Shanghai AI Laboratory

Atria Dawn Preview

Shanghai AI Laboratory

Atria Dawn Preview is Shanghai AI Laboratory’s agentic model for turning research and engineering questions into executable results. Built on GLM-5.2’s 744B mixture-of-experts foundation, it is post-trained for discovery, code creation, office deliverables, and cybersecurity tasks in authorized environments. The released text-only checkpoint supports a documented 256K inference context, downloadable MIT-licensed weights, and hosted API access. It combines tool execution with environmental feedback for experiment design, failure recovery, and verification. Configuration maximums above the documented serving limit are not treated as evidence of a larger supported context.

Intern S2 397B

Shanghai AI Laboratory

Intern S2 is the stable scientific multimodal follow-up to the 397B preview checkpoint. It strengthens extended agent execution and research workflows, with deep-thinking and ordinary-response modes. Public API documentation identifies the stable model separately and schedules preview decommissioning for October 31. The release includes downloadable Apache-licensed weights and chat access. The official laboratory announcement identifies the public release date as 2026-09-13.

Intern Lumina U2

Shanghai AI Laboratory

Intern Lumina U2 is a unified language-capable multimodal diffusion model covering text questions, visual understanding, image generation, and editing. Its sparse 16B backbone activates 1B parameters and uses an eight-codebook discrete visual tokenizer. The Ascend-trained weights are publicly available; the NVIDIA-trained checkpoint, training code, and report remain future work. Code publication in August preceded the September weight release. Primary catalogs verify the release month; the exact first-public day remains unverified and is not inferred from private repository or API creation metadata.

Intern S2 Mobius

Shanghai AI Laboratory

Intern S2 Mobius is a 35B research model that separates knowledge storage from reasoning computation. Globally shared memory lets recurrent reasoners refine continuous hidden states and access knowledge beyond a fixed layer hierarchy. It is continually pretrained from Qwen3.5-35B and post-trained with supervised learning and reinforcement learning. The public checkpoint and deployment code allow study of concise latent reasoning and multi-token prediction. Primary catalogs verify the release month; the exact first-public day remains unverified and is not inferred from private repository or API creation metadata.

Intern S2 Preview 397B

Shanghai AI Laboratory

Intern S2 Preview scales multimodal pretraining, scientific reinforcement learning, and long-horizon agent environments. It learns from raw pages of scientific literature and combines general reasoning with tasks across more than twenty scientific domains. The 397B checkpoint supports language output, image understanding, and tool calls. Its text evaluations use up to 256K inference, while multimodal evaluations use shorter 64K limits. The official laboratory announcement identifies the public release date as 2026-07-17.

Agents-A1 4B

Shanghai AI Laboratory

Agents-A1 4B is Shanghai AI Laboratory’s model for long-horizon search, engineering, and scientific agent workflows. Its training combines long verified trajectories, domain experts, and on-policy distillation to consolidate specialized skills into one language model. It generates reasoning, text, and tool requests; external tools and a harness provide execution. Official cards explicitly date the 2026-07-14 release and document a 262K context. Apache-licensed weights and evaluation code support local deployment, while benchmark performance depends on the evaluation environment and external search infrastructure.

Agents-A1

Shanghai AI Laboratory

Agents-A1 is Shanghai AI Laboratory’s model for long-horizon search, engineering, and scientific agent workflows. Its training combines long verified trajectories, domain experts, and on-policy distillation to consolidate specialized skills into one language model. It generates reasoning, text, and tool requests; external tools and a harness provide execution. Official cards explicitly date the 2026-06-26 release and document a 262K context. Apache-licensed weights and evaluation code support local deployment, while benchmark performance depends on the evaluation environment and external search infrastructure.

Intern-S2 Preview 35B

Shanghai AI Laboratory

Intern-S2 Preview 35B is the efficient scientific multimodal preview in Shanghai AI Laboratory’s next generation. Continued pretraining from Qwen3.5 and scientific task scaling strengthen reasoning and agent use while activating about 3B of 35B parameters. Specialized real-valued prediction modules extend the model to scientific structure tasks alongside natural-language and image understanding. The laboratory explicitly dates the weight release to May 15. This identity remains available separately from the later 397B preview; the legacy intern-s2-preview API alias currently points to this 35B model.

Intern-S1 Pro

Shanghai AI Laboratory

Intern-S1 Pro expands Shanghai AI Laboratory’s scientific multimodal family to one trillion total parameters with 22B activated per token. It combines a sparse mixture of 512 experts, Fourier position encoding, and improved time-series modeling to support scientific reasoning alongside general language and vision tasks. The laboratory’s February 5 announcement explicitly dates the open-weight release to February 4, preceding the March technical report. Apache-licensed weights, chat, and an API are available, with a documented 256K context. Its full weight storage requirements remain substantial despite sparse activation.

