> ## Documentation Index > Fetch the complete documentation index at: https://docs.altrum.ai/llms.txt > Use this file to discover all available pages before exploring further. # Multi-Model > Seamlessly switch between and combine different AI models, enabling flexible, robust, and guardrail driven AI workflows. ## Overview The Multi-Model feature provides access to **55+ AI models** across 7 major providers. This comprehensive model library includes various model sizes, capabilities, and price points, enabling organisations to select the optimal model for each use case while maintaining API consistency. ### Key Capabilities From lightweight to state-of-the-art models across all AI providers. Built-in controls ensure only supported models are used ensuring consistent AI output quality. Automatic parameter adjustments based on model capabilities to ensure optimal performance. Real-time cost calculation and monitoring for all supported models in AI Gateway. *** ## Business Benefits ### 1. Best of Breed Model Selection * **Task-Optimised Performance**\ Choose the ideal model for each specific use case — *GPT-4o for complex reasoning, Claude for long-context analysis, Gemini for multi-modal tasks etc.*. * **Cost-Performance Optimisation**\ Select cost effective models for simple tasks (*e.g., GPT-4o-mini, Claude Haiku etc.*) and premium models for complex operations. * **Competitive Advantage**\ Leverage unique capabilities of different models to outperform competitors using single model approaches. * **Innovation Velocity**\ Immediately access new models as they are released without infrastructure changes. *** ### 2. Risk Mitigation & Reliability * **Model Diversification**\ Avoid dependency on a single model's availability, performance or pricing changes. * **Automatic Failover**\ Seamlessly switch to alternative models during outages or degraded performance. * **Compliance Flexibility**\ Use region specific or compliance certified models (*Azure AI, AWS Bedrock, Google AI etc.*) for regulated workloads. * **Quality Assurance** A/B test different models to ensure consistent quality across providers. *** ### 3. Cost Management & Optimisation * **Dynamic Cost Control**\ Route requests to cheaper models based on complexity and budget constraints. * **Volume Discounts**\ Leverage pricing tiers across multiple providers simultaneously. * **Budget Allocation**\ Set model specific budgets and automatically switch when limits are reached. * **ROI Maximisation**\ Use premium models only where their advanced capabilities justify the cost. *** ### 4. Enterprise Scalability * **Load Distribution**\ Distribute high volume workloads across multiple models to avoid rate limits. * **Geographic Optimisation**\ Use region specific models for lower latency and data residency compliance. * **Capacity Management**\ Access combined capacity of all providers during peak demand. * **Performance Benchmarking**\ Compare model performance in production with real workloads. *** ## Supported Models by Provider ### OpenAI Models (20 Models) | **Model** | **Description** | **Release Date** | **Modalities** | **Context Window** | **Max Output Tokens** | **Knowledge Cut-Off** | | ---------------------------------------- | ----------------------------------------------------------------------------------------- | ---------------- | -------------- | ------------------ | --------------------- | --------------------- | | GPT-5 Chat (`gpt-5-chat-latest`) | Snapshot used in ChatGPT. Recommended for testing latest improvements in chat use cases. | Latest | Text, Image | 128,000 | 16,384 | Sep 30, 2024 | | GPT-5 (`gpt-5-2025-08-07`) | Flagship model for coding, reasoning, and agentic tasks across domains. | 2025-08-07 | Text, Image | 400,000 | 128,000 | Sep 30, 2024 | | GPT-5 Mini (`gpt-5-mini-2025-08-07`) | Faster, more cost-efficient GPT-5 variant for well-defined tasks and precise prompts. | 2025-08-07 | Text, Image | 400,000 | 128,000 | May 31, 2024 | | GPT-5 Nano (`gpt-5-nano-2025-08-07`) | Cheapest, fastest GPT-5 variant. Ideal for summarization and classification. | 2025-08-07 | Text, Image | 400,000 | 128,000 | May 31, 2024 | | GPT-4.1 (`gpt-4.1-2025-04-14`) | Excels at instruction following and tool use. Supports 1M token context with low latency. | 2025-04-14 | Text, Image | 1,047,576 | 32,768 | Jun 01, 2024 | | GPT-4.1 Mini (`gpt-4.1-mini-2025-04-14`) | Smaller, faster GPT-4.1 variant. Maintains broad capabilities with 1M token context. | 2025-04-14 | Text, Image | 1,047,576 | 32,768 | Jun 01, 2024 | | GPT-4.1 Nano (`gpt-4.1-nano-2025-04-14`) | Ultra-light GPT-4.1 variant for efficiency with 1M token context. | 2025-04-14 | Text, Image | 1,047,576 | 32,768 | Jun 01, 2024 | | GPT-4 Preview (`gpt-4-0125-preview`) | Research preview of GPT-4 Turbo, an older high-intelligence model. | 2024-01-25 | Text | 128,000 | 4,096 | Dec 01, 2023 | | GPT-4 Legacy (`gpt-4-0613`) | Older GPT-4 model, still available for compatibility. | 2023-06-13 | Text | 8,192 | 8,192 | Dec 01, 2023 | | GPT-4 Turbo (`gpt-4-turbo-2024-04-09`) | Cheaper, faster variant of GPT-4. Superseded by GPT-4o. | 2024-04-09 | Text, Image | 128,000 | 4,096 | Dec 01, 2023 | | GPT-4o (`gpt-4o-2024-05-13`) | Versatile, high-intelligence flagship model. Multimodal (text + image). | 2024-05-13 | Text, Image | 128,000 | 4,096 | Oct 01, 2023 | | GPT-4o (`gpt-4o-2024-08-06`) | Updated GPT-4o snapshot. | 2024-08-06 | Text, Image | 128,000 | 16,384 | Oct 01, 2023 | | GPT-4o (`gpt-4o-2024-11-20`) | Updated GPT-4o snapshot. | 2024-11-20 | Text, Image | 128,000 | 16,384 | Oct 01, 2023 | | GPT-4o Latest (`chatgpt-4o-latest`) | Points to the GPT-4o snapshot used in ChatGPT. | Rolling | Text, Image | 128,000 | 16,384 | Oct 01, 2023 | | GPT-4o Mini (`gpt-4o-mini-2024-07-18`) | Lightweight GPT-4o variant. Fast, affordable, and fine-tuning friendly. | 2024-07-18 | Text, Image | 128,000 | 16,384 | Oct 01, 2023 | | O1 (`o1-2024-12-17`) | RL-trained reasoning model. Thinks step-by-step before answering. | 2024-12-17 | Text, Image | 200,000 | 100,000 | Oct 01, 2023 | | O3 (`o3-2025-04-16`) | High-performance reasoning model for math, science, coding, and multimodal analysis. | 2025-04-16 | Text, Image | 200,000 | 100,000 | Jun 01, 2024 | | O3 Mini (`o3-mini-2025-01-31`) | Small reasoning model. Supports structured outputs, function calling, and batch API. | 2025-01-31 | Text | 200,000 | 100,000 | Oct 01, 2023 | | O4 Mini (`o4-mini-2025-04-16`) | Latest small o-series model. Optimized for fast reasoning, coding, and visual tasks. | 2025-04-16 | Text, Image | 200,000 | 100,000 | Jun 01, 2024 | | GPT-3.5 Turbo (`gpt-3.5-turbo-0125`) | Legacy GPT-3.5 model. Still supported, but GPT-4o Mini is recommended instead. | 2024-01-25 | Text | 16,385 | 4,096 | Sep 01, 2021 | *** ### Anthropic Claude Models (7 Models) | **Model** | **Description** | **Release Date** | **Modalities** | **Context Window** | **Max Output Tokens** | **Knowledge Cut-Off** | | ----------------------------------------------------------------------------------- | ---------------------------------------------------- | ---------------- | ---------------------------------- | ------------------ | --------------------- | -------------------------------------------- | | Claude Sonnet 4.5 (`claude-sonnet-4-5-20250929`) | Best model for complex agents and coding. | 2025-09-29 | Text, Image (Vision), Multilingual | 200K / 1M (beta) | 64,000 | Reliable: Jan 2025 · Training data: Jul 2025 | | Claude Sonnet 4 (`claude-sonnet-4-20250514`) | High-performance model. | 2025-05-14 | Text, Image (Vision), Multilingual | 200K / 1M (beta) | 64,000 | Reliable: Jan 2025 · Training data: Mar 2025 | | Claude Sonnet 3.7 (`claude-3-7-sonnet-20250219`, alias: `claude-3-7-sonnet-latest`) | High-performance model with early extended thinking. | 2025-02-19 | Text, Image (Vision), Multilingual | 200K | 64,000 | Reliable: Oct 2024 · Training data: Nov 2024 | | Claude Opus 4.1 (`claude-opus-4-1-20250805`) | Exceptional model for specialized complex tasks. | 2025-08-05 | Text, Image (Vision), Multilingual | 200K | 32,000 | Reliable: Jan 2025 · Training data: Mar 2025 | | Claude Opus 4 (`claude-opus-4-20250514`) | Previous flagship model. | 2025-05-14 | Text, Image (Vision), Multilingual | 200K | 32,000 | Reliable: Jan 2025 · Training data: Mar 2025 | | Claude Haiku 3.5 (`claude-3-5-haiku-20241022`, alias: `claude-3-5-haiku-latest`) | Fastest Claude model. | 2024-10-22 | Text, Image (Vision), Multilingual | 200K | 8,192 | Reliable: Jul 2024 · Training data: Jul 2024 | | Claude Haiku 3 (`claude-3-haiku-20240307`) | Compact model for near-instant responsiveness. | 2024-03-07 | Text, Image (Vision), Multilingual | 200K | 4,096 | Reliable: 2023 · Training data: Aug 2023 | *** ### Amazon Bedrock Models (18 Models) | **Model** | **Description** | **Release Date** | **Modalities** | **Context Window** | **Max Output Tokens** | **Knowledge Cut-Off** | | ------------------------------------------------------------------ | --------------------------------------------------------------- | ---------------- | -------------------------- | ------------------ | --------------------- | --------------------- | | Claude Sonnet 4.5 (`anthropic.claude-sonnet-4-5-20250929-v1:0`) | Latest Claude Sonnet reasoning/chat model. | 2025-09-29 | Text, Image | 200K | – | – | | Claude Sonnet 4 (`anthropic.claude-sonnet-4-20250514-v1:0`) | Advanced Claude Sonnet v4. | 2025-05-14 | Text, Image | 200K | – | – | | Claude Sonnet 3.7 (`anthropic.claude-3-7-sonnet-20250219-v1:0`) | Claude 3.7 Sonnet generation model. | 2025-02-19 | Text, Image | 200K | – | – | | Claude Sonnet 3.5 v2 (`anthropic.claude-3-5-sonnet-20241022-v2:0`) | Updated Claude 3.5 Sonnet. | 2024-10-22 | Text, Image | 200K | – | – | | Claude Sonnet 3.5 (`anthropic.claude-3-5-sonnet-20240620-v1:0`) | Standard Claude 3.5 Sonnet. | 2024-06-20 | Text, Image | 200K | – | – | | Claude Haiku 3 (`anthropic.claude-3-haiku-20240307-v1:0`) | Lightweight Claude model optimized for speed/cost. | 2024-03-07 | Text | 48K | – | – | | Claude Sonnet 3 (`anthropic.claude-3-sonnet-20240229-v1:0`) | Claude 3 Sonnet general-purpose model. | 2024-02-29 | Text, Image | 28K | – | – | | Nova Lite (`amazon.nova-lite-v1:0`) | Amazon Nova lightweight model. | 2025 | Text | 300K | – | – | | Nova Micro (`amazon.nova-micro-v1:0`) | Amazon Nova smallest variant. | 2025 | Text | 128K | – | – | | Nova Pro (`amazon.nova-pro-v1:0`) | Amazon Nova flagship model. | 2025 | Text | 300K | – | – | | Titan Text G1 – Express (`amazon.titan-text-express-v1`) | Balanced Titan LLM for text generation. | 2023 | Text | 8K | – | – | | Titan Text G1 – Lite (`amazon.titan-text-lite-v1`) | Lightweight Titan model. | 2023 | Text | 4K | – | – | | IBM Granite 3.2 Instruct 8B (`ibm-granite-3-2-8b-instruct`) | General-purpose instruct model. | 2025 | Text | – | – | – | | IBM Granite 3.0 Instruct 8B (`granite-3-0-8b-instruct`) | Earlier instruct model (8B params). | 2024 | Text | – | – | – | | IBM Granite 20B Code Instruct (`ibm-granite-20b-code-instruct-8k`) | Code-focused model (20B params). | 2024 | Text (Code) | 8K | – | – | | IBM Granite 8B Code Instruct (`ibm-granite-8b-code-instruct-128k`) | Code instruct model with extended context. | 2024 | Text (Code) | 128K | – | – | | IBM Granite 34B Code Instruct (`ibm-granite-34b-code-instruct-8k`) | Large code instruct model (34B params). | 2024 | Text (Code) | 8K | – | – | | Llama 3 8B Instruct (`meta.llama3-8b-instruct-v1:0`) | Meta Llama 3 instruct-tuned model. | 2024 | Text | 8K | – | – | | Llama 3 70B Instruct (`meta.llama3-70b-instruct-v1:0`) | Larger Meta Llama 3 instruct model. | 2024 | Text | 8K | – | – | | DeepSeek-R1 (`deepseek-llm-r1`) | DeepSeek foundation model. | 2025 | Text | – | – | – | | DeepSeek V3.1 (`deepseek.v3-v1:0`) | Latest DeepSeek v3.1 model. | 2025 | Text | 163,840 | – | – | | Mistral 7B Instruct (`mistral.mistral-7b-instruct-v0:2`) | Instruction-tuned Mistral 7B. | 2024-03-01 | Text, Code, Classification | 32K | – | – | | Mistral Large 24.02 (`mistral.mistral-large-2402-v1:0`) | Large Mistral model for reasoning, text, code, RAG, and agents. | 2024-04-02 | Text, Code, RAG, Agents | 32K | – | – | | Mixtral 8x7B Instruct (`mistral.mixtral-8x7b-instruct-v0:1`) | Mixture-of-experts instruct model. | 2024-03-01 | Text, Code, Reasoning | 32K | – | – | *** ### Azure OpenAI Models | **Model** | **Description** | **Release Date** | **Modalities** | **Context Window** | **Max Output Tokens** | **Knowledge Cut-Off** | | ----------------------------------------------------- | ---------------------------------------------------------------------------------------------- | ---------------- | -------------- | ----------------------------------------- | --------------------- | --------------------- | | GPT-5 (`gpt-5-2025-08-07`) | Flagship GPT-5 with reasoning, structured outputs, text + image processing, functions & tools. | 2025-08-07 | Text, Image | 400,000 (272K in / 128K out) | 128,000 | Sep 30, 2024 | | GPT-5 Mini (`gpt-5-mini-2025-08-07`) | Smaller, faster GPT-5 variant. | 2025-08-07 | Text, Image | 400,000 (272K in / 128K out) | 128,000 | May 31, 2024 | | GPT-5 Nano (`gpt-5-nano-2025-08-07`) | Optimized GPT-5 variant with smaller footprint. | 2025-08-07 | Text, Image | 400,000 (272K in / 128K out) | 128,000 | May 31, 2024 | | GPT-5 Chat Preview (`gpt-5-chat-2025-08-07`) | Chat-optimized GPT-5 (preview). | 2025-08-07 | Text, Image | 128,000 | 16,384 | Sep 30, 2024 | | GPT-5 Chat Preview (`gpt-5-chat-2025-10-03`) | Updated chat-optimized GPT-5 (preview). | 2025-10-03 | Text, Image | 128,000 | 16,384 | Sep 30, 2024 | | GPT-5 Codex (`gpt-5-codex-2025-09-11`) | GPT-5 optimized for coding and structured outputs. | 2025-09-11 | Text, Image | 400,000 (272K in / 128K out) | 128,000 | – | | GPT-5 Pro (`gpt-5-pro-2025-10-06`) | GPT-5 Pro with advanced reasoning, structured outputs, functions & tools. | 2025-10-06 | Text, Image | 400,000 (272K in / 128K out) | 128,000 | Sep 30, 2024 | | GPT-OSS 120B (`gpt-oss-120b`) Preview | Open-source style reasoning model. | 2025 | Text | 131,072 | 131,072 | May 31, 2024 | | GPT-OSS 20B (`gpt-oss-20b`) Preview | Smaller GPT-OSS variant. | 2025 | Text | 131,072 | 131,072 | May 31, 2024 | | GPT-4.1 (`gpt-4.1-2025-04-14`) | Multimodal model with streaming, function calling, and structured outputs. | 2025-04-14 | Text, Image | 1,047,576 · 128K (managed) · 300K (batch) | 32,768 | May 31, 2024 | | GPT-4.1 Nano (`gpt-4.1-nano-2025-04-14`) | Lightweight GPT-4.1 variant. | 2025-04-14 | Text, Image | 1,047,576 · 128K (managed) · 300K (batch) | 32,768 | May 31, 2024 | | GPT-4.1 Mini (`gpt-4.1-mini-2025-04-14`) | Smaller GPT-4.1 variant. | 2025-04-14 | Text, Image | 1,047,576 · 128K (managed) · 300K (batch) | 32,768 | May 31, 2024 | | Codex Mini (`codex-mini-2025-05-16`) | Fine-tuned o4-mini optimized for code. | 2025-05-16 | Text, Image | 200K in / 100K out | 100,000 | May 31, 2024 | | O3 Pro (`o3-pro-2025-06-10`) | Advanced reasoning model with enhanced capabilities. | 2025-06-10 | Text, Image | 200K in / 100K out | 100,000 | May 31, 2024 | | O4 Mini (`o4-mini-2025-04-16`) | Reasoning model with efficient performance. | 2025-04-16 | Text, Image | 200K in / 100K out | 100,000 | May 31, 2024 | | O3 (`o3-2025-04-16`) | Reasoning model with tool use. | 2025-04-16 | Text, Image | 200K in / 100K out | 100,000 | May 31, 2024 | | O3 Mini (`o3-mini-2025-01-31`) | Text-only reasoning model. | 2025-01-31 | Text | 200K in / 100K out | 100,000 | Oct 2023 | | O1 (`o1-2024-12-17`) | Reasoning model with structured outputs. | 2024-12-17 | Text, Image | 200K in / 100K out | 100,000 | Oct 2023 | | O1 Preview (`o1-preview-2024-09-12`) | Early preview release of O1. | 2024-09-12 | Text | 128K in / 32,768 out | 32,768 | Oct 2023 | | O1 Mini (`o1-mini-2024-09-12`) | Cost-efficient O1 variant. | 2024-09-12 | Text | 128K in / 65,536 out | 65,536 | Oct 2023 | | GPT-4o (`gpt-4o-2024-11-20`) | Multimodal GPT-4o with JSON mode, function calling, and strong vision support. | 2024-11-20 | Text, Image | 128,000 | 16,384 | Oct 2023 | | GPT-4o (`gpt-4o-2024-08-06`) | Updated GPT-4o release. | 2024-08-06 | Text, Image | 128,000 | 16,384 | Oct 2023 | | GPT-4o (`gpt-4o-2024-05-13`) | Early GPT-4o release (Turbo Vision parity). | 2024-05-13 | Text, Image | 128,000 | 4,096 | Oct 2023 | | GPT-4o Mini (`gpt-4o-mini-2024-07-18`) | Smaller, fast