Amazon Bedrock

Voxtral Small 24B is a text model.

gpt-oss-120b is OpenAI's open-weight Mixture-of-Experts language model with 117B total and 5.1B active parameters, released under the Apache 2.0 license. It takes text input and produces text output with a 128K-token context window, configurable low, medium and high reasoning, full chain-of-thought access and native tool use including function calling and structured outputs.

DeepSeek-V3.2 uses DeepSeek Sparse Attention for efficient long contexts, with strong reasoning and tool use.

Gemma 3 27B Instruct is the 27-billion-parameter instruction-tuned model in Google DeepMind's Gemma 3 family of open-weight multimodal models. It takes text and image input and produces text output, with a 128K-token context window and support for over 140 languages, for tasks such as chat, summarization, question answering and image understanding.

MiniMax-M2.5 is MiniMax's open-weight large language model for coding, agentic tool use and search, and office work. It takes text input and produces text output, and is built for complex multi-step agentic tasks and deliverable outputs such as Word, PowerPoint and Excel documents.

Kimi K2.5 is Moonshot AI's open-weight, natively multimodal Mixture-of-Experts model with 1T total and 32B active parameters and a 256K-token context window. It accepts text and image input, offers thinking and instant modes, and is built for coding, visual coding, agent swarm workflows and office tasks.

NVIDIA Nemotron 3 Nano 30B is a compact and efficient language model from NVIDIA, optimized for fast inference while maintaining strong performance across diverse tasks.

Qwen3-VL 235B A22B Instruct is Alibaba's Qwen vision-language Mixture-of-Experts model with about 236B total and 22B active parameters. It takes image, video and text input and produces text, with a native 256K-token context, OCR in 32 languages, GUI agent abilities and long-video understanding.

GLM-4.7 is Z.ai's open-weight Mixture-of-Experts model with 355B total and 32B active parameters. It takes text input and output with a 200K-token context window and up to 128K output tokens, and is built for agentic coding, UI generation, tool use, reasoning and writing.

GLM-5 is Z.ai's open-weight Mixture-of-Experts model with 744B total and 40B active parameters, built for complex systems engineering and long-horizon agentic tasks. It takes text input and output with a 200K-token context window and up to 128K output tokens.

DeepSeek V3.1 is DeepSeek's hybrid Mixture-of-Experts model with 671B total and 37B active parameters, supporting both thinking and non-thinking modes in a single model. It has a 128K-token context window and improves tool calling and agent tasks over DeepSeek V3.

Gemma 3 12B Instruct is the 12-billion-parameter instruction-tuned model in Google DeepMind's Gemma 3 family of open-weight multimodal models. It takes text and image input and produces text output, with a 128K-token context window and support for over 140 languages, for tasks such as chat, summarization, question answering and image understanding.

Gemma 3 4B Instruct is the 4-billion-parameter instruction-tuned model in Google DeepMind's Gemma 3 family of open-weight multimodal models. It takes text and image input and produces text output, with a 128K-token context window and support for over 140 languages, for tasks such as chat, summarization, question answering and image understanding.

gpt-oss-20b is an open-weight 21B parameter model released by OpenAI under the Apache 2.0 license. It uses a Mixture-of-Experts (MoE) architecture with 3.6B active parameters per forward pass, optimized for lower-latency inference. The model is trained in OpenAI’s Harmony response format and supports reasoning level configuration, fine-tuning, and agentic capabilities including function calling, tool use, and structured outputs.

Qwen3 32B is a dense 32.8B-parameter model in the Qwen3 family from Alibaba's Qwen team, released in April 2025. It switches between a thinking mode for complex reasoning and a fast non-thinking mode, supports 119 languages, and has a native 32K-token context window (131K with YaRN).

MiniMax-M2 is MiniMax's open-weight Mixture-of-Experts language model built for coding and agentic workflows, with 230 billion total parameters and 10 billion active per token. It takes text input and produces text output, with a context window of 204,800 tokens. It is designed for multi-file edits, code-run-fix loops and test-validated repairs, and for long tool-calling sequences across shell, browser, retrieval and code-execution tools.

MiniMax-M2.1 is MiniMax's open-weight Mixture-of-Experts language model, an update to MiniMax-M2 focused on real-world coding and agentic tasks. It takes text input and produces text output. MiniMax highlights multi-language programming across Rust, Java, Go, C++, Kotlin, Objective-C, TypeScript and JavaScript, native Android and iOS development, web and app development, tool use and long-horizon planning, and multi-step office workflows.

Devstral 2 is Mistral AI's 123B dense transformer for agentic software engineering, released with open weights under a modified MIT license. It takes text input and produces text output, with a 256K context window. It is built to power coding agents that explore codebases, call tools and make coordinated edits across multiple files, for work such as bug fixing and modernizing legacy systems.

