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Chat Models — Cohere Family
| Model | Model ID | Tier | Parameters | Context | Multimodal | Reasoning | Tool Use | Fine-tunable | Status | Regions | Best For |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Cohere Command A Reasoning |
cohere.command-a-reasoning | ★ Flagship Reasoning | 111B | 256K | ✗ | ✓ Advanced | ✓ | ✗ | ● GA (Aug 2025) | US-ASH US-CHI US-PHX SA-SAO EU-FRA UK-LON ME-RUH ME-DXB AP-HYD AP-OSA | Complex Q&A, multi-step reasoning, document analysis, structured arguments |
| Cohere Command A Vision |
cohere.command-a-vision | ★ Flagship Multimodal | 112B | 128K | ✓ Images, Charts, Docs | ✓ | ✓ | ✗ | ● GA (Jul 2025) | US-ASH US-CHI US-PHX SA-SAO EU-FRA UK-LON ME-RUH ME-DXB AP-HYD AP-OSA | Enterprise document understanding with charts & images |
| Cohere Command A |
cohere.command-a-03-2025 | ◉ Flagship Chat | 111B | 256K | ✗ | — | ✓ Advanced | ✗ | ● GA (Mar 2025) | US-ASH US-CHI SA-SAO EU-FRA UK-LON ME-RUH ME-DXB AP-HYD AP-OSA | Agentic enterprise tasks, RAG, multilingual, high-throughput production |
| Cohere Command R+ 08-2024 |
cohere.command-r-plus-08-2024 | ◉ Advanced | 104B | 128K | ✗ | — | ✓ | ✗ | ● Active | US-ASH US-CHI SA-SAO EU-FRA UK-LON ME-RUH ME-DXB AP-OSA | Complex specialized tasks, Q&A, sentiment, multilingual RAG |
| Cohere Command R 08-2024 |
cohere.command-r-08-2024 | ▷ Standard | 35B | 128K | ✗ | — | ✓ | ✓ T-Few / Vanilla | ● Active | US-ASH US-CHI SA-SAO EU-FRA UK-LON ME-RUH AP-OSA | RAG pipelines, info retrieval, cost-efficient enterprise chat |
Chat Models — Google Gemini Family
| Model | Model ID | Tier | Parameters | Context | Multimodal | Reasoning | Tool Use | Fine-tunable | Status | Regions | Best For |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Google Gemini 2.5 Pro |
google.gemini-2.5-pro | ★ Flagship | — | 1M | ✓ Text, Image, Code, Audio, Video | ✓ Advanced Reasoning | ✓ | ✗ | ● GA | US-ASH US-CHI US-PHX EU-FRA AP-OSA | Most complex multimodal problems, large dataset analysis, SOTA reasoning tasks |
| Google Gemini 2.5 Flash |
google.gemini-2.5-flash | ◉ Balanced | — | 1M | ✓ Text, Image, Code, Audio, Video | ✓ Thinking features | ✓ | ✗ | ● GA | US-ASH US-CHI US-PHX EU-FRA AP-HYD AP-OSA | Balanced workloads needing speed + intelligence, complex applications |
| Google Gemini 2.5 Flash-Lite |
google.gemini-2.5-flash-lite | ○ Budget / Fast | — | 1M | ✓ Text, Image, Code, Audio, Video | — | ✓ | ✗ | ● GA | US-ASH US-CHI US-PHX EU-FRA | High-volume, simpler tasks; cost-sensitive production workloads |
Chat Models — Meta Llama Family
| Model | Model ID | Tier | Parameters | Context | Multimodal | Reasoning | Tool Use | Fine-tunable | Status | Regions | Best For |
|---|---|---|---|---|---|---|---|---|---|---|---|
Llama 4 Maverick |
meta.llama-4-maverick-17b-128e-instruct-fp8 | ★ Flagship MoE | ~400B | 512K | ✓ Text + Image | — | ✓ | ✗ | ● GA (2025) | US-CHI SA-SAO UK-LON ME-RUH AP-HYD AP-OSA | Multimodal understanding, multilingual, coding, agentic systems, large-scale inference |
Llama 4 Scout |