GPT-4o variant. | 2024-07-18 | Text, Image | 128,000 | 16,384 | Oct 2023 | | GPT-4 Turbo (`gpt-4-turbo-2024-04-09`) | Multimodal GPT-4 Turbo, successor to preview models. | 2024-04-09 | Text, Image | 128,000 | 4,096 | Dec 2023 | | GPT-3.5 Turbo (`gpt-35-turbo-0125`) | JSON mode, function calling, reproducible outputs. | 2024-01-25 | Text | 16,385 in / 4,096 out | 4,096 | Sep 2021 | | GPT-3.5 Turbo (`gpt-35-turbo-1106`) | Earlier GPT-3.5 Turbo variant. | 2023-11-06 | Text | 16,385 in / 4,096 out | 4,096 | Sep 2021 | | GPT-3.5 Turbo Instruct (`gpt-35-turbo-instruct-0914`) | Replacement for legacy Completions models. | 2023-09-14 | Text | 4,097 | 4,097 | Sep 2021 | *** ### Azure AI Inference Models | **Model** | **Description** | **Release Date** | **Modalities** | **Context Window** | **Max Output Tokens** | **Knowledge Cut-Off** | | ----------------------------------------------------------- | ------------------------------------------------------------------ | ---------------- | ------------------ | ------------------ | --------------------- | --------------------- | | AI21 Jamba 1.5 Mini (`AI21-Jamba-1.5-Mini`) | Tool calling: Yes; supports text, JSON, structured outputs. | – | Text | 262,144 | 4,096 | – | | AI21 Jamba 1.5 Large (`AI21-Jamba-1.5-Large`) | Tool calling: Yes; supports text, JSON, structured outputs. | – | Text | 262,144 | 4,096 | – | | O3 Mini (`o3-mini`) | OpenAI O-series; tool calling: Yes; structured outputs. | – | Text, Image | 200,000 | 100,000 | – | | O1 (`o1`) | OpenAI O-series; tool calling: Yes; structured outputs. | – | Text, Image | 200,000 | 100,000 | – | | O1 Preview (`o1-preview`) | Early O1 preview; tool calling: Yes. | – | Text | 128,000 | 32,768 | – | | O1 Mini (`o1-mini`) | Cost-efficient O1 variant; tool calling: No. | – | Text | 128,000 | 65,536 | – | | GPT-4o (`gpt-4o`) | Multimodal GPT-4o; tool calling: Yes; supports structured outputs. | – | Text, Image, Audio | 131,072 | 16,384 | – | | GPT-4o Mini (`gpt-4o-mini`) | Smaller GPT-4o variant; tool calling: Yes. | – | Text, Image, Audio | 131,072 | 16,384 | – | | Cohere Command A (`Cohere-command-A`) | Cohere instruct model; tool calling: Yes. | – | Text | 256,000 | 8,000 | – | | Cohere Command R+ (`Cohere-command-r-plus-08-2024`) | Optimized for reasoning and retrieval; tool calling: Yes. | 2024-08 | Text | 131,072 | 4,096 | – | | Cohere Command R (`Cohere-command-r-08-2024`) | Earlier R-series model; tool calling: Yes. | 2024-08 | Text | 131,072 | 4,096 | – | | JAIS 30B (`jais-30b-chat`) | Multilingual model; tool calling: Yes. | – | Text | 8,192 | 4,096 | – | | DeepSeek V3 (`DeekSeek-V3-0324`) | Latest DeepSeek v3; tool calling: No. | 2024-03 | Text | 131,072 | 131,072 | – | | DeepSeek V3 (Legacy) (`DeepSeek-V3-Legacy`) | Earlier DeepSeek v3. | – | Text | 131,072 | 131,072 | – | | DeepSeek R1 (`DeepSeek-R1`) | Reasoning-focused model. | – | Text | 163,840 | 163,840 | – | | Llama 4 Scout (`Llama-4-Scout-17B-16E-Instruct`) | Meta Llama 4 variant; tool calling: Yes. | – | Text, Image | 128,000 | 8,192 | – | | Llama 4 Maverick (`Llama-4-Maverick-17B-128E-Instruct-FP8`) | Meta Llama 4 Maverick; tool calling: Yes. | – | Text, Image | 128,000 | 8,192 | – | | Llama 3.3 70B (`Llama-3.3-70B-Instruct`) | Meta Llama 3.3 large model. | – | Text | 128,000 | 8,192 | – | | Llama 3.2 Vision (`Llama-3.2-90B-Vision-Instruct`) | Meta Llama 3.2 multimodal vision model. | – | Text, Image | 128,000 | 8,192 | – | | Llama 3.2 Vision (`Llama-3.2-11B-Vision-Instruct`) | Smaller Meta Llama 3.2 vision variant. | – | Text, Image | 128,000 | 8,192 | – | | Llama 3.1 8B (`Meta-Llama-3.1-8B-Instruct`) | Meta Llama 3.1 instruct variant. | – | Text | 131,072 | 8,192 | – | | Llama 3.1 405B (`Meta-Llama-3.1-405B-Instruct`) | Largest Meta Llama 3.1 instruct variant. | – | Text | 131,072 | 8,192 | – | | MAI DS R1 (`MAI-DS-R1`) | Reasoning model. | – | Text | 163,840 | 163,840 | – | | Phi-4 (`Phi-4`) | Microsoft Phi-4 general-purpose. | – | Text | 16,384 | 16,384 | – | | Phi-4 Mini (`Phi-4-mini-instruct`) | Small Phi-4 variant. | – | Text | 131,072 | 4,096 | – | | Phi-4 Multimodal (`Phi-4-multimodal-instruct`) | Multimodal Phi-4 (text, image, audio). | – | Text, Image, Audio | 131,072 | 4,096 | – | | Phi-4 Reasoning (`Phi-4-reasoning`) | Phi-4 reasoning-focused model. | – | Text | 32,768 | 32,768 | – | | Phi-4 Mini Reasoning (`Phi-4-mini-reasoning`) | Lightweight reasoning variant. | – | Text | 128,000 | 128,000 | – | | Phi-3.5 Mini (`Phi-3.5-mini-instruct`) | Phi-3.5 small instruct model. | – | Text | 131,072 | 4,096 | – | | Phi-3.5 MoE (`Phi-3.5-MoE-instruct`) | Phi-3.5 mixture-of-experts variant. | – | Text | 131,072 | 4,096 | – | | Phi-3.5 Vision (`Phi-3.5-vision-instruct`) | Phi-3.5 multimodal variant. | – | Text, Image | 131,072 | 4,096 | – | | Phi-3 Mini 128K (`Phi-3-mini-128k-instruct`) | Compact Phi-3 variant with 128K context. | – | Text | 131,072 | 4,096 | – | | Phi-3 Mini 4K (`Phi-3-mini-4k-instruct`) | Compact Phi-3 with 4K context. | – | Text | 4,096 | 4,096 | – | | Phi-3 Small 128K (`Phi-3-small-128k-instruct`) | Small Phi-3 with 128K context. | – | Text | 131,072 | 4,096 | – | | Phi-3 Small 8K (`Phi-3-small-8k-instruct`) | Small Phi-3 with 8K context. | – | Text | 131,072 | 4,096 | – | | Phi-3 Medium 128K (`Phi-3-medium-128k-instruct`) | Medium Phi-3 with 128K context. | – | Text | 131,072 | 4,096 | – | | Phi-3 Medium 4K (`Phi-3-medium-4k-instruct`) | Medium Phi-3 with 4K