Compare the Top Small Language Models that integrate with Claude Code as of September 2026

This a list of Small Language Models that integrate with Claude Code. Use the filters on the left to add additional filters for products that have integrations with Claude Code. View the products that work with Claude Code in the table below.

What are Small Language Models for Claude Code?

Small Language Models (SLMs) are compact AI models designed to perform natural language understanding and generation tasks while requiring significantly fewer computational resources than large language models (LLMs). These models are optimized for low latency, lower memory usage, on-device inference, and cost-efficient deployment, making them well suited for edge devices, mobile applications, embedded systems, and enterprise workloads with strict performance or privacy requirements. SLMs can power applications such as chatbots, document summarization, code generation, classification, translation, question answering, and AI agents while delivering fast inference and reduced infrastructure costs. Many small language models are available as open-source or commercial offerings and integrate with AI frameworks, inference engines, cloud platforms, and developer tools for flexible deployment. By balancing performance, efficiency, and scalability, small language models help organizations build responsive, cost-effective AI applications across a wide range of environments. Compare and read user reviews of the best Small Language Models for Claude Code currently available using the table below. This list is updated regularly.

  • 1
    Claude Haiku 4.5
    Anthropic has launched Claude Haiku 4.5, its latest small-language model designed to deliver near-frontier performance at significantly lower cost. The model provides similar coding and reasoning quality as the company’s mid-tier Sonnet 4, yet it runs at roughly one-third of the cost and more than twice the speed. In benchmarks cited by Anthropic, Haiku 4.5 meets or exceeds Sonnet 4’s performance in key tasks such as code generation and multi-step “computer use” workflows. It is optimized for real-time, low-latency scenarios such as chat assistants, customer service agents, and pair-programming support. Haiku 4.5 is made available via the Claude API under the identifier “claude-haiku-4-5” and supports large-scale deployments where cost, responsiveness, and near-frontier intelligence matter. Claude Haiku 4.5 is available now on Claude Code and our apps. Its efficiency means you can accomplish more within your usage limits while maintaining premium model performance.
    Starting Price: $1 per million input tokens
  • 2
    GLM-4.5V-Flash
    GLM-4.5V-Flash is an open source vision-language model, designed to bring strong multimodal capabilities into a lightweight, deployable package. It supports image, video, document, and GUI inputs, enabling tasks such as scene understanding, chart and document parsing, screen reading, and multi-image analysis. Compared to larger models in the series, GLM-4.5V-Flash offers a compact footprint while retaining core VLM capabilities like visual reasoning, video understanding, GUI task handling, and complex document parsing. It can serve in “GUI agent” workflows, meaning it can interpret screenshots or desktop captures, recognize icons or UI elements, and assist with automated desktop or web-based tasks. Although it forgoes some of the largest-model performance gains, GLM-4.5V-Flash remains versatile for real-world multimodal tasks where efficiency, lower resource usage, and broad modality support are prioritized.
    Starting Price: Free
  • 3
    SubQ 1.1 Small

    SubQ 1.1 Small

    Subquadratic

    SubQ 1.1 Small is a long-context AI model from Subquadratic designed to reason over complete enterprise artifacts such as codebases, document collections, contracts, and financial filings. It uses Subquadratic Sparse Attention, or SSA, to reduce the high compute costs normally associated with processing very large context windows. The model delivers near-perfect long-context retrieval across 1M, 2M, 6M, and 12M token tests while using far less attention compute than dense attention. SubQ 1.1 Small also maintains strong general reasoning, coding, knowledge, and agentic task performance across multiple benchmarks. Its capabilities make it useful for financial analysis, legal review, contract work, software engineering, due diligence, and other workflows where information is spread across large artifacts. SubQ is built for organizations that want to move beyond fragmented retrieval pipelines and enable direct reasoning over massive bodies of information.
  • 4
    Ling 3.0 Tiny

    Ling 3.0 Tiny

    Ant Group

    Ling 3.0 Tiny is an open-weights reasoning model with 7.9B total parameters, 1.3B active parameters, and a 262K-token context window. Built with a mixture-of-experts architecture, it extends the open-weights Pareto frontier for intelligence versus active parameters and is small enough to run locally in many settings. The model scores 25 on the Artificial Analysis Intelligence Index, comparable to gpt-oss-120b (high, 24) while using 15x fewer total parameters and 4x fewer active parameters. This parameter efficiency comes with relatively high token usage, with 213M output tokens required to run the Intelligence Index. Ling 3.0 Tiny also shows substantial improvements in hallucination behavior over Ling-mini-2.0, improving its AA-Omniscience score by 59 points while maintaining similar accuracy. Rather than guessing when uncertain, it attempted only 37% of questions in the evaluation, resulting in a 30% hallucination rate compared with 96% for the previous generation.
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