Kimi K2 vs Qwen 3 vs GLM 4.5 – The Ultimate 2025 Model Comparison for AI Builders

Quick Summary: What separates Kimi K2, Qwen 3, and GLM 4.5 in 2025?

Answer: These three Chinese‑built large language models all leverage Mixture‑of‑Experts architectures, but they target different strengths. Kimi K2 focuses on coding excellence and agentic reasoning with a 1‑trillion parameter architecture (32 B active) and a 130 K token context window, offering 64–65 % scores on SWE‑bench while balancing cost. Qwen 3 Coder is the most polyglot; it scales to 480 B parameters (35 B active), uses dual thinking modes and extends its context window to 256 K–1 M tokens for repository‑scale tasks. GLM 4.5 prioritises tool‑calling and efficiency, achieving 90.6 % tool‑calling success with only 355 B parameters and requiring just eight H20 chips for self‑hosting. The models’ pricing differs: Kimi K2 charges about $0.15 per million input tokens, Qwen 3 about $0.35–0.60, and GLM 4.5 around $0.11. Choosing the right model depends on your workload: coding accuracy and agentic autonomy, extended context for refactoring, or tool integration and low hardware footprint.

Quick Digest – Key Specs & Use‑Case Summary

How to use this guide

This in‑depth comparison draws on independent research, academic papers, and industry analyses to give you an actionable perspective on these frontier models. Each section includes an Expert Insights bullet list featuring quotes and statistics from researchers and industry thought leaders, alongside our own commentary. Throughout the article, we also highlight how Clarifai’s platform can help deploy and fine‑tune these models for production use.

Why the Eastern AI Revolution matters for developers

Chinese AI companies are no longer chasing the West; they’re redefining the state of the art. In 2025, Chinese open‑source models such as Kimi K2, Qwen 3, and GLM 4.5 achieved SWE‑bench scores within a few points of the best Western models while costing 10–100× less. This disruptive price‑performance ratio is not a fluke – it’s rooted in strategic choices: optimized coding performance, agentic tool integration, and a focus on open licensing.

A new benchmark of excellence

The SWE‑bench benchmark, released by researchers at Princeton, tests whether language models can resolve real GitHub issues across multiple files. Early versions of GPT‑4 barely solved 2 % of tasks; yet by 2025 these Chinese models were solving 64–67 %. Importantly, their context windows and tool‑calling abilities enable them to handle entire codebases rather than toy problems.

Creative example: The 10x cost disruption

Imagine a startup building an AI coding assistant. It needs to process 1 B tokens per month. Using a Western model might cost $2,500–$15,000 monthly. By adopting GLM 4.5 or Kimi K2, the same workload could cost $110–$150, allowing the company to reinvest savings into product development and hardware. This economic leverage is why developers worldwide are paying attention.

Expert Insights

• Princeton researchers highlight that SWE‑bench tasks require models to understand multiple functions and files simultaneously, pushing them beyond simple code completions.
  
  
• Independent analyses show that Chinese models deliver 10–100× cost savings over Western alternatives while approaching parity on benchmarks.
  
  
• Industry commentators note that open licensing and local deployment options are driving rapid adoption.
  
  

Meet the models: Overview of Kimi K2, Qwen 3 Coder and GLM 4.5

Overview of Kimi K2

Kimi K2 is Moonshot AI’s flagship model. It employs a Mixture‑of‑Experts (MoE) architecture with 1 trillion total parameters, but only 32 B activate per token. This sparse design means you get the power of a huge model without massive compute requirements. The context window tops out at 130 K tokens, enabling it to ingest entire microservice codebases. SWE‑bench Verified scores place it at around 65 %, competitive with Western proprietary models. The model is priced at $0.15 per million input tokens and $2.50 per million output tokens, making it suitable for high‑volume deployments.

Kimi K2 shines in agentic coding. Its architecture supports multi‑step tool integration, so it can not only generate code but also execute functions, call APIs, and run tests autonomously. A mixture of eight active experts handle each token, allowing domain‑specific expertise to emerge. The modified MIT license permits commercial use with minor attribution requirements.

Creative example: You’re tasked with debugging a complex Python application. Kimi K2 can load the entire repository, identify the problematic functions, and write a fix that passes tests. It can even call an external linter via Clarifai’s tool orchestration, apply the recommended changes, and verify them – all within a single interaction.