meta.llama-4-scout-17b-16e-instruct | ◉ Efficient MoE | ~109B | 192K | ✓ Text + Image | — | ✓ | ✗ | ● GA (2025) | US-CHI SA-SAO UK-LON ME-RUH AP-HYD AP-OSA | Smaller GPU deployments, efficient multimodal, multilingual, coding |
Llama 3.3 70B |
meta.llama-3.3-70b-instruct | ◉ Best Text 70B | 70B | 128K | ✗ | — | ✓ | ✓ LoRA | ● GA | US-CHI US-PHX SA-SAO EU-FRA UK-LON ME-RUH ME-DXB AP-HYD AP-OSA | Best text-only 70B tasks; outperforms 3.1 70B and 3.2 90B on text benchmarks |
Llama 3.2 90B Vision |
meta.llama-3.2-90b-vision-instruct | ◉ Vision Flagship | 90B | 128K | ✓ Text + Image | — | ✓ | ✗ | ● Active | US-CHI SA-SAO UK-LON ME-RUH AP-OSA | Multimodal understanding with large model capacity, image reasoning |
Llama 3.2 11B Vision |
meta.llama-3.2-11b-vision-instruct | ▷ Compact Vision | 11B | 128K | ✓ Text + Image | — | ✓ | ✗ | ● Active (Dedicated only) | US-CHI SA-SAO UK-LON AP-OSA | Cost-efficient multimodal; resource-constrained deployments |
Llama 3.1 405B |
meta.llama-3.1-405b-instruct | ★ Largest Open | 405B | 128K | ✗ | — | ✓ | ✗ | ● Active | US-CHI SA-SAO EU-FRA UK-LON AP-OSA | Highest text quality open model; complex reasoning, advanced generation |
Chat Models — OpenAI gpt-oss Family
| Model | Model ID | Tier | Parameters | Context | Multimodal | Reasoning | Tool Use | Fine-tunable | Status | Regions | Best For |
|---|---|---|---|---|---|---|---|---|---|---|---|
| OpenAI gpt-oss-120b |
openai.gpt-oss-120b | ★ Flagship OSS | 120B | 128K | ✗ | ✓ Advanced Reasoning + Agentic | ✓ Advanced Tool Use | ✗ | ● GA | US-ASH US-CHI US-PHX SA-SAO EU-FRA UK-LON ME-RUH ME-DXB AP-HYD AP-OSA | Reasoning, agentic tasks; outperforms similar-size open models; OpenAI-compatible API |
| OpenAI gpt-oss-20b |
openai.gpt-oss-20b | ▷ Efficient OSS | 20B | 128K | ✗ | ✓ Reasoning + Agentic | ✓ | ✗ | ● GA | US-ASH US-CHI US-PHX SA-SAO EU-FRA UK-LON ME-RUH ME-DXB AP-HYD AP-OSA | Efficient consumer-hardware-optimized reasoning; agentic tasks at lower cost |
Chat Models — xAI Grok Family
| Model | Model ID | Tier | Parameters | Context | Multimodal | Reasoning | Tool Use | Fine-tunable | Status | Regions | Best For |
|---|---|---|---|---|---|---|---|---|---|---|---|
| xAI Grok 4 |
xai.grok-4 | ★ Flagship | — | 128K | ✓ Text + Image | ✓ Advanced | ✓ | ✗ | ● GA | US-ASH US-CHI US-PHX | Advanced multimodal reasoning, enterprise data extraction, coding, summarization |
| xAI Grok 4 Fast |
xai.grok-4-fast-reasoning xai.grok-4-fast-non-reasoning |
▷ Fast Flagship | — | 2M | ✓ Text + Image | ✓ Reasoning + Non-Reasoning modes | ✓ | ✗ | ● GA | US-ASH US-CHI US-PHX | Same capability as Grok 4 with 2M context; cost-speed-optimized production |
| xAI Grok 4.1 Fast |
xai.grok-4-1-fast-reasoning xai.grok-4-1-fast-non-reasoning |
★ Agentic Flagship | — | 2M | ✓ Text + Image | ✓ Reasoning + Non-Reasoning modes | ✓ Parallel Tool Calling | ✗ | ● GA | US-ASH US-CHI US-PHX | Complex agentic systems, customer support, research with 2M multimodal context |
| xAI Grok 4.20 |
xai.grok-4.20-reasoning xai.grok-4.20-non-reasoning xai.grok-4.20-0309-reasoning xai.grok-4.20-0309-non-reasoning |
