PHP AI Model Helper Class

> Type: const Model: string (final-class)

A static catalogue of AI model identifiers. Constants eliminate typos, enable IDE autocompletion, and create a single source across codebase.

Installation

composer install luminovang/php-ai-models

Luminova\AI\Model (enum version) ? PHP string-backed enum class.

Table of Contents

• Overview
• Installation / Import
• Naming Convention
• Constants Reference - OpenAI ? GPT-5 - OpenAI ? GPT-4.1 - OpenAI ? GPT-4o - OpenAI ? Reasoning (o-series) - OpenAI ? Image Generation - OpenAI ? Text-to-Speech - OpenAI ? Transcription - OpenAI ? Embeddings - OpenAI ? Moderation - Claude (Anthropic) ? 4.6 Generation - Claude (Anthropic) ? 4.5 Generation - Claude (Anthropic) ? 4.1 Generation - Claude (Anthropic) ? 4.0 Generation - Claude (Anthropic) ? 3.7 Generation - Claude (Anthropic) ? 3.5 Generation - Ollama ? Llama - Ollama ? Gemma - Ollama ? Mistral / Mixtral - Ollama ? Qwen - Ollama ? DeepSeek - Ollama ? Phi - Ollama ? Coding Models - Ollama ? Vision Models - Ollama ? Embedding Models
• Static Methods - client() - forClient() - forCapability() - capabilities() - isVision() - isReasoning() - isEmbedding() - exists() - all()
• Usage Examples - Basic Usage - Validating User Input - Iterating a Client's Models - Filtering by Capability - Guarding Vision Calls - Routing by Client

Overview

Model is a final class that cannot be instantiated. All constants and methods are accessed statically:

use Luminova\AI\Model;

// Anywhere a model string is expected ? pass the constant value.
$ai->message('Hello!', ['model' => Model::GPT_4_1_MINI]);
$ai->embed('Hello world', ['model' => Model::TEXT_EMBEDDING_3_SMALL]);
$ai->vision('Describe this.', '/tmp/img.png', ['model' => Model::LLAVA]);

Import

use Luminova\AI\Model;

No additional dependencies. The class uses ReflectionClass (PHP core) only for the all() helper method.

Naming Convention

| Rule | Example | |---|---| | Hyphens and dots ? underscores | gpt-4.1-mini ? GPT_4_1_MINI | | Size tag suffix (:8b) | llama3.1:8b ? LLAMA_3_1_8B | | MoE tag (8x7b) | mixtral:8x7b ? MIXTRAL_8X7B | | Versioned snapshot | claude-opus-4-5-20251101 ? CLAUDE_OPUS_4_5_SNAP | | Clean alias alongside snapshot | claude-opus-4-5 ? CLAUDE_OPUS_4_5 |

Constants Reference

OpenAI ? GPT-5 Family

| Constant | API Value | Notes | |---|---|---| | Model::GPT_5 | gpt-5 | Flagship model. Complex reasoning, multimodal, 256 K context. | | Model::GPT_5_MINI | gpt-5-mini | Faster, more affordable GPT-5 variant. | | Model::GPT_5_NANO | gpt-5-nano | Smallest GPT-5; optimized for latency and cost. |

OpenAI ? GPT-4.1 Family

| Constant | API Value | Notes | |---|---|---| | Model::GPT_4_1 | gpt-4.1 | 1 M token context, instruction-following, coding. Supports fine-tuning. | | Model::GPT_4_1_MINI | gpt-4.1-mini | Default chat model for the Luminova OpenAI client. Supports fine-tuning. | | Model::GPT_4_1_NANO | gpt-4.1-nano | Fastest / cheapest GPT-4.1. Supports fine-tuning. |

OpenAI ? GPT-4o Family

| Constant | API Value | Notes | |---|---|---| | Model::GPT_4O | gpt-4o | Multimodal (text + image + audio). 128 K context. | | Model::GPT_4O_MINI | gpt-4o-mini | Lightweight GPT-4o. 128 K context. | | Model::GPT_4O_AUDIO | gpt-4o-audio-preview | Native audio I/O. | | Model::GPT_4O_MINI_AUDIO | gpt-4o-mini-audio-preview | Lower-cost audio variant. | | Model::GPT_4O_REALTIME | gpt-4o-realtime-preview | Low-latency real-time speech and text. | | Model::GPT_4O_MINI_REALTIME | gpt-4o-mini-realtime-preview | Lower-cost realtime variant. | | Model::COMPUTER_USE | computer-use-preview | GUI interaction via the Responses API. |

