for Open-Weight MoE Supremacy
The central development is this: The landscape of large language models (LLMs) is rapidly evolving, with a new generation of open-weight Mixture-of-Experts (MoE) models pushing the boundaries of capability and accessibility. Three prominent Chinese research labs have recently unveiled their contenders, all featuring million-token context windows and targeting demanding workloads like long-horizon coding and sophisticated agent tasks.
Table of Contents
- The Race for Open-Weight MoE Supremacy
- Meet the Contenders: A Closer Look at Each Model
- Performance Benchmarks: Who Leads the Pack?
- Licensing and Accessibility: Open-Weight, But With Caveats
- Serving Costs and Performance: API vs. Self-Hosting
- Which Model for Your AI Project?
- Expert Perspective
- Frequently Asked Questions
- Key Takeaways
- Kimi K3: The New Frontrunner
- DeepSeek V4 Pro: The Balanced Performer
- GLM-5.2: Compact Yet Potent
- Artificial Analysis Intelligence Index
- Coding Benchmarks: A Specialized View
- API Pricing Comparison (per Million Tokens)
- Blended Cost and Speed
- Self-Hosting Feasibility
- Why is MoE LLM comparison important?
- What impact could MoE LLM comparison have?
- What should readers watch next with MoE LLM comparison?
- How does this relate to kimi?
Meanwhile, This article provides an in-depth comparison of Moonshot AI‘s Kimi K3, DeepSeek V4 Pro, and Zhipu AI’s GLM-5.2. We’ll explore their measured capabilities, crucial licensing terms, and the practical serving costs that AI teams must consider when integrating these powerful models.
Meet the Contenders: A Closer Look at Each Model
While all three are sparse MoE models, they differ in scale and specific features.
Kimi K3: The New Frontrunner
- Total Parameters: A staggering 2.8 trillion, making it the largest by this metric.
- Architecture: Stable LatentMoE, activating 16 out of 896 experts per token.
- Key Features: Native vision capabilities, a 1-million-token context window, and always-on reasoning. Moonshot AI positions it as the first open 3-trillion-class model.
- Release: July 16, 2026.
DeepSeek V4 Pro: The Balanced Performer
- Total Parameters: 1.6 trillion.
- Active Parameters: Approximately 49 billion, utilizing 384 routed experts plus one shared expert.
- Context Window: 1 million tokens, with a maximum output of 384K tokens.
- Variants: A smaller DeepSeek V4 Flash variant (284B total, 13B active) is available for less resource-intensive tasks.
- Release: April 24, 2026.
GLM-5.2: Compact Yet Potent
- Total Parameters: 744 billion (or 753B according to Artificial Analysis), the smallest of the three but still substantial.
- Active Parameters: Roughly 40 billion.
- Context Window: 1 million tokens, with a maximum output of 131K tokens.
- Reasoning Modes: Ships with High and Max reasoning modes.
- Release: June 13, 2026.
Performance Benchmarks: Who Leads the Pack?
In practical terms, Comparing models across different labs can be tricky due to varying test harnesses. For a neutral assessment, we turn to the Artificial Analysis Intelligence Index, which applies a consistent suite of tests.
Artificial Analysis Intelligence Index
- Kimi K3: Scores approximately 57, placing it at #3 overall, comparable to top proprietary models like Opus 4.8 and GPT-5.5, and behind only Claude Fable 5 and GPT-5.6 Sol.
- GLM-5.2: Achieves a score of 51, having held the top open-weight spot before Kimi K3’s release.
- DeepSeek V4 Pro (Max reasoning): Scores 44.
On this comprehensive index, Kimi K3 demonstrates superior overall capability.
Coding Benchmarks: A Specialized View
For example, Moonshot’s internal testing, using matched harnesses for Kimi K3 and GLM-5.2, shows K3 significantly outperforming GLM-5.2 across all shared coding benchmarks:
- DeepSWE: Kimi K3 (67.5) vs. GLM-5.2 (46.2)
- Program Bench: Kimi K3 (77.8) vs. GLM-5.2 (63.7)
- Terminal Bench 2.1: Kimi K3 (88.3) vs. GLM-5.2 (82.7)
- FrontierSWE: Kimi K3 (81.2) vs. GLM-5.2 (67.3)
DeepSeek V4 Pro, tested separately, shines on its own: it scored 80.6% on SWE-bench Verified (tied with Gemini 3.1 Pro at its release) and an impressive 83.5 on MRCR 1M, confirming its strong long-context coding abilities. GLM-5.2 also performed well on SWE-bench Pro at 62.1, surpassing GPT-5.5.
That said, In summary, Kimi K3 is the strongest overall, while DeepSeek V4 Pro is highly competitive for specific coding tasks. GLM-5.2 remains a very capable open-weight option, especially considering its smaller size.
Licensing and Accessibility: Open-Weight, But With Caveats
While all three are termed ‘open-weight,’ their practical accessibility varies significantly.
- DeepSeek V4 Pro: Fully MIT-licensed, with weights available on Hugging Face from day one. This allows for unrestricted commercial use, fine-tuning, and self-hosting immediately.