★ Latest Flagship | — | 2M | ✓ Text + Image | ✓ Reasoning + Non-Reasoning Variants | ✓ Advanced Agentic | ✗ | ● GA (Mar 2026) | US-ASH US-CHI US-PHX | Latest-gen multimodal agentic reasoning with 2M context and dual reasoning modes |
| xAI Grok 4.20 Multi-Agent |
xai.grok-4.20-multi-agent xai.grok-4.20-multi-agent-0309 |
◆ Multi-Agent Research | — | 2M | ✓ Text + Image | ✓ Orchestrated multi-agent reasoning | ✓ Multi-Agent Orchestration | ✗ | ● GA (Mar 2026) | US-ASH US-CHI US-PHX | Real-time multi-agent research — parallel web search, data analysis & synthesis by specialized sub-agents |
| xAI Grok 3 |
xai.grok-3 | ◉ Standard | — | 131K | ✗ | — | ✓ | ✗ | ● GA | US-ASH US-CHI US-PHX | General enterprise tasks, data extraction, text summarization |
| xAI Grok 3 Fast |
xai.grok-3-fast | ▷ Standard Fast | — | 131K | ✗ | — | ✓ | ✗ | ● GA | US-ASH US-CHI US-PHX | High-throughput enterprise tasks at Grok 3 quality level |
| xAI Grok 3 Mini |
xai.grok-3-mini | ○ Lightweight Thinker | — | 131K | ✗ | ✓ Traces exposed | ✓ | ✗ | ● GA | US-ASH US-CHI US-PHX | Logic-based tasks not requiring deep domain knowledge; transparent thinking traces |
| xAI Grok 3 Mini Fast |
xai.grok-3-mini-fast | ○ Lightweight Fast | — | 131K | ✗ | ✓ Traces exposed | ✓ | ✗ | ● GA | US-ASH US-CHI US-PHX | Low-latency logic tasks at minimum cost |
| xAI Grok Code Fast 1 |
xai.grok-code-fast-1 | ◆ Coding Specialist | — | 256K | ✗ | ✓ Summarized traces | ✓ Agentic Coding | ✗ | ● GA (Aug 2025) | US-ASH US-CHI US-PHX | TypeScript, Python, Java, Rust, C++, Go; zero-to-one projects, bug fixes, agentic coding loops |
Embedding Models
| Model Name | Model ID | Generation | Multimodal (Image) | Language Scope | Size Variant | Use Case | Status | Regions |
|---|---|---|---|---|---|---|---|---|
Embed 4 |
cohere.embed-v4.0 | Gen 4 · Latest | ✓ Text + Image (base64) | Multilingual | Full | Latest multimodal embeddings; text & image semantic search | ● Active | US-ASH US-CHI SA-SAO EU-FRA UK-LON ME-RUH ME-DXB AP-HYD AP-OSA |
Embed English Image 3 |
cohere.embed-english-image-v3.0 | Gen 3 | ✓ Text + Image | English | Full | English-only text+image semantic search | ● Active | US-ASH US-CHI SA-SAO EU-FRA UK-LON ME-DXB AP-OSA |
Embed English Light Image 3 |
cohere.embed-english-light-image-v3.0 | Gen 3 | ✓ Text + Image | English | Light | Cost-efficient English text+image embeddings | ● Active | US-ASH US-CHI SA-SAO EU-FRA UK-LON ME-DXB AP-OSA |
Embed Multilingual Image 3 |
cohere.embed-multilingual-image-v3.0 | Gen 3 | ✓ Text + Image | Multilingual | Full | Global multilingual text+image semantic search | ● Active | US-ASH US-CHI SA-SAO EU-FRA UK-LON ME-DXB AP-HYD AP-OSA |
Embed Multilingual Light Image 3 |
cohere.embed-multilingual-light-image-v3.0 | Gen 3 | ✓ Text + Image | Multilingual | Light | Budget multilingual text+image embeddings | ● Active | US-ASH US-CHI SA-SAO EU-FRA UK-LON ME-DXB AP-OSA |
Embed English 3 |
cohere.embed-english-v3.0 | Gen 3 | ✗ Text only | English | Full | Pure text English semantic search, classification, clustering | ● Active | US-CHI SA-SAO EU-FRA UK-LON |