OpenAI ? Reasoning (o-series)

| Constant | API Value | Notes | |---|---|---| | Model::O3 | o3 | Most capable reasoning model. Supports visual reasoning. | | Model::O3_PRO | o3-pro | o3 with extra compute for critical tasks. | | Model::O3_DEEP_RESEARCH | o3-deep-research | Multi-step web and document research. | | Model::O4_MINI | o4-mini | Fast reasoning; top benchmark for math/coding/vision. | | Model::O4_MINI_DEEP_RESEARCH | o4-mini-deep-research | Deep research variant of o4 Mini. |

OpenAI ? Image Generation

| Constant | API Value | Notes | |---|---|---| | Model::GPT_IMAGE_1_5 | gpt-image-1.5 | Latest image model. High-resolution + inpainting. Requires approval. | | Model::GPT_IMAGE_1 | gpt-image-1 | Default image model for the Luminova OpenAI client. Requires approval. | | Model::DALL_E_3 | dall-e-3 | Generally available. Up to 1792×1024 px. | | Model::DALL_E_2 | dall-e-2 | Previous generation; lower cost. |

OpenAI ? Text-to-Speech

| Constant | API Value | Notes | |---|---|---| | Model::GPT_4O_MINI_TTS | gpt-4o-mini-tts | Default TTS model. Voices: alloy, echo, fable, onyx, nova, shimmer. | | Model::TTS_1 | tts-1 | Optimized for real-time use. | | Model::TTS_1_HD | tts-1-hd | Higher quality, more natural intonation. |

OpenAI ? Transcription

| Constant | API Value | Notes | |---|---|---| | Model::GPT_4O_TRANSCRIBE | gpt-4o-transcribe | Superior accuracy, multilingual. | | Model::GPT_4O_MINI_TRANSCRIBE | gpt-4o-mini-transcribe | Faster, lower-cost. Currently recommended. | | Model::WHISPER_1 | whisper-1 | Default transcription model. 99+ languages. |

OpenAI ? Embeddings

| Constant | API Value | Notes | |---|---|---| | Model::TEXT_EMBEDDING_3_LARGE | text-embedding-3-large | Highest accuracy. 3072-dimensional (reducible). Best for RAG. | | Model::TEXT_EMBEDDING_3_SMALL | text-embedding-3-small | Default embedding model. 1536-dimensional. | | Model::TEXT_EMBEDDING_ADA_002 | text-embedding-ada-002 | Legacy. Prefer TEXT_EMBEDDING_3_SMALL for new work. |

OpenAI ? Moderation

| Constant | API Value | Notes | |---|---|---| | Model::OMNI_MODERATION | omni-moderation-latest | Text + image moderation. | | Model::TEXT_MODERATION | text-moderation-latest | Text-only moderation. |

Claude (Anthropic) ? 4.6 Generation (current)

| Constant | API Value | Notes | |---|---|---| | Model::CLAUDE_OPUS_4_6 | claude-opus-4-6 | Most capable. ~14.5 h task horizon. 1 M context (beta). | | Model::CLAUDE_SONNET_4_6 | claude-sonnet-4-6 | Default Claude model. Preferred by developers over previous Opus. |

Claude (Anthropic) ? 4.5 Generation

| Constant | API Value | Notes | |---|---|---| | Model::CLAUDE_OPUS_4_5 | claude-opus-4-5 | 67% price cut, 76% fewer output tokens vs previous Opus. | | Model::CLAUDE_OPUS_4_5_SNAP | claude-opus-4-5-20251101 | Pinned snapshot ? guaranteed reproducibility. | | Model::CLAUDE_SONNET_4_5 | claude-sonnet-4-5 | Industry-leading agent capabilities. | | Model::CLAUDE_HAIKU_4_5 | claude-haiku-4-5 | Fastest, most cost-effective Claude 4.5. | | Model::CLAUDE_HAIKU_4_5_SNAP | claude-haiku-4-5-20251001 | Pinned snapshot. |