Expert Insights

• Industry evaluators highlight that Kimi K2’s 32 B active parameters allow high accuracy with lower inference costs.
  
  
• The K2 Thinking variant extends context to 256 K tokens and exposes a reasoning_content field for transparency.
  
  
• Analysts note K2’s tool‑calling success in multi‑step tasks; it can orchestrate 200–300 sequential tool calls.
  
  

Overview of Qwen 3 Coder

Qwen 3 Coder—often referred to as Qwen 3.25—balances power and flexibility. With 480 B total parameters and 35 B active, it offers robust performance on coding benchmarks and reasoning tasks. Its hallmark is the 256 K token native context window, which can be expanded to 1 M tokens using context extension techniques. This makes Qwen particularly suited to repository‑scale refactoring and cross‑file understanding.

A unique feature is the dual thinking modes: Rapid mode for instantaneous completions and Deep thinking mode for complex reasoning. Dual modes let developers choose between speed and depth. Pricing varies by provider but tends to be in the $0.35–0.60 range per million input tokens, with output costs around $1.50–2.20. Qwen is released under Apache 2.0, allowing wide commercial use.

Creative example: An e‑commerce company needs to refactor a 200 k‑line JavaScript monolith to modern React. Qwen 3 Coder can load the entire repository thanks to its long context, refactor components across files, and maintain coherence. Its Rapid mode will quickly fix syntax errors, while Deep mode can redesign architecture.

Expert Insights

• Evaluators emphasise Qwen’s polyglot support of 358 programming languages and 119 human languages, making it the most versatile.
  
  
• The dual‑mode architecture helps balance latency and reasoning depth.
  
  
• Independent benchmarks show Qwen achieves 67 % on SWE‑bench Verified, edging out its peers.
  
  

Overview of GLM 4.5

GLM 4.5, created by Z.AI, emphasises efficiency and agentic performance. Its 355 B total parameters with 32 B active deliver performance comparable to larger models while requiring eight Nvidia H20 chips. A lighter Air variant uses 106 B total / 12 B active and runs on 32–64 GB VRAM, making self‑hosting more accessible. The context window sits at 128 K tokens, which covers 99 % of real use cases.

GLM 4.5’s standout feature is its agent‑native design: it incorporates planning and tool execution into its core. Evaluations show a 90.6 % tool‑calling success rate, the highest among open models. It supports a Thinking Mode and a Non‑Thinking Mode; developers can toggle deep reasoning on or off. The model is priced around $0.11 per million input tokens and $0.28 per million output tokens. Its MIT license allows commercial deployment without restrictions.

Creative example: A fintech startup uses GLM 4.5 to build an AI agent that automatically responds to customer tickets. The agent uses GLM’s tool calls to fetch account data, run fraud checks, and generate responses. Because GLM runs fast on modest hardware, the company deploys it on a local Clarifai runner, ensuring compliance with financial regulations.

Expert Insights

• GLM 4.5’s 90.6 % tool‑calling success surpasses other open models.
  
  
• Z.AI documentation emphasises its low cost and high speed with API costs as low as $0.2 per million tokens and generation speeds >100 tokens per second.
  
  
• Independent tests show GLM 4.5’s Air variant runs on consumer GPUs, making it appealing for on‑prem deployments.
  
  

How do these models differ in architecture and context windows?

Understanding Mixture‑of‑Experts and reasoning modes

All three models employ Mixture‑of‑Experts (MoE), where only a subset of experts activates per token. This design reduces computation while enabling specialised experts for tasks like syntax, semantics, or reasoning. Kimi K2 selects 8 of its 384 experts per token, while Qwen 3 uses 35 B active parameters for each inference. GLM 4.5 also uses 32 B active experts but builds agentic planning into the architecture.

Context windows: balancing memory and cost

• Kimi K2 & GLM 4.5: ~128–130 K tokens. Perfect for typical codebases or multi‑document tasks.
  
  
• Qwen 3 Coder: 256 K tokens native; extendable to 1 M tokens with context extrapolation. Ideal for large repositories or research where long contexts improve coherence.
  
  
• K2 Thinking: extends to 256 K tokens with transparent reasoning, exposing intermediate logic via the reasoning_content field.
  
  

Longer context windows also increase costs and latency. Feeding 1 M tokens into Qwen 3 could cost $1.20 just for input processing. For most applications, 128 K suffices.

Reasoning modes and heavy vs light modes

• Qwen 3 offers Rapid and Deep modes: choose speed for autocompletion or depth for architecture decisions.
  
