Spatial-SSRL-7B
internlm基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
Input Text · Image→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗DISCOVER & COMPARE
Explore AI models 84999
Spatial-SSRL-Qwen3VL-4B
internlm基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
Input Text · Image→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗SWE-Fixer-Editor-72B
internlm基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗SWE-Fixer-Retriever-7B
internlm基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Visual-ERM
internlm基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗InternVLA-M1
InternRobotics基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗InternVLA-N1-DualVLN
InternRobotics基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Agents-A1
InternScience> [!Note] > This repository contains model weights and configuration files for Agents-A1 in the Hugging Face Transformers format. > > These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, etc. **Agents‑A1** is a 35B Mixture‑of‑Experts agentic model from InternScience, built to scale heterogeneous agentic abilities across multiple domains including **Long‑horizon Search, Engineering, Scientific Research, Instruction Following, and Tool-calling**. We investigate agent-horizon scaling from two perspectives: scaling long-horizon trajectories and scaling heterogeneous agent abilities. From the scaling of long-horizon trajectories, **Agents‑A1** is trained with the assistance of a domain-grounded knowledge-action infrastructure that jointly constructs actions, observations, and verifier outcomes, turning the agent's process into a trainable target. From the scaling of heterogeneous agent abilities, **Agents‑A1** presents a three-stage training paradigm for building scalable general-purpose agentic model. First, we perform full-domain supervised fine-tuning to align the base model with broad agentic behaviors. Second, we train domain-level teacher models to capture specialized expertise in each domain. Third, we propose multi-teacher multi-domain on-policy distillation with heterogeneity-aware optimization to improve knowledge transfer efficiency across different domains.
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Agents-A1-4B
InternScience> [!Note] > This repository contains model weights and configuration files for Agents-A1 in the Hugging Face Transformers format. > > These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, etc. - **2026.7.8**: 🔥🔥 By popular demand from the community, our 4B model is coming in the next few days — making it faster and easier to build your own local AI assistant. - **2026.7.2**: 🔥🔥 Based on Agents-A1, we have released a series of quantized model variants. Please refer to the Agents-A1 collection. Besides, we’d like to thank the mlx-community for providing quantized versions at multiple scales. Try running Agents-A1 on your Mac!
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Agents-A1-4B-F16-GGUF
InternScience基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Agents-A1-4B-Q4_K_M-GGUF
InternScience> [!Note] > This repository contains model weights and configuration files for Agents-A1 in the Hugging Face Transformers format. > > These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, etc. - **2026.7.8**: 🔥🔥 By popular demand from the community, our 4B model is coming in the next few days — making it faster and easier to build your own local AI assistant. - **2026.7.2**: 🔥🔥 Based on Agents-A1, we have released a series of quantized model variants. Please refer to the Agents-A1 collection. Besides, we’d like to thank the mlx-community for providing quantized versions at multiple scales. Try running Agents-A1 on your Mac!
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Agents-A1-4B-Q8_0-GGUF
InternScience> [!Note] > This repository contains model weights and configuration files for Agents-A1 in the Hugging Face Transformers format. > > These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, etc. - **2026.7.8**: 🔥🔥 By popular demand from the community, our 4B model is coming in the next few days — making it faster and easier to build your own local AI assistant. - **2026.7.2**: 🔥🔥 Based on Agents-A1, we have released a series of quantized model variants. Please refer to the Agents-A1 collection. Besides, we’d like to thank the mlx-community for providing quantized versions at multiple scales. Try running Agents-A1 on your Mac!
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Agents-A1-FP8
InternScience基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Agents-A1-Q4_K_M-GGUF
InternScience> [!Note] > This repository contains model weights and configuration files for Agents-A1 in the Hugging Face Transformers format. > > These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, etc. - **2026.7.8**: 🔥🔥 By popular demand from the community, our 4B model is coming in the next few days — making it faster and easier to build your own local AI assistant. - **2026.7.2**: 🔥🔥 Based on Agents-A1, we have released a series of quantized model variants. Please refer to the Agents-A1 collection. Besides, we’d like to thank the mlx-community for providing quantized versions at multiple scales. Try running Agents-A1 on your Mac!