Embed English Light 3 |
cohere.embed-english-light-v3.0 | Gen 3 | ✗ Text only | English | Light | Cost-efficient English text embeddings at scale | ● Active | US-CHI SA-SAO |
Embed Multilingual 3 |
cohere.embed-multilingual-v3.0 | Gen 3 | ✗ Text only | Multilingual | Full | Global enterprise text semantic search in 100+ languages | ● Active | US-ASH US-CHI US-PHX SA-SAO EU-FRA UK-LON AP-HYD AP-OSA |
Embed Multilingual Light 3 |
cohere.embed-multilingual-light-v3.0 | Gen 3 | ✗ Text only | Multilingual | Light | Affordable multilingual text embeddings at volume | ● Active | US-CHI SA-SAO |
Rerank Model
| Model Name | Model ID | Input | Output | Use Case | Status | Regions |
|---|---|---|---|---|---|---|
Rerank 3.5 |
cohere.rerank.v3-5 | Query + List of texts | Ordered array with relevance scores | RAG pipelines, document ranking, search result reordering, precision improvement | ● Active | US-ASH US-CHI SA-SAO EU-FRA UK-LON ME-RUH AP-OSA |
Imported Models
Available via OCI Generative AI Model Import — import open-weights models from HuggingFace into your own dedicated GPU cluster endpoint. 8 provider families · 82 models · supports fine-tuned variants within ±10% parameter count.
Alibaba Qwen Family
| Model Name | HuggingFace Model ID | Type | Params | Context | Cluster Shape |
|---|---|---|---|---|---|
| Alibaba QwQ-32B |
Qwen/QwQ-32B | Reasoning | 32B | 128K | A100_80G_X2 |
| Alibaba Qwen Image |
Qwen/Qwen-Image | Image Gen | — | — | A100_80G_X1 |
| Alibaba Qwen Image Edit |
Qwen/Qwen-Image-Edit | Image Gen | — | — | A100_80G_X1 |
| Alibaba Qwen Image 2512 |
Qwen/Qwen-Image-2512 | Image Gen | — | — | A100_80G_X1 |
| Alibaba Qwen Image Edit 2511 |
Qwen/Qwen-Image-Edit-2511 | Image Gen | — | — | A100_80G_X1 |
| Alibaba Qwen Image Edit 2509 |
Qwen/Qwen-Image-Edit-2509 | Image Gen | — | — | A100_80G_X1 |
| Alibaba Qwen3-Embedding-0.6B |
Qwen/Qwen3-Embedding-0.6B | Embed | 0.6B | 32K | A10_X1 |
| Alibaba Qwen3-Embedding-4B |
Qwen/Qwen3-Embedding-4B | Embed | 4B | 32K | A10_X2 |
| Alibaba Qwen3-Embedding-8B |
Qwen/Qwen3-Embedding-8B | Embed | 8B | 32K | A100_80G_X1 |
| Alibaba Qwen3-0.6B |
Qwen/Qwen3-0.6B | Chat | 0.6B | 32K | A100_80G_X1 |
| Alibaba Qwen3-1.7B |
Qwen/Qwen3-1.7B | Chat | 1.7B | 32K | A100_80G_X1 |
| Alibaba Qwen3-4B |
Qwen/Qwen3-4B | Chat | 4B | 32K | A100_80G_X1 |
| Alibaba Qwen3-8B |
Qwen/Qwen3-8B | Chat | 8B | 32K | A100_80G_X1 |
| Alibaba Qwen3-14B |
Qwen/Qwen3-14B | Chat | 14B | 32K | A100_80G_X1 |
| Alibaba Qwen3-32B |
Qwen/Qwen3-32B | Chat | 32B | 32K | A100_80G_X2 |
| Alibaba Qwen3-4B-Instruct-2507 |
Qwen/Qwen3-4B-Instruct-2507 | Chat | 4B | 32K | A100_80G_X1 |
| Alibaba Qwen3-30B-A3B-Instruct-2507 |
Qwen/Qwen3-30B-A3B-Instruct-2507 | Chat | 30B 3B active | 32K | A100_80G_X2 |
| Alibaba Qwen3-235B-A22B-Instruct-2507 |
Qwen/Qwen3-235B-A22B-Instruct-2507 | Chat | 235B 22B active | 32K | H100_X8 |
| Alibaba Qwen3-VL-30B-A3B-Instruct |
Qwen/Qwen3-VL-30B-A3B-Instruct | Vision | 30B 3B active | — | H100_X2 |