  
• GLM 4.5 offers Thinking Mode for complex reasoning and Non‑Thinking Mode for fast responses.
  
  
• K2 Thinking includes a Heavy Mode, running eight reasoning trajectories in parallel to boost accuracy at the cost of compute.
  
  

Creative example

If you’re analysing a legal contract with 500 pages, Qwen 3’s 1 M token window can ingest the entire document and produce summaries without chunking. For everyday tasks like debugging or design, 128 K is sufficient, and using GLM 4.5 or Kimi K2 will reduce costs.

Expert Insights

• Z.AI documentation notes that GLM 4.5’s Thinking Mode and Non‑Thinking Mode can be toggled via the API, balancing speed and depth.
  
  
• DataCamp emphasises that K2 Thinking uses a reasoning_content field to reveal each step, enhancing transparency.
  
  
• Researchers caution that longer context windows drive up costs and may only be necessary for specialised tasks.
  
  

Benchmark & performance comparison

How do these models perform across benchmarks?

Benchmarks like SWE‑bench, LiveCodeBench, BrowseComp, and GPQA reveal differences in strength. Here’s a snapshot:

• SWE‑bench Verified (bug fixing): Qwen 3 scores 67 %, Kimi K2 ~65 %, GLM 4.5 ~64 %.
  
  
• LiveCodeBench (code generation): GLM 4.5 leads with 74 %, Kimi K2 around 83 %, Qwen around 59 %.
  
  
• BrowseComp (web tool use & reasoning): K2 Thinking scores 60.2, beating GPT‑5 and Claude Sonnet.
  
  
• GPQA (graduate physics): K2 Thinking scores ~84.5, close to GPT‑5’s 85.7.
  
  

Tool‑calling success: GLM 4.5 tops the charts with 90.6 %, while Qwen’s function calls remain strong; K2’s success is comparable but not publicly quantified.

Creative example: Benchmark in action

Picture a developer using each model to fix 15 real GitHub issues. According to an independent analysis, Kimi K2 completed 14/15 tasks successfully, while Qwen 3 managed 7/15. GLM wasn’t evaluated in that specific set, but separate tests show its tool‑calling excels at debugging.

Expert Insights

• Princeton researchers note that models must coordinate changes across files to succeed on SWE‑bench, pushing them toward multi‑agent reasoning.
  
  
• Industry analysts caution that benchmarks don’t capture real‑world variability; actual performance depends on domain and data.
  
  
• Independent tests highlight that Kimi K2’s real‑world success rate (93 %) surpasses its benchmark ranking.
  
  

Cost & pricing analysis: Which model gives the best value?

Token pricing comparison

• Kimi K2: $0.15 per 1 M input tokens and $2.50 per 1 M output tokens. For 100 M tokens per month, that’s about $150 input cost.
  
  
• Qwen 3 Coder: Pricing varies; independent evaluations list $0.35–0.60 input and $1.50–2.20 output. Some providers offer lower tiers at $0.25.
  
  
• GLM 4.5: $0.11 input / $0.28 output; some sources quote $0.2/$1.1 for high‑speed variant.
  
  

Hidden costs & hardware requirements

Deploying locally means VRAM and GPU requirements: Kimi K2 and Qwen 3 models need multiple high‑end GPUs (often 8× H100 NVL, ~1050 GB VRAM for Qwen, ~945 GB for GLM). GLM’s Air variant runs on 32–64 GB VRAM. Running in the cloud transfers costs to API usage and storage.

Licensing & compliance

• GLM 4.5: MIT license allows commercial use with no restrictions.
  
  
• Qwen 3 Coder: Apache 2.0 license, open for commercial use.
  
  
• Kimi K2: Modified MIT license; free for most uses but requires attribution for products exceeding 100 M monthly active users or $20 M monthly revenue.

Creative example: Start‑up budgeting

A mid‑sized SaaS company wants to integrate an AI code assistant processing 500 M tokens a month. Using GLM 4.5 at $0.11 input / $0.28 output, the cost is around $195 per month. Using Kimi K2 costs approximately $825 ($75 input + $750 output). Qwen 3 falls between, depending on provider pricing. For the same capacity, the cost difference could pay for additional developers or GPUs.

Expert Insights

• Z.AI’s documentation underscores that GLM 4.5 achieves high speed and low cost, making it attractive for high‑volume applications.
  