InternVL 3.5

Shanghai AI Laboratory

InternVL 3.5 is Shanghai AI Laboratory’s open-weight vision-language model generation. A vision-language family using offline then online cascade reinforcement learning, a visual resolution router, and decoupled vision-language deployment. It supports language responses to images and video frames, document analysis, and visual questions. This family page groups ordinary size and packaging variants. Context and licensing depend on the selected checkpoint; large models can inherit additional language-backbone license conditions. Published evaluation results use specific visual preprocessing and inference settings, so they do not guarantee application performance.

Intern-S1 Mini

Shanghai AI Laboratory

Intern-S1 Mini is the compact member of Shanghai AI Laboratory’s scientific multimodal model family. It pairs an 8B language backbone with a roughly 300M vision encoder, retaining scientific question answering, diagram interpretation, and reasoning in a smaller deployment footprint than the 241B flagship. The release provides Apache-licensed weights and hosted inference. The official announcement dates the independently released Mini checkpoint to August 21, after the July 26 flagship. Its API documentation specifies a 32K context window.

Intern-S1

Shanghai AI Laboratory

Intern-S1 is Shanghai AI Laboratory’s scientific multimodal foundation model, combining general language abilities with specialized scientific reasoning. It contains a 235B mixture-of-experts language backbone and a 6B vision encoder, totaling about 241B parameters with 28B active. Continued pretraining includes scientific text and multimodal material for interpreting chemical structures, biological sequences, and experimental figures. Apache-licensed weights and hosted chat/API access are available. The official year-end retrospective establishes a July 2025 release; the August technical report is a later publication.

InternVL 3

Shanghai AI Laboratory

InternVL 3 is Shanghai AI Laboratory’s open-weight vision-language model generation. A native multimodal pretraining generation with variable visual position encoding, stronger perception and reasoning, and multimodal test-time scaling. It supports language responses to images and video frames, document analysis, and visual questions. This family page groups ordinary size and packaging variants. Context and licensing depend on the selected checkpoint; large models can inherit additional language-backbone license conditions. Published evaluation results use specific visual preprocessing and inference settings, so they do not guarantee application performance.

InternLM 3

Shanghai AI Laboratory

InternLM 3 is Shanghai AI Laboratory’s language model release for multilingual conversation, reasoning, and local customization. The 8B instruction model combines stronger reasoning with tool calling and a 128K inference context. Official weights and deployment examples support reproducible research and private deployment. This page represents the named generation rather than creating duplicate entries for base, chat, and quantized packages. Chinese and English capabilities are emphasized. A large advertised context does not establish reliable retrieval or reasoning throughout every input length.

InternVL 2.5

Shanghai AI Laboratory

InternVL 2.5 is Shanghai AI Laboratory’s open-weight vision-language model generation. A multimodal generation with improved training data, visual reasoning, and mixed preference optimization, spanning compact to large instruction models. It supports language responses to images and video frames, document analysis, and visual questions. This family page groups ordinary size and packaging variants. Context and licensing depend on the selected checkpoint; large models can inherit additional language-backbone license conditions. Published evaluation results use specific visual preprocessing and inference settings, so they do not guarantee application performance.

InternLM 2.5

Shanghai AI Laboratory

InternLM 2.5 is Shanghai AI Laboratory’s language model release for multilingual conversation, reasoning, and local customization. The reasoning and long-context generation introduces 7B weights followed by 1.8B and 20B checkpoints; the 1M context is available on the explicitly extended 7B version. Official weights and deployment examples support reproducible research and private deployment. This page represents the named generation rather than creating duplicate entries for base, chat, and quantized packages. Chinese and English capabilities are emphasized. A large advertised context does not establish reliable retrieval or reasoning throughout every input length.

InternVL 2

Shanghai AI Laboratory

InternVL 2 is Shanghai AI Laboratory’s open-weight vision-language model generation. An instruction-tuned family for image, multi-image, and video understanding, pairing InternViT encoders with language backbones through an MLP projector. It supports language responses to images and video frames, document analysis, and visual questions. This family page groups ordinary size and packaging variants. Context and licensing depend on the selected checkpoint; large models can inherit additional language-backbone license conditions. Published evaluation results use specific visual preprocessing and inference settings, so they do not guarantee application performance.

InternLM 2

Shanghai AI Laboratory

InternLM 2 is Shanghai AI Laboratory’s language model release for multilingual conversation, reasoning, and local customization. The second generation uses a dense Transformer architecture and provides 7B and 20B base and chat models, followed by a 1.8B tier. Official weights and deployment examples support reproducible research and private deployment. This page represents the named generation rather than creating duplicate entries for base, chat, and quantized packages. Chinese and English capabilities are emphasized. A large advertised context does not establish reliable retrieval or reasoning throughout every input length.