| Alibaba Qwen3-VL-235B-A22B-Instruct |
Qwen/Qwen3-VL-235B-A22B-Instruct | Vision | 235B 22B active | — | H100_X8 |
| Alibaba Qwen2.5-Coder-32B-Instruct |
Qwen/Qwen2.5-Coder-32B-Instruct | Coder | 32B | 128K | A100_80G_X2 |
| Alibaba Qwen2.5-0.5B-Instruct |
Qwen/Qwen2.5-0.5B-Instruct | Chat | 0.5B | 128K | A100_80G_X1 |
| Alibaba Qwen2.5-1.5B-Instruct |
Qwen/Qwen2.5-1.5B-Instruct | Chat | 1.5B | 128K | A100_80G_X1 |
| Alibaba Qwen2.5-3B-Instruct |
Qwen/Qwen2.5-3B-Instruct | Chat | 3B | 128K | A100_80G_X1 |
| Alibaba Qwen2.5-7B-Instruct |
Qwen/Qwen2.5-7B-Instruct | Chat | 7B | 128K | A100_80G_X1 |
| Alibaba Qwen2.5-14B-Instruct |
Qwen/Qwen2.5-14B-Instruct | Chat | 14B | 128K | A100_80G_X1 |
| Alibaba Qwen2.5-32B-Instruct |
Qwen/Qwen2.5-32B-Instruct | Chat | 32B | 128K | A100_80G_X2 |
| Alibaba Qwen2.5-72B-Instruct |
Qwen/Qwen2.5-72B-Instruct | Chat | 72B | 128K | A100_80G_X4 |
| Alibaba Qwen2.5-VL-3B-Instruct |
Qwen/Qwen2.5-VL-3B-Instruct | Vision | 3B | — | A100_80G_X1 |
| Alibaba Qwen2.5-VL-7B-Instruct |
Qwen/Qwen2.5-VL-7B-Instruct | Vision | 7B | — | A100_80G_X1 |
| Alibaba Qwen2.5-VL-32B-Instruct |
Qwen/Qwen2.5-VL-32B-Instruct | Vision | 32B | — | A100_80G_X2 |
| Alibaba Qwen2.5-VL-72B-Instruct |
Qwen/Qwen2.5-VL-72B-Instruct | Vision | 72B | — | A100_80G_X4 |
| Alibaba Qwen2-0.5B-Instruct |
Qwen/Qwen2-0.5B-Instruct | Chat | 0.5B | 32K | A100_80G_X1 |
| Alibaba Qwen2-1.5B-Instruct |
Qwen/Qwen2-1.5B-Instruct | Chat | 1.5B | 32K | A100_80G_X1 |
| Alibaba Qwen2-7B-Instruct |
Qwen/Qwen2-7B-Instruct | Chat | 7B | 128K | A100_80G_X1 |
| Alibaba Qwen2-72B-Instruct |
Qwen/Qwen2-72B-Instruct | Chat | 72B | 128K | A100_80G_X4 |
| Alibaba Qwen2-VL-2B-Instruct |
Qwen/Qwen2-VL-2B-Instruct | Vision | 2B | — | A100_80G_X1 |
| Alibaba Qwen2-VL-7B-Instruct |
Qwen/Qwen2-VL-7B-Instruct | Vision | 7B | — | A100_80G_X1 |
| Alibaba Qwen2-VL-72B-Instruct |
Qwen/Qwen2-VL-72B-Instruct | Vision | 72B | — | A100_80G_X4 |
DeepSeek Family
| Model Name | HuggingFace Model ID | Type | Params | Context | Cluster Shape |
|---|---|---|---|---|---|
| DeepSeek DeepSeek-R1-Distill-Qwen-32B |
deepseek-ai/DeepSeek-R1-Distill-Qwen-32B | Reasoning | 32B | 128K | A100_80G_X2 |
Google Gemma Family
| Model Name | HuggingFace Model ID | Type | Params | Context | Cluster Shape |
|---|---|---|---|---|---|
| Gemma Gemma 3 270M |
google/gemma-3-270m-it | Chat | 270M | 128K | A100_80G_X1 |
| Gemma Gemma 3 1B |
google/gemma-3-1b-it | Chat | 1B | 128K | A100_80G_X1 |
| Gemma Gemma 3 4B |
google/gemma-3-4b-it | Vision | 4B | 128K | A100_80G_X1 |
| Gemma Gemma 3 12B |
google/gemma-3-12b-it | Vision | 12B | 128K | A100_80G_X1 |
| Gemma Gemma 3 27B |
google/gemma-3-27b-it | Vision | 27B | 128K | A100_80G_X2 |
| Gemma Gemma 2 2B |
google/gemma-2-2b-it | Chat | 2B | 8K | A100_80G_X1 |
| Gemma Gemma 2 9B |
google/gemma-2-9b-it | Chat | 9B | 8K | A100_80G_X1 |
| Gemma Gemma 2 27B |
google/gemma-2-27b-it | Chat | 27B | 8K | A100_80G_X2 |
Meta Llama Family
| Model Name | HuggingFace Model ID | Type | Params | Context | Cluster Shape |
|---|---|---|---|---|---|
Llama 4 Maverick 17B |
meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8 | Vision | 17B×128E | 1M | H100_X8 |
Llama 4 Scout 17B |
meta-llama/Llama-4-Scout-17B-16E-Instruct | Vision | 17B×16E | 1M | H100_X4 |
Llama 3.3 70B Instruct |
meta-llama/Llama-3.3-70B-Instruct | Chat | 70B | 128K | A100_80G_X4 |
Llama 3.2 3B Instruct |
meta-llama/Llama-3.2-3B-Instruct | Chat | 3B | 128K | A100_80G_X1 |
Llama 3.2 1B Instruct |
meta-llama/Llama-3.2-1B-Instruct | Chat | 1B | 128K | A100_80G_X1 |
Llama 3.1 8B Instruct |
meta-llama/Llama-3.1-8B-Instruct | Chat | 8B | 128K | A100_80G_X1 |
Llama 3 8B Instruct |
meta-llama/Meta-Llama-3-8B-Instruct | Chat | 8B | 8K | A100_80G_X1 |
Llama 3 70B Instruct |
meta-llama/Meta-Llama-3-70B-Instruct | Chat | 70B | 8K | A100_80G_X4 |
Llama 2 70B Chat |
meta-llama/Llama-2-70b-chat-hf | Chat | 70B | 4K | A100_80G_X4 |
Llama 2 13B Chat |
meta-llama/Llama-2-13b-chat-hf | Chat | 13B | 4K | A100_80G_X1 |
Llama 2 7B Chat |
meta-llama/Llama-2-7b-chat-hf | Chat | 7B | 4K | A100_80G_X1 |
Microsoft Phi Family
| Model Name | HuggingFace Model ID | Type | Params | Context | Cluster Shape |
|---|---|---|---|---|---|
| Microsoft Phi-4 |
microsoft/phi-4 | Chat | 14B | 16K | A100_80G_X1 |
| Microsoft Phi-3 Vision 128K |
microsoft/Phi-3-vision-128k-instruct | Vision | 4.2B | 128K | H100_X1 |
| Microsoft Phi-3 Medium 128K |
microsoft/Phi-3-medium-128k-instruct | Chat | 14B | 128K | A100_80G_X1 |
| Microsoft Phi-3 Medium 4K |
microsoft/Phi-3-medium-4k-instruct | Chat | 14B | 4K | A100_80G_X1 |
| Microsoft Phi-3 Small 128K |
microsoft/Phi-3-small-128k-instruct | Chat | 7B | 128K | A100_80G_X1 |
| Microsoft Phi-3 Small 8K |
microsoft/Phi-3-small-8k-instruct | Chat | 7B | 8K | A100_80G_X1 |
| Microsoft Phi-3 Mini 128K |
microsoft/Phi-3-mini-128k-instruct | Chat | 3.8B | 128K | A100_80G_X1 |
| Microsoft Phi-3 Mini 4K |
microsoft/Phi-3-mini-4k-instruct | Chat | 3.8B | 4K | A100_80G_X1 |
Mistral Family
| Model Name | HuggingFace Model ID | Type | Params | Context | Cluster Shape |
|---|---|---|---|---|---|
| Mistral Mixtral 8x7B Instruct v0.1 |
mistralai/Mixtral-8x7B-Instruct-v0.1 | Chat | 8×7B MoE | 32K | A100_80G_X2 |
| Mistral Mistral Nemo Instruct 2407 |
mistralai/Mistral-Nemo-Instruct-2407 | Chat | 12B | 128K | A100_80G_X1 |
| Mistral Mistral 7B Instruct v0.3 |
mistralai/Mistral-7B-Instruct-v0.3 | Chat | 7B | 32K | A100_80G_X1 |
| Mistral Mistral 7B Instruct v0.2 |
mistralai/Mistral-7B-Instruct-v0.2 | Chat | 7B | 32K | A100_80G_X1 |
| Mistral Mistral 7B Instruct v0.1 |
mistralai/Mistral-7B-Instruct-v0.1 | Chat | 7B | 8K | A100_80G_X1 |
| Mistral E5 Mistral 7B Instruct |
intfloat/e5-mistral-7b-instruct | Embed | 7B | 32K | A10_X1 |
NVIDIA Nemotron Family
| Model Name | HuggingFace Model ID | Type | Params | Context | Cluster Shape |
|---|---|---|---|---|---|
| NVIDIA Nemotron 3 Super 120B |
nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16 | Chat | 120B 12B active | 1M | H100_X8 |
| NVIDIA Nemotron 3 Nano 30B (FP8) |
nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-FP8 | Chat | 30B 3B active | 1M | H100_X4 |