  
• Industry analyses point out that hardware efficiency influences total cost; GLM’s ability to run on fewer chips reduces capital expenses.
  
  
• Analysts caution that pricing tables seldom account for network and storage costs incurred when sending long contexts to the cloud.
  
  

Tool‑calling & agentic capabilities: Which model behaves like a real agent?

Why tool‑calling matters

Tool‑calling allows language models to execute functions, query databases, call APIs, or use calculators. In an agentic system, the model decides which tool to use and when, enabling complex workflows like research, debugging, data analysis, and dynamic content creation. Clarifai offers a tool orchestration framework that seamlessly integrates these function calls into your applications, abstracting API details and managing rate limits.

Comparing tool‑calling performance

• GLM 4.5: Highest tool‑calling success at 90.6 %. Its architecture integrates planning and execution, making it a natural fit for multi‑step workflows.
  
  
• Kimi K2 Thinking: Capable of 200–300 sequential tool calls, providing transparency via a reasoning trace.
  
  
• Qwen 3 Coder: Supports function‑calling protocols and integrates with CLIs for code tasks. Its dual modes allow quick switching between generation and reasoning.
  
  

Creative example: Automated research assistant

Suppose you’re building a research assistant that needs to gather news articles, summarise them, and create a report. GLM 4.5 can call a web search API, extract content, run summarisation tools, and compile results. Clarifai’s workflow engine can manage the sequence, allowing the model to call Clarifai’s NLP and Vision APIs for classification, sentiment analysis, or image tagging.

Expert Insights

• DataCamp emphasises that transparent reasoning in K2 exposes intermediate steps, making it easier to debug agent decisions.
  
  
• Independent tests show GLM’s tool‑calling leads in debugging scenarios, especially memory leak analysis.
  
  
• Analysts note Qwen’s function‑calling is robust but depends on the surrounding tool ecosystem and documentation.
  
  

Speed & efficiency: Which model runs the fastest?

Generation speed and latency

• GLM 4.5 offers 100+ tokens/sec generation speeds and claims peaks of 200 tokens/sec. Its first‑token latency is low, making it responsive for real‑time applications.
  
  
• Kimi K2 produces about 47 tokens/sec with a 0.53 sec first‑token latency. When combined with quantisation (INT4), K2’s throughput doubles without sacrificing accuracy.
  
  
• Qwen 3 has variable speed depending on mode: Rapid mode is fast, but Deep mode incurs longer reasoning time. Running in multi‑GPU setups further increases throughput.

Hardware efficiency & quantisation

GLM 4.5’s architecture emphasises hardware efficiency. It runs on eight H20 chips, and the Air variant runs on a single GPU, making it accessible for on‑prem deployment. K2 and Qwen require more VRAM and multiple GPUs. Quantisation techniques like INT4 and heavy modes allow trade‑offs between speed and accuracy.

Creative example: Real‑time chat vs. batch processing

In a real‑time chat assistant for customer support, GLM 4.5 or Qwen 3 Rapid mode will deliver quick responses with minimal delay. For batch code generation tasks, Kimi K2 with heavy mode may deliver higher quality at the cost of latency. Clarifai’s compute orchestration can schedule heavy tasks on larger GPU clusters and run quick tasks on edge devices.

Expert Insights

• Z.AI notes that GLM 4.5’s high‑speed mode supports low latency and high concurrency, making it ideal for interactive applications.
  
  
• Evaluators highlight that K2’s quantisation doubles inference speed with minimal accuracy loss.
  
  
• Industry analyses point out that Qwen’s deep mode is resource‑intensive, requiring careful scheduling in production systems.
  
  

Language & multimodal support: Who speaks more languages?

Multilingual capabilities

• Qwen 3 leads in language coverage: 119 human languages and 358 programming languages. This makes it ideal for international teams, cross‑lingual research, or working with obscure codebases.
• GLM 4.5 offers strong multilingual support, particularly in Chinese and English, and its visual variant (GLM 4.5‑V) extends to images and text.
• Kimi K2 specialises in code and is language‑agnostic for programming tasks but doesn’t support as many human languages.

Multimodal extensions

GLM 4.5‑V accepts images, enabling vision‑language tasks like document OCR or design layouts. Qwen has a VL Plus variant (vision + language). These multimodal models remain in early access but will be pivotal for building agents that understand websites, diagrams, and videos. Clarifai’s Vision API can complement these models by providing high‑precision classification, detection, and segmentation on images and videos.