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Agents-A1-Q8_0-GGUF
InternScience基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗MLExpert
InternScience基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Sage-A1.5
InternScience> [!Note] > This repository contains model weights and configuration files for Sage-A1.5 in the Hugging Face Transformers format. > > These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, etc. **Sage‑A1.5** is a 35B Mixture‑of‑Experts agentic model based on **Qwen3.6** and **Agents-A1** from Shanghai AI Laboratory, built to advance long-horizon scientific problem solving through adaptive capability composition. Building upon **A1**, **Sage‑A1.5** learns not only to solve individual tasks, but also to organize work and revise its approach during execution, highlighting three key capabilities: **Agentic Orchestration, Verification, and Open Scientific Discovery**. First, agentic orchestration extends single-agent execution through task delegation and parallel collaboration among subagents with isolated contexts, enabling complex tasks to be decomposed and addressed concurrently while reducing interference across concurrent subagents. Second, agentic verification strengthens long-horizon reliability through progress checking, evidence validation, and reflective strategy revision, allowing **Sage‑A1.5** to recognize unproductive search paths and pursue alternative approaches. Third, open scientific discovery extends agentic capabilities beyond answering predefined questions to conducting exploratory research, connecting scientific inquiry with interactive analysis and inspectable research artifacts. We hope **Sage‑A1.5** provides the community with an accessible foundation for open scientific discovery, offering a practical path from long-horizon task execution to collaborative, verifiable, and inspectable scientific exploration.
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Sage-A1.5-F16-GGUF
InternScience> [!Note] > This repository contains **GGUF** model weights for Sage-A1.5 in the **F16** (unquantized) format, for deployment with llama.cpp, Ollama, LM Studio, and other GGUF-compatible runtimes. > > For the original Hugging Face Transformers weights (compatible with Transformers, vLLM, SGLang, etc.), see InternScience/Sage-A1.5. **Sage‑A1.5** is a 35B Mixture‑of‑Experts agentic model based on **Qwen3.6** and **Agents-A1** from Shanghai AI Laboratory, built to advance long-horizon problem solving through adaptive capability composition. Building upon **Agents‑A1**, **Sage‑A1.5** learns not only to solve individual tasks, but also to organize work and revise its approach during execution, highlighting three key capabilities: **Agentic Orchestration, Verification, and Open Scientific Discovery**. First, agentic orchestration extends single-agent execution through task delegation and parallel collaboration among subagents with isolated contexts, enabling complex tasks to be decomposed and addressed concurrently while reducing interference across concurrent subagents. Second, agentic verification strengthens long-horizon reliability through progress checking, evidence validation, and reflective strategy revision, allowing **Sage‑A1.5** to recognize unproductive search paths and pursue alternative approaches. Third, open scientific discovery extends agentic capabilities beyond answering predefined questions to conducting exploratory research, connecting scientific inquiry with interactive analysis and inspectable research artifacts. We hope **Sage‑A1.5** provides the community with an accessible foundation for open scientific discovery, offering a practical path from long-horizon task execution to collaborative, verifiable, and inspectable scientific exploration.
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Sage-A1.5-Q4_K_M-GGUF
InternScience> [!Note] > This repository contains **GGUF** model weights for Sage-A1.5, quantized to **Q4_K_M**, for deployment with llama.cpp, Ollama, LM Studio, and other GGUF-compatible runtimes. > > For the original Hugging Face Transformers weights (compatible with Transformers, vLLM, SGLang, etc.), see InternScience/Sage-A1.5. **Sage‑A1.5** is a 35B Mixture‑of‑Experts agentic model based on **Qwen3.6** and **Agents-A1** from Shanghai AI Laboratory, built to advance long-horizon scientific problem solving through adaptive capability composition. Building upon **Agents‑A1**, **Sage‑A1.5** learns not only to solve individual tasks, but also to organize work and revise its approach during execution, highlighting three key capabilities: **Agentic Orchestration, Verification, and Open Scientific Discovery**. First, agentic orchestration extends single-agent execution through task delegation and parallel collaboration among subagents with isolated contexts, enabling complex tasks to be decomposed and addressed concurrently while reducing interference across concurrent subagents. Second, agentic verification strengthens long-horizon reliability through progress checking, evidence validation, and reflective strategy revision, allowing **Sage‑A1.5** to recognize unproductive search paths and pursue alternative approaches. Third, open scientific discovery extends agentic capabilities beyond answering predefined questions to conducting exploratory research, connecting scientific inquiry with interactive analysis and inspectable research artifacts. We hope **Sage‑A1.5** provides the community with an accessible foundation for open scientific discovery, offering a practical path from long-horizon task execution to collaborative, verifiable, and inspectable scientific exploration.