Claude (Anthropic) ? 4.1 Generation

| Constant | API Value | Notes | |---|---|---| | Model::CLAUDE_OPUS_4_1 | claude-opus-4-1 | Industry leader for coding and long-horizon agentic tasks. | | Model::CLAUDE_OPUS_4_1_SNAP | claude-opus-4-1-20250805 | Pinned snapshot. | | Model::CLAUDE_SONNET_4_1 | claude-sonnet-4-1 | Production-ready agents at scale. |

Claude (Anthropic) ? 4.0 Generation

| Constant | API Value | Notes | |---|---|---| | Model::CLAUDE_OPUS_4 | claude-opus-4-0 | First Claude 4-gen Opus. State-of-the-art coding at release. | | Model::CLAUDE_SONNET_4 | claude-sonnet-4-0 | First Claude 4-gen Sonnet. Fast and context-aware. |

Claude (Anthropic) ? 3.7 Generation

| Constant | API Value | Notes | |---|---|---| | Model::CLAUDE_SONNET_3_7 | claude-sonnet-3-7 | Introduced extended (hybrid) thinking. | | Model::CLAUDE_SONNET_3_7_SNAP | claude-3-7-sonnet-20250219 | Pinned snapshot. |

Claude (Anthropic) ? 3.5 Generation (legacy)

| Constant | API Value | Notes | |---|---|---| | Model::CLAUDE_SONNET_3_5 | claude-3-5-sonnet-20241022 | Upgraded Sonnet with computer use (Oct 2024). | | Model::CLAUDE_HAIKU_3_5 | claude-3-5-haiku-20241022 | Lightweight, fast. Ideal for rapid completions. |

Ollama ? Llama Family (Meta)

| Constant | API Value | Notes | |---|---|---| | Model::LLAMA_3 | llama3 | Baseline Llama 3 (8 B). Most widely deployed. | | Model::LLAMA_3_1 | llama3.1 | 128 K context support. | | Model::LLAMA_3_1_8B | llama3.1:8b | Explicit 8 B tag. | | Model::LLAMA_3_1_70B | llama3.1:70b | Large-scale; multi-GPU or high-VRAM. | | Model::LLAMA_3_2 | llama3.2 | Compact (1 B / 3 B). Optimized for edge hardware. | | Model::LLAMA_3_2_1B | llama3.2:1b | Ultra-compact for edge and embedded use. | | Model::LLAMA_3_2_3B | llama3.2:3b | Small but capable for CLI copilots. | | Model::LLAMA_3_3 | llama3.3 | Latest large Llama (70 B). Excellent long-form chat. | | Model::LLAMA_3_3_70B | llama3.3:70b | Explicit 70 B tag. |

Ollama ? Gemma Family (Google)

| Constant | API Value | Notes | |---|---|---| | Model::GEMMA_3 | gemma3 | Current-gen (1 B?27 B). 128 K context; vision-capable (4 B+). | | Model::GEMMA_3_4B | gemma3:4b | Vision-capable; fits 8 GB VRAM. | | Model::GEMMA_3_12B | gemma3:12b | 12?16 GB VRAM sweet spot. | | Model::GEMMA_3_27B | gemma3:27b | Flagship Gemma 3 variant. | | Model::GEMMA_2 | gemma2 | Previous gen; proven reliability (2 B, 9 B, 27 B). | | Model::GEMMA_2_2B | gemma2:2b | Smallest Gemma 2; edge deployments. | | Model::GEMMA_2_9B | gemma2:9b | Good performance within 10 GB VRAM. | | Model::GEMMA_2_27B | gemma2:27b | Creative and NLP-focused tasks. |

Ollama ? Mistral / Mixtral

| Constant | API Value | Notes | |---|---|---| | Model::MISTRAL | mistral | Fast 7 B model with strong European language support. | | Model::MISTRAL_7B | mistral:7b | Explicit 7 B tag. | | Model::MIXTRAL_8X7B | mixtral:8x7b | Mixture-of-Experts; 2 experts active per token. | | Model::MIXTRAL_8X22B | mixtral:8x22b | Larger MoE; near-frontier quality for local hardware. |