Creative example: Global codebase translation

A multinational company has code comments in Mandarin, Spanish, and French. Qwen 3 can translate comments while refactoring code, ensuring global teams understand each function. When combined with Clarifai’s language detection models, the workflow becomes seamless.

Expert Insights

• Analysts note that Qwen’s polyglot support opens the door for legacy or niche programming languages and cross‑lingual documentation.
  
  
• Z.AI documentation emphasises GLM 4.5’s visual language variants for multimodal tasks.
  
  
• Evaluations indicate that Kimi K2’s focus on code ensures strong performance across programming languages, though it doesn’t cover as many natural languages.
  
  

Real‑world use cases & task performance

Coding tasks: building, refactoring & debugging

Independent evaluations reveal clear strengths:

• Full‑stack feature implementation: Kimi K2 completed tasks (e.g., building user authentication) in three prompts at low cost. Qwen 3 produced excellent documentation but was slower and more expensive. GLM 4.5 produced basic implementations quickly but lacked depth.
  
  
• Legacy code refactoring: Qwen 3’s long context allowed it to refactor a 2,000‑line jQuery file into React with reusable components. Kimi K2 handled the task but required splitting files because of its context limit. GLM 4.5’s response was the fastest but left some jQuery patterns unchanged.
  
  
• Debugging production issues: GLM 4.5 excelled at diagnosing memory leaks using tool calls and completed the task in minutes. Kimi K2 found the issue but required more prompts.

Design & creative tasks

A comparative test generating UI components (modern login page and animated weather cards) showed all models could build functional pages, but GLM 4.5 delivered the most refined design. Its Air variant achieved smooth animations and polished UI details, demonstrating strong front‑end capabilities.

Agentic tasks & research

K2 Thinking orchestrated 200–300 tool calls to conduct daily news research and synthesis. This makes it suitable for agentic workflows such as data analysis, finance reporting, or complex system administration. GLM 4.5 also performed well, leveraging its high tool‑calling success in tasks like heap dump analysis and automated ticket responses.

Creative example: Automated code reviewer

You can build a code reviewer that scans pull requests, highlights issues, and suggests fixes. The reviewer uses GLM 4.5 for quick analysis and tool invocation (e.g., running linters), and Kimi K2 to propose high‑quality, context‑aware code changes. Clarifai’s annotation and workflow tools manage the pipeline: capturing code snapshots, triggering model calls, logging results, and updating the development dashboard.

Expert Insights

• Evaluations show Kimi K2 is the most reliable in greenfield development, completing 93 % of tasks.
  
  
• Qwen 3 dominates large‑scale refactoring thanks to its context window.
  
  
• GLM 4.5 outperforms in debugging and tool‑dependent tasks due to its high tool‑calling success.
  
  

Deployment & ecosystem considerations

API vs. self‑hosting

• Qwen 3 Max is API‑only and expensive. The open‑weight Qwen 3 Coder is available via API and open source, but scaling may require significant hardware.
  
  
• Kimi K2 and GLM 4.5 offer downloadable weights with permissive licenses. You can deploy them on your own infrastructure, preserving data control and lowering costs.
  
  

Documentation & community

• GLM 4.5 has well‑written documentation with examples, accessible in both English and Chinese. Community forums actively support international developers.
  
  
• Qwen 3 documentation can be sparse, requiring familiarity to use effectively.
  
  
• Kimi K2 documentation exists but feels incomplete.
  
  

Compliance & data sovereignty

Open models allow on‑prem deployment, ensuring data never leaves your infrastructure, critical for GDPR and HIPAA compliance. API‑only models require trusting the provider with your data. Clarifai offers on‑prem and private‑cloud options with encryption and access controls, enabling organisations to deploy these models securely.

Creative example: Hybrid deployment

A healthcare company wants to build a coding assistant that processes patient data. They use Kimi K2 locally for code generation, and Clarifai’s secure workflow engine to orchestrate external API calls (e.g., patient record retrieval), ensuring sensitive data never leaves the organisation. For non‑sensitive tasks like UI design, they call GLM 4.5 via Clarifai’s platform.

Expert Insights

• Analysts stress that data sovereignty remains a key driver for open models; on‑prem deployment reduces compliance headaches.
  
  
• Independent evaluations recommend GLM 4.5 for developers needing thorough documentation and community support.
  
  
• Researchers warn that API‑only models can incur high costs and create vendor lock‑in.
  