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Sage-A1.5-Q6_K-GGUF
InternScience> [!Note] > This repository contains **GGUF** model weights for Sage-A1.5, quantized to **Q6_K**, for deployment with llama.cpp, Ollama, LM Studio, and other GGUF-compatible runtimes. > > For the original Hugging Face Transformers weights (compatible with Transformers, vLLM, SGLang, etc.), see InternScience/Sage-A1.5. **Sage‑A1.5** is a 35B Mixture‑of‑Experts agentic model based on **Qwen3.6** and **Agents-A1** from Shanghai AI Laboratory, built to advance long-horizon scientific problem solving through adaptive capability composition. Building upon **Agents‑A1**, **Sage‑A1.5** learns not only to solve individual tasks, but also to organize work and revise its approach during execution, highlighting three key capabilities: **Agentic Orchestration, Verification, and Open Scientific Discovery**. First, agentic orchestration extends single-agent execution through task delegation and parallel collaboration among subagents with isolated contexts, enabling complex tasks to be decomposed and addressed concurrently while reducing interference across concurrent subagents. Second, agentic verification strengthens long-horizon reliability through progress checking, evidence validation, and reflective strategy revision, allowing **Sage‑A1.5** to recognize unproductive search paths and pursue alternative approaches. Third, open scientific discovery extends agentic capabilities beyond answering predefined questions to conducting exploratory research, connecting scientific inquiry with interactive analysis and inspectable research artifacts. We hope **Sage‑A1.5** provides the community with an accessible foundation for open scientific discovery, offering a practical path from long-horizon task execution to collaborative, verifiable, and inspectable scientific exploration.
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Sage-A1.5-Q8_0-GGUF
InternScience> [!Note] > This repository contains **GGUF** model weights for Sage-A1.5, quantized to **Q8_0**, for deployment with llama.cpp, Ollama, LM Studio, and other GGUF-compatible runtimes. > > For the original Hugging Face Transformers weights (compatible with Transformers, vLLM, SGLang, etc.), see InternScience/Sage-A1.5. **Sage‑A1.5** is a 35B Mixture‑of‑Experts agentic model based on **Qwen3.6** and **Agents-A1** from Shanghai AI Laboratory, built to advance long-horizon scientific problem solving through adaptive capability composition. Building upon **Agents‑A1**, **Sage‑A1.5** learns not only to solve individual tasks, but also to organize work and revise its approach during execution, highlighting three key capabilities: **Agentic Orchestration, Verification, and Open Scientific Discovery**. First, agentic orchestration extends single-agent execution through task delegation and parallel collaboration among subagents with isolated contexts, enabling complex tasks to be decomposed and addressed concurrently while reducing interference across concurrent subagents. Second, agentic verification strengthens long-horizon reliability through progress checking, evidence validation, and reflective strategy revision, allowing **Sage‑A1.5** to recognize unproductive search paths and pursue alternative approaches. Third, open scientific discovery extends agentic capabilities beyond answering predefined questions to conducting exploratory research, connecting scientific inquiry with interactive analysis and inspectable research artifacts. We hope **Sage‑A1.5** provides the community with an accessible foundation for open scientific discovery, offering a practical path from long-horizon task execution to collaborative, verifiable, and inspectable scientific exploration.
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗internlm2-limarp-chat-20b-GGUF
intervitens-archive基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗e5-base
intfloat**News (May 2023): please switch to e5-base-v2, which has better performance and same method of usage.** Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset. Please refer to our paper at https://arxiv.org/pdf/2212.03533.pdf.
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗e5-base-unsupervised
intfloatText Embeddings by Weakly-Supervised Contrastive Pre-training. Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022 Here are some rules of thumb: - Use "query: " and "passage: " correspondingly for asymmetric tasks such as passage retrieval in open QA, ad-hoc information retrieval. Different versions of `transformers` and `pytorch` could cause negligible but non-zero performance differences.
Input Text→ Output Embedding
Price not verifiedCheck multimodal and channel pricing separately
View details ↗No matching models
Adjust provider, category or input/output filters. New models are added continuously.
COST EXPLORER
Great models. Clear costs.
Adjust monthly token usage to compare estimated API costs.
1 M = 1 million tokens · USD
Compares base text-token rates within the same channel
Compares base text-token rates within the same channel
Uses collected, comparable USD-per-million-token rates. Snapshots exclude cache discounts, tiers, extra reasoning output and taxes. Check current source prices.
MODEL HANDBOOK
Understand before you choose
How should API prices be compared?+
Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.
Do open weights mean free commercial use?+
Not necessarily. Check each version’s license, commercial restrictions and policies. Self-hosting also incurs compute and maintenance costs.
Is the top-ranked model the best for me?+
Rankings depend on tasks, versions and methods. Compare accuracy, latency, reliability and costs on your own workload.