Ollama ? Qwen Family (Alibaba)

| Constant | API Value | Notes | |---|---|---| | Model::QWEN_3 | qwen3 | Latest generation. Up to 256 K context; strong multilingual. | | Model::QWEN_3_4B | qwen3:4b | Compact; fits low-VRAM hardware. | | Model::QWEN_3_14B | qwen3:14b | Mid-range; single consumer GPU. | | Model::QWEN_3_72B | qwen3:72b | Maximum capability; enterprise-grade. | | Model::QWEN_2_5 | qwen2.5 | Previous gen; 18 T tokens; 128 K context. | | Model::QWEN_2_5_7B | qwen2.5:7b | | | Model::QWEN_2_5_14B | qwen2.5:14b | | | Model::QWEN_2_5_CODER | qwen2.5-coder | Coding-focused; 87 languages; matches GPT-4o at 32 B. | | Model::QWEN_2_5_CODER_7B | qwen2.5-coder:7b | Excellent code quality on limited hardware. | | Model::QWEN_2_5_CODER_32B | qwen2.5-coder:32b | Best local coding model at this scale. |

Ollama ? DeepSeek Family

| Constant | API Value | Notes | |---|---|---| | Model::DEEPSEEK_R1 | deepseek-r1 | Open reasoning model; matches o3 on key benchmarks. | | Model::DEEPSEEK_R1_7B | deepseek-r1:7b | Smallest R1; 8?10 GB VRAM. | | Model::DEEPSEEK_R1_14B | deepseek-r1:14b | Best mid-range reasoning for home labs. | | Model::DEEPSEEK_R1_32B | deepseek-r1:32b | 24 GB+ VRAM setups. | | Model::DEEPSEEK_R1_70B | deepseek-r1:70b | Near-frontier; multi-GPU recommended. | | Model::DEEPSEEK_CODER | deepseek-coder | 87 programming languages; 2 T training tokens. | | Model::DEEPSEEK_CODER_33B | deepseek-coder:33b | Top-quality local code generation. |

Ollama ? Phi Family (Microsoft)

| Constant | API Value | Notes | |---|---|---| | Model::PHI_4 | phi4 | Latest lightweight model; 14 B, 128 K context. | | Model::PHI_4_14B | phi4:14b | Explicit 14 B tag. | | Model::PHI_3 | phi3 | Previous gen (3.8 B Mini / 14 B Medium). | | Model::PHI_3_MINI | phi3:mini | 3.8 B; suitable for on-device and IoT. |

Ollama ? Coding Models

| Constant | API Value | Notes | |---|---|---| | Model::CODE_LLAMA | codellama | Meta's code-focused Llama (7 B?70 B). Fill-in-the-middle support. | | Model::CODE_LLAMA_13B | codellama:13b | Good balance of code quality and hardware. | | Model::CODE_LLAMA_34B | codellama:34b | High-quality generation for 24 GB VRAM. |

Ollama ? Vision Models

| Constant | API Value | Notes | |---|---|---| | Model::LLAVA | llava | Default vision model for the Luminova Ollama client. | | Model::LLAVA_13B | llava:13b | Stronger vision understanding. | | Model::LLAVA_34B | llava:34b | Highest-quality LLaVA; 24+ GB VRAM. | | Model::LLAMA_3_2_VISION | llama3.2-vision | Better structured-output than LLaVA. | | Model::MOONDREAM | moondream | Tiny (1.8 B); edge devices; fast captioning. | | Model::BAKLLAVA | bakllava | Mistral-7B base with LLaVA multimodal fine-tuning. |

Ollama ? Embedding Models

| Constant | API Value | Notes | |---|---|---| | Model::NOMIC_EMBED_TEXT | nomic-embed-text | Default embedding model. 8 K context; strong MTEB scores. | | Model::MXBAI_EMBED_LARGE | mxbai-embed-large | 1024-dimensional; competitive with OpenAI's large model. | | Model::ALL_MINILM | all-minilm | 384-dimensional; very fast similarity search. |

Static Methods

client(string $model): string|null

Return the client short-name for a given model string. Returns null for unknown models.

Model::client(Model::GPT_4_1_MINI);      // 'openai'
Model::client(Model::CLAUDE_SONNET_4_6); // 'anthropic'
Model::client(Model::LLAVA);             // 'ollama'
Model::client('my-custom-model');        // null

forClient(string $client): array

Return all ['CONST_NAME' => 'model-id'] pairs that belong to a specific client.