  

Emerging trends & future outlook: What’s next?

Agentic AI & transparent reasoning

The next frontier is agentic AI: systems that plan, act, and adapt autonomously. K2 Thinking and GLM 4.5 are early examples. K2’s reasoning_content field lets you see how the model solves problems. GLM’s hybrid modes demonstrate how models can switch between planning and execution. Expect future models to combine planner modules, retrieval engines, and execution layers seamlessly.

Mixture‑of‑Experts at scale

MoE architectures will continue to scale, potentially reaching multi‑trillion parameters while controlling inference cost. Advanced routing strategies and dynamic expert selection will allow models to specialise further. Research by Shazeer and colleagues laid the groundwork; Chinese labs are now pushing MoE into production.

Quantisation, heavy modes & sustainability

Quantisation reduces model size and increases speed. INT4 quantisation doubles K2’s throughput. Heavy modes (e.g., K2’s eight parallel reasoning paths) improve accuracy but raise compute demands. Striking a balance between speed, accuracy, and environmental impact will be a key research area.

Long context windows & memory management

The context arms race continues: Qwen 3 already supports 1 M tokens, and future models may go further. However, longer contexts increase cost and complexity. Efficient retrieval, summarisation, and vector search (like Clarifai’s Context Engine) will be essential.

Licensing & open‑source momentum

More models are being released under MIT or Apache licenses, empowering enterprises to deploy locally and fine‑tune. Expect new versions: Qwen 3.25, GLM 4.6, and K2 Thinking improvements are already on the horizon. These open releases will further erode the advantage of proprietary models.

Geopolitics & compliance

Hardware restrictions (e.g., H20 chips vs. export‑controlled A100) shape model design. Data localisation laws drive adoption of on‑prem solutions. Enterprises will need to partner with platforms like Clarifai to navigate these challenges.

Expert Insights

• VentureBeat notes that K2 Thinking beats GPT‑5 in several reasoning benchmarks, signalling that the gap between open and proprietary models has closed.
  
  
• Vals AI updates show that K2 Thinking improves performance but faces latency challenges compared to GLM 4.6.
  
  
• Analysts predict that integrating retrieval‑augmented generation with long context models will become standard practice.
  
  

Conclusion & recommendation matrix

Which model should you choose?

Your selection depends on use case, budget, and infrastructure. Below is a guideline:

How Clarifai fits in

Clarifai’s AI Platform helps you deploy and orchestrate these models without worrying about hardware or complex APIs. Use Clarifai’s compute orchestration to schedule heavy K2 jobs on GPU clusters, run GLM 4.5 Air on edge devices, and integrate Qwen 3 into multi‑modal workflows. Clarifai’s context engine improves long‑context performance through efficient retrieval, and our model hub lets you switch models with a few clicks. Whether you’re building an internal coding assistant, an autonomous agent, or a multilingual support bot, Clarifai provides the infrastructure and tooling to make these frontier models production‑ready.

Frequently Asked Questions

Which model is best for pure coding tasks?

Kimi K2 often delivers the highest accuracy on real coding tasks, completing 14 of 15 tasks in an independent test. However, Qwen 3 excels at large codebases due to its long context.

Who has the longest context window?

Qwen 3 Coder leads with a native 256 K token window, expandable to 1 M tokens. Kimi K2 and GLM 4.5 offer ~128 K.

Are these models open source?

Yes. Kimi K2 is released under a modified MIT license requiring attribution for very large deployments. GLM 4.5 uses an MIT license. Qwen 3 is released under Apache 2.0.

Can I run these models locally?

Kimi K2 and GLM 4.5 provide weights for self‑hosting. Qwen 3 offers open weights for smaller variants; the Max version remains API‑only. Local deployments require multiple GPUs—GLM 4.5’s Air variant runs on consumer hardware.

How do I integrate these models with Clarifai?

Use Clarifai’s compute orchestration to run heavy models on GPU clusters or local runners for on‑prem. Our API gateway supports multiple models through a unified interface. You can chain Clarifai’s Vision and NLP models with LLM calls to build agents that understand text, images, and videos. Contact Clarifai’s support for guidance on fine‑tuning and deployment.

Are these models safe for sensitive data?

Open models allow on‑prem deployment, so data stays within your infrastructure, aiding compliance. Always implement rigorous security, logging, and anonymisation. Clarifai provides tools for data governance and access control.