Best AI Models for Cerebras
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Compare the Top AI Models that integrate with Cerebras as of July 2026
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This a list of AI Models that integrate with Cerebras. Use the filters on the left to add additional filters for products that have integrations with Cerebras. View the products that work with Cerebras in the table below.
What are AI Models for Cerebras?
AI models are systems designed to simulate human intelligence by learning from data and solving complex tasks. They include specialized types like Large Language Models (LLMs) for text generation, image models for visual recognition and editing, and video models for processing and analyzing dynamic content. These models power applications such as chatbots, facial recognition, video summarization, and personalized recommendations. Their capabilities rely on advanced algorithms, extensive training datasets, and robust computational resources. AI models are transforming industries by automating processes, enhancing decision-making, and enabling creative innovations. Compare and read user reviews of the best AI Models for Cerebras currently available using the table below. This list is updated regularly.
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1
Kimi K2.7 Code
Moonshot AI
Kimi K2.7 Code is an open-source, coding-focused agentic AI model developed by Moonshot AI for long-horizon software engineering tasks. It is designed to improve coding performance, agent workflows, and real-world development assistance compared with earlier Kimi K2 versions. The model supports a 256K context window, making it useful for working with large codebases, long technical documents, and complex multi-step programming tasks. Kimi K2.7 Code is available through Kimi Code and API access, with OpenAI- and Anthropic-compatible options for easier integration into developer workflows. It is also listed on Hugging Face and supports deployment through inference engines such as vLLM, SGLang, and KTransformers. With improved agentic capabilities, long-context support, and reduced thinking-token usage compared with K2.6, Kimi K2.7 Code gives developers a flexible open-source option for AI-assisted coding.Starting Price: Free -
2
SWE-1.7
Cognition
SWE-1.7 is Cognition’s frontier software engineering model designed to deliver high intelligence at a lower rollout cost. The model is optimized for long-horizon agentic coding tasks, including debugging, feature implementation, codebase exploration, migrations, terminal workflows, and multilingual software engineering. SWE-1.7 was trained from a Kimi K2.7 base using large-scale reinforcement learning improvements across infrastructure, data quality, training stability, self-compaction, and long-running task execution. It is built to explore codebases thoroughly, probe edge cases, identify hidden requirements, and produce more complete end-to-end solutions. The model is available in Devin across web, desktop, and CLI through Cerebras at very high serving speeds. SWE-1.7 is positioned for developers and engineering teams that need cost-efficient frontier-level coding intelligence for complex real-world software work.Starting Price: $20/month -
3
Kimi K3
Moonshot AI
Kimi K3 is Moonshot AI’s most capable model, built for frontier intelligence scenarios such as software engineering, knowledge work, deep reasoning, and multimodal understanding. The model has 2.8 trillion parameters and uses Kimi Delta Attention, a hybrid linear attention mechanism, along with Attention Residuals for long-context performance. Kimi K3 supports a 1 million token context window, making it useful for analyzing large codebases, long documents, complex knowledge bases, and multi-step workflows. It includes native visual understanding for images and videos, with support for structured message formats, base64 image input, uploaded video files, and multimodal reasoning. Developers can use Kimi K3 through an OpenAI-compatible API with support for streaming, structured JSON output, partial mode, custom tools, dynamic tool loading, and automatic context caching.Starting Price: $3 per 1M tokens (input) -
4
Kimi K2.6
Moonshot AI
Kimi K2.6 is a next-generation agentic AI model developed by Moonshot AI, designed to push forward real-world execution, coding, and multi-step reasoning beyond earlier K2 and K2.5 versions. It builds on a Mixture-of-Experts architecture and the multimodal, agent-first foundation of the Kimi series, combining language understanding, coding, and tool use into a single system capable of planning and executing complex workflows. It introduces deeper reasoning capabilities and significantly improved agent planning, allowing it to break down tasks, coordinate tools, and handle multi-file or multi-step problems with greater accuracy and efficiency. It supports advanced tool calling with high reliability, enabling integration with external systems such as web search or APIs, and includes built-in validation mechanisms to ensure correct execution formats.Starting Price: Free -
5
Sonar
Perplexity
Perplexity has recently introduced an enhanced version of its AI search engine, named Sonar. Built upon the Llama 3.3 70B model, Sonar has undergone additional training to improve the factual accuracy and readability of responses in Perplexity's default search mode. This advancement aims to deliver users more precise and comprehensible answers while maintaining the platform's characteristic efficiency and speed. Sonar also provides real-time, web-wide research and Q&A capabilities, allowing developers to integrate these features into their products through a lightweight, cost-effective, and user-friendly API. The Sonar API supports advanced models like sonar-reasoning-pro and sonar-pro, designed for complex tasks requiring deep understanding and context retention. These models offer detailed answers with an average of twice as many citations as previous versions, enhancing the transparency and reliability of the information provided.Starting Price: Free -
6
Mercury 2
Inception
Mercury 2 is the first reasoning model fast enough to pick up the phone, a reasoning diffusion language model built for real-time voice agents. Instead of making callers wait through seconds of dead air while an autoregressive model generates thinking tokens one by one, Mercury 2 uses a diffusion large language model architecture to generate tokens in parallel, decoding 1000+ tokens per second on standard NVIDIA GPUs. That speed is fast enough to run a full reasoning pass and start speaking within the latency budget of a natural conversation, reducing the cost of reasoning from seconds of silence to roughly 300 milliseconds. Mercury models work by corrupting clean text into noise, then training a standard Transformer to reverse the process and predict clean text across all positions simultaneously. Because each denoising pass touches many tokens, generation uses the GPU more efficiently than one-token-at-a-time decoding, making custom-silicon-like speed possible on NVIDIA H100s.
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