Magistral Small 1.2 (2509) is Mistral AI's 24B open-weight reasoning model, fine-tuned from Mistral Small 3.2. It takes text and image input and produces text output, generating a reasoning trace before its final answer, with a 128K context window and support for 24 languages. It is built for multi-step reasoning tasks and is compact enough for local deployment once quantized.

Ministral 3 14B Instruct is the largest instruction-tuned model in Mistral AI's Ministral 3 family of dense open-weight models, combining a 13.5B language model with a 0.4B vision encoder. It takes text and image input and produces text output, with native function calling and JSON output, and multilingual support. It is built for private chat and assistant deployments, local agentic workflows and fine-tuning.

Ministral 3 3B Instruct is the instruction-tuned 3B model in Mistral AI's Ministral 3 family of small dense open-weight models, combining a 3.4B language model with a 0.4B vision encoder. It takes text and image input and produces text output, with native function calling and JSON output, and support for 11 languages. It is built for edge and local deployment, and for tasks such as image captioning, classification, translation and data extraction.

Ministral 3 8B Instruct is the instruction-tuned 8B model in Mistral AI's Ministral 3 family of small dense open-weight models, combining an 8.4B language model with a 0.4B vision encoder. It takes text and image input and produces text output, with native function calling and JSON output, and multilingual support. It is built for local assistants, chat in constrained environments, image and document understanding, and agentic use cases.

Mistral Large 3 is Mistral AI's open-weight multimodal granular Mixture-of-Experts model, with 675B total and 41B active parameters. It takes text and image input with a 256K-token context window and is built for agentic work, tool use, retrieval-augmented systems, long documents and coding across dozens of languages.

Voxtral Mini 3B is a text model.

Kimi K2 Thinking is Moonshot AI's open-weight thinking model, a Mixture-of-Experts model with about 1 trillion total parameters and 32 billion active per token. It takes text input and produces text output, with a 256K-token context window. It is built to interleave step-by-step reasoning with function calls, and is designed to stay goal-directed across 200 to 300 sequential tool calls for research, coding and writing tasks.

NVIDIA Nemotron 3 Super is a hybrid Mixture-of-Experts (MoE) model engineered for highest compute efficiency and accuracy in multi-agent applications and specialized agentic systems. It is optimized to run many collaborating agents per application on a single GPU, delivering high accuracy for reasoning, tool use, and instruction following.

NVIDIA Nemotron Nano 12B v2 VL is an open 12.6B-parameter vision-language model from NVIDIA. It pairs a CRadioV2-H vision encoder with the Nemotron Nano 12B v2 language model. It takes English text, up to four images or a video as input and returns text, with 128K tokens of combined input and output context. It is built for document intelligence on invoices, receipts and manuals, visual question answering, summarization, multi-image reasoning and video understanding.

NVIDIA Nemotron Nano 9B v2 is an open 9B-parameter reasoning and chat model from NVIDIA, built on a hybrid Mamba-2 and Transformer architecture with only four attention layers. It was produced by pruning and aligning the 12B Nemotron Nano v2 base model. It handles text input and output with up to 128K tokens of context and supports tool calling. Reasoning is on by default and can be switched off, and an optional thinking budget caps reasoning tokens.

gpt-oss-safeguard-120b is an open-weight safety reasoning model from OpenAI, fine-tuned from gpt-oss-120b, with 117B total parameters and 5.1B active. It classifies content against a safety policy that the developer writes and supplies at inference time, and it produces a reviewable chain of thought for each decision. It is built for trust and safety tasks such as input and output filtering and content labeling, supports low, medium and high reasoning effort, and is released under Apache 2.0.

gpt-oss-safeguard-20b is the smaller open-weight safety reasoning model from OpenAI, fine-tuned from gpt-oss-20b, with about 21B total parameters and 3.6B active. It reads a developer-written safety policy at inference time and classifies content against it with a visible chain of thought, for lower-latency trust and safety tasks such as input and output filtering and content labeling. It supports low, medium and high reasoning effort and is released under Apache 2.0.

Qwen3 235B A22B Instruct 2507 is a text model.

Qwen3 Coder 30B A3B Instruct is an open-weight Mixture-of-Experts coding model from the Qwen team. It has 30.5B total parameters, with 3.3B active per token (8 of 128 experts), and a native 262,144-token context window. It is a text-only, non-thinking model built for agentic coding, agentic browser use and repository-scale code understanding, with a dedicated function-calling format, and is released under Apache 2.0.

Qwen3 Coder 480B A35B Instruct is a text model.

Qwen3 Coder Next is an open-weight coding model from Alibaba's Qwen team, built for coding agents and local development on the Qwen3-Next hybrid-attention Mixture-of-Experts architecture with 80B total and 3B active parameters. It has a native 262K-token context window and is aimed at agentic coding, tool calling and recovery from execution failures.

Qwen3 Next 80B A3B Instruct is a text model.

Palmyra Vision 7B is a text model.

GLM 4.7 Flash is a text model.