Guide

Official DeepSeek V4 Flash release with enhanced agentic capabilities and integrated DSpark speculative decoding

Official DeepSeek V4 Flash release with enhanced agentic capabilities and integrated DSpark speculative decoding

Official DeepSeek V4 Flash release with enhanced agentic capabilities and integrated DSpark speculative decoding

Official DeepSeek V4 Flash release with enhanced agentic capabilities and integrated DSpark speculative decoding

Inkling Small is an open-weight multimodal mixture-of-experts model from Thinking Machines Lab, with 12B active parameters out of 276B total. It is positioned as the smaller, more efficient member of...

Inkling Small is an open-weight multimodal mixture-of-experts model from Thinking Machines Lab, with 12B active parameters out of 276B total. It is positioned as the smaller, more efficient member of...

Inkling Small is an open-weight multimodal mixture-of-experts model from Thinking Machines Lab, with 12B active parameters out of 276B total. It is positioned as the smaller, more efficient member of...

Multimodal MoE reasoning model (276B total, 12B active) for text, image, and audio

Efficient model for low-latency assistance, extraction, and routine automation

Multimodal reasoning model for visual analysis, planning, and tool use

Video model for prompt-guided generation, editing, and motion workflows

Kimi multimodal agent model for visual understanding, coding, and planning

Speech generation model for controllable voice, narration, and audio delivery

Speech generation model for controllable voice, narration, and audio delivery

Qwen3.7 Flash is a vision-language reasoning model from Alibaba. It is suited for multimodal agents, visual coding, search, and computer interaction, with strengths in object recognition, spatial understanding, and real-world...

Multimodal Kimi model with 1M context and toggleable max-effort thinking for long-horizon agent work

Multimodal Kimi model with 1M context and toggleable max-effort thinking for long-horizon agent work

Multimodal Kimi model with 1M context and toggleable max-effort thinking for long-horizon agent work

Multimodal Kimi model with 1M context and toggleable max-effort thinking for long-horizon agent work

Fast-mode variant of [Opus 5](/anthropic/claude-opus-5) - identical capabilities with higher output speed at 2x pricing relative to regular Opus 5. Learn more in Anthropic's docs: https://platform.claude.com/docs/en/build-with-claude/fast-mode

Claude Opus 5 is Anthropic’s flagship model for demanding reasoning, coding, and long-horizon agentic work. It is particularly strong at end-to-end software tasks, code review and bug finding, visual analysis...

Claude Opus 5 is Anthropic’s flagship model for demanding reasoning, coding, and long-horizon agentic work. It is particularly strong at end-to-end software tasks, code review and bug finding, visual analysis...

Strongest Claude Opus model for coding, agents, and professional work

Strongest Claude Opus model for coding, agents, and professional work

Strongest Claude Opus model for coding, agents, and professional work

Strongest Claude Opus model for coding, agents, and professional work

Strongest Claude Opus model for coding, agents, and professional work

Strongest Claude Opus model for coding, agents, and professional work

Strongest Claude Opus model for coding, agents, and professional work

Flagship Claude model for deep reasoning, coding, and long-horizon agents

Flagship Claude model for deep reasoning, coding, and long-horizon agents

Strongest Claude Opus model for coding, agents, and professional work

*Ling-3.0-flash* is a *124B-parameter Mixture-of-Experts (MoE) model*, with approximately *5.1B parameters activated per token*. The model is designed with *token efficiency and production-scale agentic inference* as key priorities, enabling developers...

*Ling-3.0-flash* is a *124B-parameter Mixture-of-Experts (MoE) model*, with approximately *5.1B parameters activated per token*. The model is designed with *token efficiency and production-scale agentic inference* as key priorities, enabling developers...

Flagship Claude model for deep reasoning, coding, and long-horizon agents

Flagship Claude model for deep reasoning, coding, and long-horizon agents

Omni-modal model for text, vision, audio, and multimodal agent tasks

Preview Qwen flagship for million-token multimodal reasoning and long-horizon agentic workflows

Laguna S 2.1 is the latest coding agent model from [Poolside](<https://poolside.ai/>). Laguna S 2.1 is a 118B total parameter model with 8B active parameters, scoring 70.2% on Terminal-Bench 2.1 and...

Laguna S 2.1 is the latest coding agent model from [Poolside](<https://poolside.ai/>). Laguna S 2.1 is a 118B total parameter model with 8B active parameters, scoring 70.2% on Terminal-Bench 2.1 and...

Gemini 3.6 Flash is a high-efficiency model from Google for coding, agentic workflows, and web and app development. It is designed to produce polished outputs with fewer unnecessary edits and...

Gemini 3.6 Flash is a high-efficiency model from Google for coding, agentic workflows, and web and app development. It is designed to produce polished outputs with fewer unnecessary edits and...

Gemini 3.5 Flash Lite is a high-efficiency model from Google with upgraded agentic capabilities. It is suited for subagents that execute focused tasks within complex, multi-agent workflows.

Gemini 3.5 Flash Lite is a high-efficiency model from Google with upgraded agentic capabilities. It is suited for subagents that execute focused tasks within complex, multi-agent workflows.

Fast Gemini model balancing multimodal reasoning, tool use, and cost

Fast Gemini model balancing multimodal reasoning, tool use, and cost

Fast Gemini model balancing multimodal reasoning, tool use, and cost

Free provider route for experiments, demos, and cost-sensitive chat workloads

Agentic coding model from Poolside in the XS size class for local deployment

Fast Gemini model balancing multimodal reasoning, tool use, and cost

Low-latency Gemini model for high-volume multimodal and agent workloads