context. | – | Text | 4,096 | 4,096 | – | | Codestral 2501 (`Codestral-2501`) | Mistral Codestral code-focused model. | – | Text | 262,144 | 4,096 | – | | Ministral 3B (`Ministral-3B`) | Lightweight Mistral model; tool calling: Yes. | – | Text | 131,072 | 4,096 | – | | Mistral Nemo (`Mistral-Nemo`) | Mistral Nemo model; tool calling: Yes. | – | Text | 131,072 | 4,096 | – | | Mistral Large 24.11 (`Mistral-Large-2411`) | Latest Mistral large model; tool calling: Yes. | – | Text | 128,000 | 4,096 | – | | Mistral Medium 25.05 (`Mistral-medium-2505`) | Balanced medium model; tool calling: No. | – | Text, Image | 128,000 | 128,000 | – | | Mistral Small 25.03 (`Mistral-small-2503`) | Newer small Mistral; tool calling: Yes. | – | Text, Image | 131,072 | 4,096 | – | | Mistral Small (`Mistral-small`) | Earlier small Mistral variant. | – | Text | 32,768 | 4,096 | – | | Tsuzumi 7B (`tsuzumi-7b`) | Lightweight Tsuzumi 7B model. | – | Text | 8,192 | 8,192 | – | *** ### Google AI Models (7 Models) | **Model** | **Description** | **Release Date** | **Modalities** | **Context Window** | **Max Output Tokens** | **Knowledge Cut-Off** | | ------------------------------------------------------------------------- | --------------------------------------------------------------- | ---------------- | ------------------------- | ------------------ | --------------------- | --------------------- | | Gemini 2.5 Pro (`gemini-2.5-pro`) | Most advanced model for complex reasoning and multimodal tasks. | 2025 | Text, Image, Audio, Video | – | 65,536 | Jan 2025 | | Gemini 2.5 Flash (`gemini-2.5-flash`) | Balanced model optimized for speed and general use. | 2025 | Text, Image, Audio, Video | – | 65,536 | Jan 2025 | | Gemini 2.5 Flash (Preview) (`gemini-2.5-flash-preview-09-2025`) | Preview release of Gemini 2.5 Flash. | 2025-09 | Text, Image, Audio, Video | – | 65,536 | Jan 2025 | | Gemini 2.5 Flash-Lite (`gemini-2.5-flash-lite`) | Lightweight, cost-efficient variant. | 2025 | Text, Image, Audio, Video | – | 65,536 | Jan 2025 | | Gemini 2.5 Flash-Lite (Preview) (`gemini-2.5-flash-lite-preview-09-2025`) | Preview release of Gemini 2.5 Flash-Lite. | 2025-09 | Text, Image, Audio, Video | – | 65,536 | Jan 2025 | | Gemini 2.0 Flash (`gemini-2.0-flash`) | Earlier generation Flash model. | 2024 | Text, Image, Audio, Video | – | 8,192 | Aug 2024 | | Gemini 2.0 Flash-Lite (`gemini-2.0-flash-lite`) | Lightweight 2.0 Flash variant. | 2024 / 2025 | Text, Image, Audio | – | 8,192 | Aug 2024 | *** ### Google Vertex AI Models | **Model** | **Description** | **Release Date** | **Modalities** | **Context Window** | **Max Output Tokens** | **Knowledge Cut-Off** | | --------------------------------------------------- | ---------------------------------------- | ---------------- | ------------------------- | ------------------ | --------------------- | --------------------- | | Gemini 2.5 Flash (Preview) (`gemini-2.5-flash`) | Balanced model optimized for speed. | 2025 | Text, Image, Audio, Video | 1M | 65,536 | Jan 2025 | | Gemini 2.5 Pro (Preview) (`gemini-2.5-pro`) | Most advanced Gemini model. | 2025 | Text, Image, Audio, Video | 1M | 65,536 | Jan 2025 | | Gemini 2.0 Flash (`gemini-2.0-flash`) | Previous Flash generation. | 2024 | Text, Image, Audio, Video | – | 8,192 | Aug 2024 | | Gemini 2.0 Flash-Lite (`gemini-2.0-flash-lite`) | Lightweight Flash variant. | 2024 | Text, Image, Audio | – | 8,192 | Aug 2024 | | Claude Opus 4.1 (`claude-opus-4-1`) | Exceptional reasoning model. | 2025 | Text, Image | 200K | 32,000 | Jan 2025 | | Claude Opus 4 (`claude-opus-4`) | Previous flagship Claude model. | 2025 | Text, Image | 200K | 32,000 | Jan 2025 | | Claude Sonnet 4.5 (`claude-sonnet-4-5`) | Best for complex agents and coding. | 2025 | Text, Image | 200K / 1M (beta) | 64,000 | Jan 2025 | | Claude Sonnet 4 (`claude-sonnet-4`) | High-performance Claude Sonnet model. | 2025 | Text, Image | 200K / 1M (beta) | 64,000 | Jan 2025 | | Claude 3.7 Sonnet (`claude-3-7-sonnet`) | High-performance with extended thinking. | 2025 | Text, Image | 200K | 64,000 | Oct 2024 | | Claude 3.5 Sonnet v2 (`claude-3-5-sonnet-v2`) | Updated Claude 3.5 Sonnet. | 2024 | Text, Image | 200K | 64,000 | 2024 | | Claude 3.5 Haiku (`claude-3-5-haiku`) | Fastest Claude model. | 2024 | Text, Image | 200K | 8,192 | Jul 2024 | | Claude 3 Haiku (`claude-3-haiku`) | Compact and fast Claude model. | 2024 | Text | 200K | 4,096 | Aug 2023 | | Claude 3.5 Sonnet (`claude-3-5-sonnet`) | Standard Claude 3.5 Sonnet. | 2024 | Text, Image | 200K | 64,000 | 2024 | | Jamba 1.5 Large (Preview) (`jamba-1-5-large`) | Advanced AI21 Jamba model. | 2025 | Text | – | – | – | | Jamba 1.5 Mini (Preview) (`jamba-1-5-mini`) | Smaller AI21 Jamba 1.5 variant. | 2025 | Text | – | – | – | | Mistral Medium 3 (`mistral-medium-3`) | Medium-sized Mistral model. | 2025 | Text | – | – | – | | Mistral Small 3.1 (`mistral-small-3-1-25-03`) | Smaller, faster Mistral. | 2025-03 | Text | – | – | – | | Mistral Large (`mistral-large-24-11`) | Large Mistral model. | 2024-11 | Text | – | – | – | | Mistral 7B (`mistral-7b`) | Base 7B model. | 2023 | Text | – | – | – | | Mixtral (`mixtral`) | Mixture-of-experts Mistral model. | 2024 | Text | – | – | – | | Llama 4 Maverick (`llama-4-maverick-17b-128e`) | Meta Llama 4 Maverick. | 2025 | Text | – | – | – | | Llama 4 Scout (`llama-4-scout-17b-16e`) | Meta Llama 4 Scout. | 2025 | Text | – | – | – | | Llama 4 (`llama-4`) | Core large Llama 4 model. | 2025 | Text | – | – | – | | Llama 3.3 (`llama-3-3`) | Successor to Llama 3.2. | 2025 | Text | – | – | – | | Llama 3.2 (Preview) (`llama-3-2-preview`) | Preview release of Llama 3.2. | 2024 | Text | – | – | – | | Llama 3.2 (`llama-3-2`) | Stable release of Llama 3.2. | 2024 | Text | – | – | – | | Llama 3.2 Vision (`llama-3-2-vision`) | Multimodal Llama 3.2. | 2024 | Text, Image | – | – | – | | Llama 3.1 (`llama-3-1`) | Part of Llama 3 family. | 2024 | Text | – | – | – | | Llama 3 (`llama-3`) | Base Llama 3 model. | 2023 | Text | – | – | – | | Qwen3-Next 80B Thinking (`qwen3-next-80b-thinking`) | Reasoning-focused Qwen3 variant. | 2025 | Text | – | – | – | | Qwen3-Next 80B Instruct (`qwen3-next-80b-instruct`) | Instruction-tuned Qwen3 variant. | 2025 | Text | – | – | – | | Qwen3 Coder (`qwen3-coder`) | Qwen3 code-focused model. | 2025 | Text (Code) | – | – | – | | Qwen3 235B (`qwen3-235b`) | Very large Qwen3 model. | 2025 | Text | – | – | – | | Qwen2 (`qwen2`) | Earlier Qwen release. | 2024 | Text | – | – | – | | DeepSeek V3.1 (`deepseek-v3-1`) | Advanced DeepSeek model. | 2025 | Text | – | – | – | | DeepSeek R1 (`deepseek-r1-0528`) | Reasoning-focused DeepSeek model. | 2025-05-28 | Text | – | – | – | | GPT-OSS 120B (`gpt-oss-120b`) | Open-weight GPT-OSS model. | 2025 | Text | – | – | – | | GPT-OSS 20B (`gpt-oss-20b`) | Smaller open-weight GPT-OSS model. | 2025 | Text | – | – | – | | Phi-3 (`phi-3`) | Microsoft Phi-3 model. | 2024 | Text | – | – | – | | Gemma 3n (`gemma-3n`) | Google Gemma series model. | 2025 | Text | – | – | – | | Gemma 3 (`gemma-3`) | Member of Gemma family. | 2025 | Text | – | – | – | | Gemma 2 (`gemma-2`) | Earlier Gemma generation. | 2024 | Text | – | – | – | | Gemma (`gemma`) | First Gemma release. | 2023 | Text | – | – | – | *** ## Model Selection Guide ### By Use Case #### Complex Reasoning & Analysis ```python theme={null} models = { "premium": ["gpt-5-2025-08-07", "gpt-4.1-2025-04-14", "claude-3-5-sonnet-20241022", "gemini-1.5-pro"], "balanced": ["gpt-4o", "gpt-4.1-mini-2025-04-14", "claude-3-sonnet-20240229"], "reasoning": ["o3-2025-04-16", "o1-2024-12-17", "o3-mini-2025-01-31"] } ``` #### High Volume Processing ```python theme={null} models = { "fastest": ["gpt-5-nano-2025-08-07", "gpt-4.1-nano-2025-04-14", "claude-3-5-haiku-20241022", "gemini-1.5-flash"], "cost_optimised": ["gpt-5-mini-2025-08-07", "gpt-4o-mini", "claude-3-haiku-20240307"], "open_source": ["meta.llama3-8b-instruct-v1:0"] } ``` #### Long Context Applications ```python theme={null} models = { "maximum_context": ["gemini-1.5-pro"], # 2M tokens "large_context": ["gpt-4.1-2025-04-14", "gemini-1.5-flash"], # 1M tokens "standard_large": ["gpt-5-2025-08-07", "claude-3-5-sonnet-20241022", "gpt-4o"] # 400K/200K/128K } ``` #### Multimodal Applications ```python theme={null} models = { "vision": ["gpt-5-2025-08-07", "gpt-4o", "gemini-1.5-pro", "o3-2025-04-16"], "video": ["gemini-1.5-pro", "gemini-1.5-flash"], "images": ["gpt-5-2025-08-07", "gpt-4o", "o4-mini-2025-04-16", "gemini-1.0-pro-vision"] } ``` ### Dynamic Model Selection (Example Script) ```python theme={null} from openai import OpenAI from typing import Dict, List class ModelSelector: """Intelligent model selection based on requirements""" # Model capabilities and costs MODEL_PROFILES = { "gpt-5-2025-08-07": { "cost": "highest", "speed": "medium", "capability": "highest", "context": 400000 }, "gpt-5-mini-2025-08-07": { "cost": "medium", "speed": "fast", "capability": "highest", "context": 400000 }, "gpt-5-nano-2025-08-07": { "cost": "low", "speed": "fastest", "capability": "good", "context": 400000 }, "gpt-4.1-2025-04-14": { "cost": "high", "speed": "medium", "capability": "highest", "context": 1047576 }, "gpt-4o": { "cost": "high", "speed": "medium", "capability": "highest", "context": 128000 }, "gpt-4o-mini": { "cost": "low", "speed": "fast", "capability": "good", "context": 128000 }, "o3-2025-04-16": { "cost": "highest", "speed": "slow", "capability": "highest", "context": 200000 }, "claude-3-5-sonnet-20241022": { "cost": "medium", "speed": "fast", "capability": "highest", "context": 200000 }, "claude-3-5-haiku-20241022": { "cost": "very_low", "speed": "fastest", "capability": "good", "context": 200000 }, "gemini-1.5-pro": { "cost": "medium", "speed": "medium", "capability": "highest", "context": 2000000 }, "gemini-1.5-flash": { "cost": "low", "speed": "fast", "capability": "good", "context": 1000000 } } def select_model(self, task_complexity: str, context_size: int, budget: str, speed_requirement: str) -> str: """Select optimal model based on requirements""" suitable_models = [] for model, profile in self.MODEL_PROFILES.items(): # Check context size if context_size > profile["context"]: continue # Check budget constraints if budget == "low" and profile["cost"] in ["high", "medium"]: continue # Check speed requirements if speed_requirement == "real-time" and profile["speed"] == "slow": continue # Check capability requirements if task_complexity == "complex" and profile["capability"] != "highest": continue suitable_models.append(model) # Return best match or default return suitable_models[0] if suitable_models else "gpt-4o-mini" # Usage example selector = ModelSelector() # Select model for different scenarios model_for_chat = selector.select_model( task_complexity="simple", context_size=1000, budget="low", speed_requirement="real-time" ) # Returns: gpt-4o-mini or claude-3-5-haiku-20241022 model_for_analysis = selector.select_model( task_complexity="complex", context_size=150000, budget="high", speed_requirement="normal" ) # Returns: claude-3-5-sonnet-20241022 or gemini-1.5-pro ``` ### Cost Optimised Model Routing (Example Script) ```python theme={null} import json from typing import Dict, Optional from openai import OpenAI class CostOptimisedRouter: """Route requests to most cost-effective model""" # Cost per 1K tokens (input/output estimated) MODEL_COSTS = { "gpt-4o": {"input": 0.00250, "output": 0.01000}, "gpt-4o-mini": {"input": 0.00015, "output": 0.00060}, "claude-3-5-sonnet-20241022": {"input": 0.00300, "output": 0.01500}, "claude-3-5-haiku-20241022": {"input": 0.00025, "output": 0.00125}, "gemini-1.5-flash": {"input": 0.00010, "output": 0.00040}, "gpt-3.5-turbo": {"input": 0.00050, "output": 0.00150} } def estimate_cost(self, model: str, input_tokens: int, output_tokens: int) -> float: """Estimate cost for a request""" costs = self.MODEL_COSTS.get(model, {"input": 0, "output": 0}) input_cost = (input_tokens / 1000) * costs["input"] output_cost = (output_tokens / 1000) * costs["output"] return input_cost + output_cost def select_cheapest_capable_model(self, task_type: str, estimated_tokens: int) -> str: """Select cheapest model capable of the task""" # Define capable models by task type capable_models = { "simple": ["gpt-4o-mini", "claude-3-5-haiku-20241022", "gpt-3.5-turbo"], "moderate": ["gpt-4o-mini", "claude-3-5-haiku-20241022", "gemini-1.5-flash"], "complex": ["gpt-4o", "claude-3-5-sonnet-20241022", "gemini-1.5-pro"] } models = capable_models.get(task_type, capable_models["simple"]) # Calculate costs and select cheapest cheapest = min(models, key=lambda m: self.estimate_cost( m, estimated_tokens, estimated_tokens // 2 )) return cheapest def route_request(self, prompt: str, complexity: str = "auto") -> Dict: """Route request to optimal model""" # Auto-detect complexity if needed if complexity == "auto": prompt_length = len(prompt) if prompt_length < 100: complexity = "simple" elif prompt_length < 500: complexity = "moderate" else: complexity = "complex" # Estimate tokens (rough estimate) estimated_tokens = len(prompt) // 4 # Select model model = self.select_cheapest_capable_model(complexity, estimated_tokens) # Get provider for model provider_map = { "gpt-4o": "openai", "gpt-4o-mini": "openai", "gpt-3.5-turbo": "openai", "claude-3-5-sonnet-20241022": "anthropic", "claude-3-5-haiku-20241022": "anthropic", "gemini-1.5-pro": "google", "gemini-1.5-flash": "google" } return { "model": model, "provider": provider_map[model], "estimated_cost": self.estimate_cost(model, estimated_tokens, estimated_tokens // 2), "reasoning": f"Selected {model} as cheapest option for {complexity} task" } # Usage router = CostOptimisedRouter() # Simple query - routes to cheapest model result = router.route_request("What is 2+2?") print(f"Model: {result['model']}, Cost: ${result['estimated_cost']:.6f}") # Complex query - routes to capable but cost-effective model result = router.route_request( "Analyse this 10-page legal document and identify key risks...", complexity="complex" ) print(f"Model: {result['model']}, Cost: ${result['estimated_cost']:.4f}") ``` ### A/B Testing Different Models ```python theme={null} import asyncio import time from typing import List, Dict from openai import OpenAI class ModelABTester: """A/B test different models for quality and performance""" def __init__(self, gateway_url: str): self.gateway_url = gateway_url self.results = [] def create_client(self, provider: str, credentials: dict) -> OpenAI: """Create client for specific provider""" headers = {"x-provider-name": provider} headers.update(credentials) return OpenAI( api_key="dummy", base_url=self.gateway_url, default_headers=headers ) async def test_model(self, model: str, provider: str, credentials: dict, prompt: str) -> Dict: """Test a single model""" client = self.create_client(provider, credentials) start_time = time.time() try: response = client.chat.completions.create( model=model, messages=[{"role": "user", "content": prompt}], max_tokens=500 ) end_time = time.time() return { "model": model, "provider": provider, "success": True, "latency": end_time - start_time, "response": response.choices[0].message.content, "tokens_used": response.usage.total_tokens if response.usage else 0 } except Exception as e: return { "model": model, "provider": provider, "success": False, "error": str(e) } async def run_ab_test(self, test_configs: List[Dict], prompt: str) -> List[Dict]: """Run A/B test across multiple models""" tasks = [] for config in test_configs: task = self.test_model( config["model"], config["provider"], config["credentials"], prompt ) tasks.append(task) results = await asyncio.gather(*tasks) return results def analyse_results(self, results: List[Dict]) -> Dict: """Analyse A/B test results""" successful = [r for r in results if r.get("success")] if not successful: return {"error": "All models failed"} # Find best by latency fastest = min(successful, key=lambda x: x["latency"]) # Calculate averages avg_latency = sum(r["latency"] for r in successful) / len(successful) return { "models_tested": len(results), "successful": len(successful), "fastest_model": fastest["model"], "fastest_latency": fastest["latency"], "average_latency": avg_latency, "results": results } # Usage example async def main(): tester = ModelABTester("https://gateway.altrum.ai/v1") # Configure models to test test_configs = [ { "model": "gpt-4o-mini", "provider": "openai", "credentials": {"Authorization": "Bearer key"} }, { "model": "claude-3-5-haiku-20241022", "provider": "anthropic", "credentials": {"x-api-key": "key"} }, { "model": "gemini-1.5-flash", "provider": "google", "credentials": {"x-goog-api-key": "key"} } ] # Run test prompt = "Write a haiku about cloud computing" results = await tester.run_ab_test(test_configs, prompt) # Analyse analysis = tester.analyse_results(results) print(f"Fastest model: {analysis['fastest_model']}") print(f"Latency: {analysis['fastest_latency']:.2f}s") # Run the test asyncio.run(main()) ``` *** ## Model Comparison Matrix | Provider | Model | Context | Speed | Cost | Best For | | ------------- | --------------------- | ------- | ------- | --------- | ------------------------------------------ | | **OpenAI** | gpt-5-2025-08-07 | 400K | Medium | Highest | Flagship coding, reasoning, agentic tasks | | | gpt-5-mini-2025-08-07 | 400K | Fast | Medium | Cost-efficient GPT-5 for defined tasks | | | gpt-5-nano-2025-08-07 | 400K | Fastest | Low | Fastest, cheapest GPT-5 variant | | | gpt-4.1-2025-04-14 | 1M | Medium | High | Instruction following, tool use | | | gpt-4o | 128K | Medium | High | Complex reasoning, multimodal | | | gpt-4o-mini | 128K | Fast | Low | Simple tasks, high volume | | | o3-2025-04-16 | 200K | Slow | Highest | Math, science, coding, multimodal analysis | | | o1-2024-12-17 | 200K | Slow | High | Step-by-step reasoning | | **Anthropic** | claude-3-5-sonnet | 200K | Fast | Medium | Coding, analysis | | | claude-3-5-haiku | 200K | Fastest | Very Low | Real-time apps | | | claude-3-opus | 200K | Slow | Very High | Complex research | | **Google** | gemini-1.5-pro | 2M | Medium | Medium | Massive documents | | | gemini-1.5-flash | 1M | Fast | Low | High-speed processing | | **Bedrock** | llama3-1-70b | 8K | Medium | Low | Open-source needs | | | titan-premier | 8K | Fast | Low | AWS integration | | | mistral-large | 32K | Medium | Medium | European compliance | *** ## Best Practices ### 1. Model Selection Strategy ```python theme={null} def select_model_strategy(requirements): """Strategic model selection based on requirements""" strategies = { "quality_first": [ "gpt-5-2025-08-07", "gpt-4.1-2025-04-14", "claude-3-5-sonnet-20241022", "gemini-1.5-pro" ], "speed_first": [ "gpt-5-nano-2025-08-07", "claude-3-5-haiku-20241022", "gpt-4o-mini", "gemini-1.5-flash" ], "cost_first": [ "gpt-5-nano-2025-08-07", "gpt-4o-mini", "claude-3-haiku-20240307", "meta.llama3-8b-instruct-v1:0" ], "context_first": [ "gemini-1.5-pro", # 2M tokens "gpt-4.1-2025-04-14", # 1M tokens "gpt-5-2025-08-07", # 400K tokens "claude-3-5-sonnet-20241022" # 200K tokens ] } return strategies.get(requirements["priority"], strategies["quality_first"]) ``` ### 2. Fallback Chains ```python theme={null} class ModelFallbackChain: """Implement fallback chains for reliability""" def __init__(self): self.fallback_chains = { "premium": [ "gpt-5-2025-08-07", "gpt-4.1-2025-04-14", "claude-3-5-sonnet-20241022", "gemini-1.5-pro", "gpt-4o" ], "efficient": [ "gpt-5-nano-2025-08-07", "gpt-5-mini-2025-08-07", "claude-3-5-haiku-20241022", "gpt-4o-mini", "gemini-1.5-flash" ] } def execute_with_fallback(self, chain_type, prompt): """Execute with automatic fallback""" chain = self.fallback_chains[chain_type] for model in chain: try: return self.call_model(model, prompt) except Exception as e: print(f"Model {model} failed: {e}") continue raise Exception("All models in fallback chain failed") ``` ### 3. Cost Monitoring ```python theme={null} class CostMonitor: """Monitor and control model costs""" def __init__(self, monthly_budget: float): self.monthly_budget = monthly_budget self.current_spend = 0.0 self.model_usage = {} def track_usage(self, model: str, tokens_in: int, tokens_out: int): """Track model usage and costs""" cost = self.calculate_cost(model, tokens_in, tokens_out) self.current_spend += cost if model not in self.model_usage: self.model_usage[model] = {"calls": 0, "cost": 0} self.model_usage[model]["calls"] += 1 self.model_usage[model]["cost"] += cost # Alert if approaching budget if self.current_spend > self.monthly_budget * 0.8: self.send_budget_alert() def get_usage_report(self): """Generate usage report""" return { "total_spend": self.current_spend, "budget_remaining": self.monthly_budget - self.current_spend, "model_breakdown": self.model_usage } ``` *** ## Migration Guide ### From Single Model to Multi-Model ```python theme={null} # Before: Single model deployment client = OpenAI(api_key="key") response = client.chat.completions.create( model="gpt-4", messages=[...] ) # After: Multi-model with intelligent selection class MultiModelClient: def __init__(self): self.gateway = "https://gateway.altrum.ai/v1" def create_completion(self, messages, requirements=None): # Select model based on requirements model = self.select_optimal_model(requirements) # Create client for selected model client = self.create_client_for_model(model) # Execute with automatic fallback return self.execute_with_fallback(client, model, messages) ``` *** ## Conclusion The Multi-Model Support feature transforms AI deployment from a single model dependency to a flexible, optimised multi-model strategy in your Production AI Stack. With access to 55+ models across 7 providers, organisations can select the perfect model for each use case, optimise costs, ensure reliability through redundancy, and stay at the forefront of AI innovation.