| NVIDIA Nemotron 3 Nano 30B (BF16) |
nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 | Chat | 30B 3B active | 1M | A100_80G_X1 |
| NVIDIA Llama 3.1 Nemotron 70B Instruct |
nvidia/Llama-3.1-Nemotron-70B-Instruct-HF | Chat | 70B | 128K | A100_80G_X4 |
OpenAI GptOss Family
| Model Name | HuggingFace Model ID | Type | Params | Context | Cluster Shape |
|---|---|---|---|---|---|
| OpenAI GptOss 20B |
openai/gpt-oss-20b | Chat | 20B | 128K | H100_X1 |
| OpenAI GptOss 120B |
openai/gpt-oss-120b | Chat | 120B | 128K | H100_X2 |
Use-Case Selection Guide
🔍 RAG / Document Search
Cohere Command A Reasoning
256K context, built for RAG, advanced reasoning over documents
Cohere Command R 08-2024
RAG-optimized, 128K context, fine-tunable, cost-efficient
Google Gemini 2.5 Pro
1M context — handle entire documents in one pass
🤖 Agentic / Tool-Use Workflows
xAI Grok 4.20 Multi-Agent
Real-time multi-agent research: parallel specialist orchestration for web search, analysis & synthesis
xAI Grok 4.1 Fast
2M context, parallel tool calls, 3× fewer hallucinations
Cohere Command A
256K context, best throughput for Cohere agentic tasks
💻 Code Generation
xAI Grok Code Fast 1
Specialized coding model — plan, write, test, debug loop
Meta Llama 4 Maverick
MoE, strong coding + tool-calling capabilities
Google Gemini 2.5 Pro
Top-tier code reasoning, debugging, complex architecture
🌍 Multimodal (Text + Image)
Google Gemini 2.5 Pro
Best multimodal — text, image, code, audio, video
Cohere Command A Vision
Enterprise-focused image, chart, document understanding
Meta Llama 4 Maverick
Open-weight multimodal with MoE efficiency
⚡ High-Volume / Low-Latency
Google Gemini 2.5 Flash-Lite
Fastest + cheapest in Gemini family; 1M context
xAI Grok 3 Mini Fast
Lightweight thinker, lowest latency xAI model
OpenAI gpt-oss-20b
Consumer-grade hardware optimized, fast reasoning
🏢 Enterprise Fine-Tuning
Cohere Command R 08-2024
T-Few + Vanilla fine-tuning on dedicated AI clusters
Meta Llama 3.3 70B
LoRA fine-tuning, best 70B text performance
🌐 Multilingual Applications
Cohere Command A
Native multilingual support, 256K context, high-throughput production
Meta Llama 4 Scout / Maverick
Open-weight multilingual models with strong cross-language performance
Cohere Embed Multilingual 3 / Embed 4
Semantic search in 100+ languages; Gen 4 adds multimodal
📄 Long-context Document Analysis
xAI Grok 4.1 Fast
2M tokens — process full codebases or entire document archives in one pass
Google Gemini 2.5 Pro
1M context with multimodal; ideal for large PDFs, reports, mixed media
xAI Grok 4 Fast
2M context at optimized cost; great for batch document workloads
🇪🇺 EU Data Sovereignty (Frankfurt · London)
On-demand + Dedicated — EU-FRA & UK-LON
Cohere Command A
256K context, multilingual, RAG + agentic — widest EU access
Meta Llama 3.3 70B
Open-weight, LoRA fine-tunable, strong text performance
OpenAI gpt-oss-120b / 20b
OpenAI-compatible API, advanced reasoning — Frankfurt on-demand + dedicated
Dedicated Only — EU-FRA & UK-LON
Cohere Command A Reasoning
