模型

Inkling

Thinking Machines Lab's open-weights general-purpose multimodal Mixture-of-Experts model with 975B total parameters and 41B active parameters. Inkling accepts text, image, and audio inputs, produces text, and is designed for agentic and tool-use systems, coding assistants, chatbots, and retrieval-augmented generation.

GPT-5.6 Luna

The fast, low-cost tier of OpenAI's GPT-5.6 series, optimized for high-volume, latency-sensitive tasks such as classification, extraction, routing, and lightweight agentic steps. Approaches the larger GPT-5.6 tiers on many benchmarks while running several times faster at a fraction of the price.

GPT-5.6 Terra

OpenAI GPT-5.6 系列的均衡层级，以少量峰值质量换取显著更低的延迟和成本。它保留了强大的推理、编程和智能体工具使用能力，并支持可配置的推理强度，适合作为需要大规模前沿能力的生产工作负载的默认选择。

Muse Spark 1.1

Meta Superintelligence Labs' updated flagship, building on Muse Spark with stronger agentic reasoning, more reliable multi-agent orchestration, and improved multimodal understanding across voice, text, and image. Extends the context window and reduces latency and reasoning token usage while raising coding and tool-use accuracy. Powers Meta AI across its product ecosystem.

Gemini 3.5 Pro

Google DeepMind 的旗舰 Gemini 模型，基于全新底层架构重建，拥有 200 万 token 上下文窗口，并提供面向高难度数学、编程和多模态任务的 Deep Think 推理模式。原生支持文本、图像、音频和视频，具备流式函数调用和强大的长上下文信息关联能力。

GPT-5.6 Sol

OpenAI GPT-5.6 系列旗舰模型，在提升可靠性和效率的同时，推进了编程、科学推理、长周期规划和智能体工作流能力。新增最高推理强度设置，以及可为复杂多步骤任务启动子智能体的 ultra 模式。

Grok 4.5

xAI 迄今最强的模型，面向编程、智能体任务和知识工作，并与真实软件工程中的编程工具协同开发。提供实时信息访问、扩展推理和大上下文工具调用，并兼容 OpenAI API。

Claude Sonnet 5

Anthropic 能力最强的 Sonnet 级模型，以更低价格将前沿编程、智能体和专业工作能力带到中型层级，并缩小了与 Opus 4.8 的差距。支持可选推理强度的自适应思考、100 万 token 上下文窗口，以及文本、图像和文件输入。代号 Fennec。

Command A+

Cohere's enterprise flagship model building on Command A with stronger reasoning, agentic tool use, and multilingual performance across 23 languages. Optimized for secure, high-throughput RAG, retrieval, and long-horizon agent workflows in regulated environments, with private and on-premise deployment options.

DiffusionGemma

Google DeepMind's experimental diffusion-based member of the Gemma 4 open model family. Unlike autoregressive models that generate text one token at a time, DiffusionGemma denoises a canvas of placeholder tokens to produce up to 256 tokens in parallel, finalizing output in one block. A Mixture-of-Experts model with 26B total parameters and 3.8B active per inference, delivering roughly 4x the throughput of similarly sized autoregressive Gemma models on local hardware. Excels at non-linear tasks like in-line editing, molecular sequencing, mathematical graphing, and self-correcting puzzles.

Claude Fable 5

Anthropic 首款公开提供的 Mythos 级模型，能力超过该公司此前面向公众发布的所有模型。它在几乎所有已测试的基准中达到领先水平，尤其擅长软件工程、知识工作、视觉理解和科学研究；任务越长、越复杂，优势越明显。内置安全机制会将敏感的网络安全、生物、化学和蒸馏查询路由至 Claude Opus 4.8。

Claude Mythos 5

Anthropic's frontier Mythos-class model — the same underlying model as Claude Fable 5 but with safeguards lifted in some areas. It has the strongest cybersecurity capabilities of any model in the world, alongside state-of-the-art performance in software engineering, knowledge work, vision, and scientific research. Access is restricted to a small group of trusted cyberdefenders and infrastructure providers through Project Glasswing.

Gemma 4 12B

Google's medium-size open-weight model with 12 billion parameters from the Gemma 4 family. Encoder-free unified multimodal architecture that natively processes text, image, audio, and video inputs without dedicated encoders. Features a 256K context window and supports 140+ languages. First medium-sized model capable of natively ingesting audio. Suitable for local deployment on GPUs with 16GB VRAM.

Nemotron 3 Ultra

NVIDIA's flagship open 550B-parameter Mixture-of-Experts model with 55B active parameters, built for frontier reasoning and orchestration in long-running agentic systems. Features hybrid Mamba-Transformer architecture, LatentMoE routing, multi-token prediction, and NVFP4 precision for 5x higher throughput. Achieves 30% lower cost-to-task-completion on agentic benchmarks. Supports 1M+ token context window with 95% accuracy on Ruler@1M.

Claude Opus 4.8

Anthropic 最先进的模型，在 Opus 4.7 基础上提升了编程、智能体技能、推理和知识工作的多项基准表现。具备更高的诚实性、更高效的工具使用、动态工作流支持和更好的对齐表现。

Palmyra X5

Writer's most advanced adaptive reasoning model with a 1 million token context window. Processes full million-token prompts in approximately 22 seconds with multi-turn function calls in 300ms. Optimized for enterprise agentic AI workflows at 3-4x lower cost than GPT-4.1.

DBRX

Databricks' open-source 132B parameter Mixture-of-Experts transformer model with 36B active parameters per input. Released under Databricks Open Model License, optimized for enterprise workloads including SQL generation and coding tasks.

Gemini 3.5 Flash

Google DeepMind's balanced Gemini 3.5 model that pairs Pro-line reasoning quality with Flash-line latency and cost. Natively multimodal across text, image, audio, and video with a 1M-token context window, configurable thinking levels, and streaming function calling, tuned for high-throughput production workloads.

Gemini 3.1 Flash-Lite

Google's most cost-efficient Gemini model optimized for high-volume, low-latency use cases. Delivers 2.5x faster time to first token versus Gemini 2.5 Flash with full multimodal support. Ideal for agentic tasks, data extraction, translation, and classification.

Gemini 3 Flash

Google's balanced model combining Gemini 3 Pro's reasoning capabilities with the Flash line's latency, efficiency, and cost. Features configurable thinking levels, multimodal function responses, and streaming function calling for complex agentic workflows.

Granite 4.1 30B

IBM's largest dense decoder-only 30B parameter language model from the Granite 4.1 family. Trained on approximately 15T tokens with long-context extension up to 512K tokens. Supports tool calling, RAG, code generation, multilingual tasks across 12 languages. Released under Apache 2.0.

Granite 4.1 8B

IBM's dense decoder-only 8B parameter language model from the Granite 4.1 family. Supports 131K-token context, tool calling, RAG, code generation with fill-in-the-middle, text summarization, classification, and extraction across 12 languages. Released under Apache 2.0.

Laguna M.1

Poolside AI's flagship agentic coding model with 225B total parameters and 23B active (MoE). Trained from scratch in-house on 30T tokens across 6,144 NVIDIA Hopper GPUs. Optimized for complex multi-step software engineering tasks including codebase exploration, file editing, test running, and iterative debugging.

GPT-5.5

OpenAI's most capable model designed for complex real-world work including coding, online research, information analysis, and document creation. Features advanced agentic capabilities with tool search and multi-step task execution.