- GLM-5.2: Also MIT-licensed, with full weights accessible on Hugging Face under the zai-org organization, offering the same freedoms as DeepSeek V4 Pro.
- Kimi K3: The exception. Moonshot AI has committed to publishing weights by July 27, 2026, under a Modified MIT license. Until then, K3 is exclusively available via its API and Kimi applications. The Modified MIT license includes an attribution clause that only triggers if usage exceeds 100 million monthly active users.
Interestingly, For immediate, unrestricted access to weights and self-hosting capabilities, DeepSeek V4 Pro and GLM-5.2 are the clear choices today.
Serving Costs and Performance: API vs. Self-Hosting
The cost of operating these models, particularly through APIs, shows considerable differences.
API Pricing Comparison (per Million Tokens)
- Kimi K3: Input $3.00, Output $15.00, Cached Input $0.30
- DeepSeek V4 Pro: Input $0.435, Output $0.87, Cached Input ~$0.0036
- GLM-5.2: Input $1.40, Output $4.40, Cached Input $0.26
However, DeepSeek V4 Pro is the undeniable cost leader. One dollar can yield approximately 1.15 million output tokens from V4 Pro, compared to about 227K from GLM-5.2, and a mere 67K from Kimi K3.
Blended Cost and Speed
Artificial Analysis provides a blended cost per 1 million tokens (7:2:1 cache/input/output basis):
- DeepSeek V4 Pro: $0.18
- GLM-5.2: $0.90
- Kimi K3: $2.31
On a cost-per-task basis, the differences are even more stark:
- DeepSeek V4 Pro: $0.04
- GLM-5.2: $0.32
- Kimi K3: $0.94
Regarding speed, GLM-5.2 stands out, measured at approximately 168 tokens/second. DeepSeek V4 Pro and Kimi K3 are significantly slower, both around 62 tokens/second. However, Moonshot reports over 90% cache hits for Kimi K3 in coding workloads, which drastically reduces its effective input cost.
Self-Hosting Feasibility
For teams considering self-hosting, the hardware requirements are immense:
- GLM-5.2 (744B): Requires over 1TB of VRAM in BF16, equating to roughly eight H200 GPUs at FP8.
- DeepSeek V4 Pro (1.6T): Demands even more VRAM.
- Kimi K3 (2.8T): Moonshot recommends 64 or more accelerators, making local serving practically out of reach for most teams. K3 does use MXFP4 weights with MXFP8 activations for broader hardware support, but the scale remains a barrier.
Which Model for Your AI Project?
Choosing the right MoE model depends heavily on your priorities:
- For the most cost-effective solution with strong coding capabilities: DeepSeek V4 Pro is the clear winner. Its low API cost and immediate, unrestricted MIT license make it highly attractive for budget-conscious projects requiring robust coding performance.
- For peak benchmark performance and cutting-edge capabilities: Kimi K3 leads the pack. However, teams must be prepared for higher API costs and the wait until July 27, 2026, for open weights.
- For a balanced approach with immediate self-hosting and good performance: GLM-5.2 offers a compelling middle ground. It’s faster than its rivals, more affordable than Kimi K3, and its open weights are available now for local deployment, making it more capable than its parameter count might suggest.
For example, If license clarity and immediate access to weights are paramount, DeepSeek V4 Pro and GLM-5.2 are your go-to options. For those chasing the absolute highest benchmark scores and willing to use an API or wait for future weight releases, Kimi K3 stands ready to deliver.
Expert Perspective
A practical read on MoE LLM comparison starts with kimi. That is where the earliest effects are likely to show up if this development keeps building.
What happens next will come down to adoption speed, policy response, and execution quality. That combination could make MoE LLM comparison a meaningful reference point across deepseek.
For decision-makers, the useful lens is not the headline alone but how open changes priorities once organizations have to respond.
Frequently Asked Questions
Why is MoE LLM comparison important?
The Race for Open-Weight MoE Supremacy The central development is this: The landscape of large language models (LLMs) is rapidly evolving, with a new generation of open-weight Mixture-of-Experts (MoE) models pushing the boundaries of capability and accessibility.
What impact could MoE LLM comparison have?
Three prominent Chinese research labs have recently unveiled their contenders, all featuring million-token context windows and targeting demanding workloads like long-horizon coding and sophisticated agent tasks.
What should readers watch next with MoE LLM comparison?
Meanwhile, This article provides an in-depth comparison of Moonshot AI’s Kimi K3, DeepSeek V4 Pro, and Zhipu AI’s GLM-5.2.
How does this relate to kimi?
It connects because the article frames kimi as one of the clearest areas where the topic may be felt in practice.
Key Takeaways
- Kimi K3 leads in overall capability on the Artificial Analysis Intelligence Index but is API-only until its open-weight release on July 27, 2026.
- DeepSeek V4 Pro is the most cost-efficient, offering the lowest API prices and immediate MIT-licensed weights.
- GLM-5.2 is the smallest but fastest of the three, offering immediate self-hosting under an MIT license.
- All three boast 1-million-token context windows, but only DeepSeek V4 Pro and GLM-5.2 currently have their open weights available.