Advanced reasoning over documents — tenancy-exclusive GPUs in both EU regions
Meta Llama 4 Maverick / Scout
Latest Llama 4 multimodal models — UK-LON dedicated only
Cohere Embed 3 / 4 + Rerank 3.5
Semantic search & reranking with EU data residency
On-demand only (ext.) — EU-FRA
Google Gemini 2.5 Pro / Flash / Flash-Lite
External API routed through EU-FRA endpoint — data does not reside on OCI hardware
xAI Grok — No EU Presence
All xAI Grok models are US-only (Ashburn, Chicago, Phoenix) — not suitable for EU data residency requirements
🏗️ Dedicated AI Clusters
Required for Fine-Tuning
Fine-tuning jobs run exclusively on dedicated GPU clusters — Cohere T-Few/Vanilla and Meta LoRA cannot run on-demand
Data Residency & Compliance
Tenancy-exclusive GPUs; your data never shares hardware — suited for regulated industries (GDPR, HIPAA, financial)
Not Available — Google & xAI
Google Gemini and xAI Grok route through external APIs — dedicated clusters are not supported for these providers
⚡ On-demand (No Cluster Needed)
Google Gemini & xAI Grok — Always On-demand
External API call; no cluster provisioning, instant availability, pay-per-token billing
Cohere & OpenAI gpt-oss — On-demand in Select Regions
On-demand access available without a dedicated cluster — ideal for PoC and variable workloads
Fine-Tuning Requires Dedicated
On-demand mode supports inference only — to fine-tune a model you must provision a dedicated AI cluster first
LEGEND
★ Flagship / Best-in-class
◉ Balanced / Advanced
▷ Speed / Efficiency tier
○ Lightweight / Budget
◆ Specialized
2M/1M Context ≥ 1M tokens
256K Context = 192K–512K tokens
128K Context = 128K tokens
✓ Feature supported
✗ Not supported
MoE Mixture of Experts (sparse activation)
US-CHI Region: on-demand + dedicated
EU-FRA Region: dedicated AI clusters only
US-ASH Region: on-demand / external call only
¹ Parameter counts are shown only when officially disclosed by the provider. Proprietary models (Google Gemini, xAI Grok) do not publish parameter counts and are omitted. "—" means not publicly disclosed.
² Fine-tuning on OCI uses dedicated AI clusters (GPU resources belonging exclusively to your tenancy). Cohere supports T-Few & Vanilla strategies; Meta Llama supports LoRA.
³ Retired models no longer listed in the current OCI pretrained-model catalog are omitted from the main tables; deprecated models still listed by OCI are shown with deprecated status.
⁴ Model Import feature (GA 2025) lets you bring your own LLMs from Hugging Face or OCI Object Storage.
⁶ OCI documents Grok 4 at 128K context, Grok 4.3 at 1M context, and Grok 4 Fast, Grok 4.1 Fast, Grok 4.20, and Grok 4.20 Multi-Agent at 2M context.
⁵ Data sources (OCI Official Documentation; catalog last updated 17 May 2026; OC1 commercial regions only): Pretrained Models · Models by Region · Inferencing Modes · Model Import
⚠ Unofficial reference — not an official Oracle document. This page was assembled from OCI public documentation. Data may contain errors or be out of date. Always verify against docs.oracle.com before making production decisions.