Text & Chat Models

The original Bio_ClinicalBERT from Alsentzer et al., a BERT model initialized from BioBERT and further pretrained on all MIMIC-III clinical notes. Served as a fill-mask endpoint it predicts masked tokens in clinical text and produces clinical embeddings. It is the standard encoder backbone behind many downstream clinical NLP fine-tunes.

Token-classification model from d4data that tags 84 biomedical entity types in clinical and medical text, including disease, sign, symptom, medication, dosage, lab value, body part and procedure. Trained on the Maccrobat clinical case corpus on a DistilBERT base, so it runs cheaply for high-volume tagging.

Anthropic's most powerful model. Exceptional at complex analysis, agentic tasks, and extended reasoning.

Anthropic's most capable Opus-tier model. State of the art on long-horizon agentic work, coding and knowledge tasks, with a 1M-token context window at standard pricing.

Anthropic's most capable model. Excellent for complex analysis, coding, math, and creative writing.

DeepSeek's refreshed V3.1 release. 671B MoE / 37B active. Tops open-weights leaderboards on coding and reasoning.

DeepSeek's April 2026 flagship. 1.6T MoE / 49B active params, 1M context, rivals top closed-source models on STEM and coding at a fraction of the price.

Google's fastest multimodal model. Supports text, images, audio, and video input.

Google's latest thinking model. Excels at reasoning, coding, math, and science with massive context window.

OpenAI's newest flagship model. Improved reasoning, instruction following, and coding over GPT-4o.

OpenAI's most capable multimodal model. Excellent for complex reasoning, coding, and creative tasks.

OpenAI's current flagship chat model (released April 2026). Strongest general reasoning, coding and tool use in the GPT-5 line, with vision input and a large context window.

xAI's flagship reasoning model with vision and tool use. 256k context, strong at complex reasoning and STEM tasks.

xAI's Grok 4.20 reasoning snapshot. Runs an extended thinking pass before answering for multi-step analysis, math and STEM, with a 1M token context window and strong agentic tool calling.

Moonshot AI's 1T-parameter MoE model. Industry-leading agentic coding and tool-use benchmarks.

Token-classification model that extracts 41 medical entity types from clinical text, such as disease, medication, dosage, frequency, lab test, sign and symptom. Fine-tuned on a DeBERTa v3 base, which gives more accurate spans than older BERT-based taggers on the same corpus.

MiniMax's 456B hybrid lightning-attention model with native 4M-token context. Industry-leading long-context.

OpenAI's reasoning model optimized for STEM tasks, coding, and math. Uses chain-of-thought reasoning.

Perplexity's premium web-grounded search model with multi-step reasoning over live sources.

AI21's flagship hybrid Mamba-Transformer model with a 256k context window for long-document tasks.

Cost-efficient hybrid Mamba-Transformer model with 256k context. Tuned for high-throughput RAG.

BioBERT fine-tuned on the NCBI Disease corpus for disease-name recognition. Given biomedical text it tags spans that mention a disease or condition, using BIO labels. Useful for pulling diagnoses and disease mentions out of abstracts, case reports and clinical notes.

Anthropic's fast and affordable model. Great for quick tasks, summarization, and simple coding.

Text-classification model from Betty van Aken that decides whether a medical condition mentioned in a clinical note is present, absent or possible. The target entity is marked in the input with [entity] tags, and the model returns the assertion status. Built on Bio_ClinicalBERT and trained on the 2010 i2b2/VA assertion data.

Token-classification model that tags the three core i2b2 clinical entity types in patient notes: problem, test and treatment. Given a sentence from a discharge summary or progress note it marks which spans are medical problems, which are diagnostic tests and which are treatments or medications.

Open-weights multilingual research model from Cohere covering 23 languages. 35B parameters.

Cohere's fast lightweight chat model (deprecated Sep 2025). Kept as comparison tombstone.

Cohere's mid-tier RAG/tool model. Cost-efficient sibling of Command R+ with 128k context.

Cohere's flagship RAG- and tool-optimized chat model. 128k context, refreshed August 2024.

DeepSeek's reasoning model with chain-of-thought capabilities. Excellent for complex problem-solving.

Powerful open-weight model from DeepSeek. Strong at coding, math, and Chinese/English tasks.

Efficiency-optimized variant of DeepSeek V4. 284B MoE / 13B active, 1M context, ultra-low pricing for high-throughput workloads.

Small, fast, and affordable model for lightweight tasks. Great balance of speed and capability.

Smaller, faster, cheaper member of OpenAI's GPT-5 family. Tuned for high-throughput chat, classification and extraction where the full flagship is overkill.

OpenAI GPT-5.1 chat model (November 2025). An earlier GPT-5 point release kept available for compatibility. Good general-purpose reasoning and coding.

xAI's flagship model. Strong at reasoning, coding, and real-time knowledge with web search capabilities.

xAI's Grok 4.20 standard snapshot. Skips the extended thinking pass for lower-latency answers on tasks that do not need deep deliberation. 1M token context window.

xAI's Grok 4.20 multi-agent snapshot. Coordinates several specialized agents under one call to handle multi-step workflows that mix research, tool use and synthesis. 1M token context window.

Meta's open-source 70B parameter model. Strong all-around performance with multilingual support.

Mistral's flagship model. Strong reasoning, multilingual, and coding capabilities.

OpenAI's o3 reasoning model. Spends compute on a private chain of thought before answering, strong at math, science and hard coding problems that benefit from deliberate reasoning.

OpenAI's o4-mini reasoning model. A cost-efficient reasoning model that trades some depth for much lower price and latency, good for high-volume math and code tasks.

Perplexity's fastest and cheapest web-grounded chat model. Live-source citations included.

Perplexity's reasoning model with chain-of-thought and integrated web search.

Alibaba's powerful open-source model. Excellent at coding, math, and multilingual tasks.

Alibaba's flagship pretrained MoE model. Top-tier reasoning and code performance via DashScope API.

Meta SeamlessM4T v2 Large. Universal multilingual translation across 100+ languages with text-to-text mode for documents and chat.

Snowflake's open MoE model: 480B total / 17B active params with dense+MoE hybrid architecture.

01.AI's larger general-purpose chat model with 32k context window and strong bilingual performance.