$openaiModels    = Model::forClient('openai');
$anthropicModels = Model::forClient('anthropic');
$ollamaModels    = Model::forClient('ollama');

foreach ($ollamaModels as $name => $id) {
    echo "{$name} => {$id}" . PHP_EOL;
}
// LLAMA_3 => llama3
// LLAMA_3_1 => llama3.1
// ...

forCapability(string $capability): array

Return all ['CONST_NAME' => 'model-id'] pairs that support a given capability tag.

Available tags: chat, vision, image, embedding, speech, transcription, reasoning, coding, fine-tuning, moderation.

$visionModels    = Model::forCapability('vision');
$embeddingModels = Model::forCapability('embedding');
$reasoningModels = Model::forCapability('reasoning');

capabilities(string $model): array

Return all capability tags for a given model string.

Model::capabilities(Model::O3);
// ['chat', 'vision', 'reasoning', 'coding']

Model::capabilities(Model::NOMIC_EMBED_TEXT);
// ['embedding']

Model::capabilities(Model::DALL_E_3);
// ['image']

isVision(string $model): bool

Model::isVision(Model::GPT_4_1);          // true
Model::isVision(Model::LLAVA);            // true
Model::isVision(Model::NOMIC_EMBED_TEXT); // false

isReasoning(string $model): bool

Model::isReasoning(Model::O3);            // true
Model::isReasoning(Model::DEEPSEEK_R1);  // true
Model::isReasoning(Model::GPT_4_1_MINI); // false

isEmbedding(string $model): bool

Model::isEmbedding(Model::TEXT_EMBEDDING_3_SMALL); // true
Model::isEmbedding(Model::NOMIC_EMBED_TEXT);       // true
Model::isEmbedding(Model::GPT_4_1);                // false

exists(string $model): bool

Check whether a model string is catalogued. Useful for validating user-supplied input before sending it to a client API.

Model::exists(Model::GPT_4_1_MINI);  // true
Model::exists('my-custom-model');    // false

all(): array

Return every public constant as a ['CONST_NAME' => 'model-id'] map using reflection. Private constants (PROVIDER_MAP, CAPABILITY_MAP) are automatically excluded.

$all = Model::all();
// [
//   'GPT_5'          => 'gpt-5',
//   'GPT_5_MINI'     => 'gpt-5-mini',
//   'GPT_4_1_MINI'   => 'gpt-4.1-mini',
//   ...
// ]

echo count(Model::all()); // 103

Usage Examples

Basic Usage

use Luminova\AI\Model;
use Luminova\AI\AI;

// Chat
$reply = AI::Openai($key)->message('Hello!', [
    'model' => Model::GPT_4_1_MINI,
]);

// Chat with Claude
$reply = AI::Anthropic($key)->message('Summarise this.', [
    'model' => Model::CLAUDE_SONNET_4_6,
]);

// Local inference with Ollama
$reply = AI::Ollama()->message('Explain recursion.', [
    'model' => Model::LLAMA_3_2,
]);

// Embeddings
$vector = AI::Openai($key)->embed('Hello world', [
    'model' => Model::TEXT_EMBEDDING_3_SMALL,
]);

// Vision
$output = AI::Openai($key)->vision('What is in this image?', '/tmp/photo.jpg', [
    'model' => Model::GPT_4_1,
]);

Validating User Input

$userModel = $request->get('model', Model::GPT_4_1_MINI);

if (!Model::exists($userModel)) {
    throw new InvalidArgumentException("Unknown model: {$userModel}");
}

$reply = $ai->message('Hello!', ['model' => $userModel]);

Iterating a Client's Models

// Build a select list for a UI
$options = [];

foreach (Model::forClient('openai') as $name => $id) {
    $options[$id] = str_replace('_', ' ', ucfirst(strtolower($name)));
}

// ['gpt-4.1-mini' => 'Gpt 4 1 mini', ...]

Filtering by Capability

// Only offer vision-capable models in the UI
$visionModels = Model::forCapability('vision');

// Only offer embedding models for the vector store config
$embeddingModels = Model::forCapability('embedding');

// Show reasoning models separately
$reasoningModels = Model::forCapability('reasoning');

Guarding Vision Calls

function analyzeImage(string $prompt, string $imagePath, string $model): array
{
    if (!Model::isVision($model)) {
        throw new RuntimeException(
            "Model '{$model}' does not support vision. " .
            "Try: " . Model::GPT_4_1 . " or " . Model::LLAVA
        );
    }

    return AI::getInstance()->vision($prompt, $imagePath, ['model' => $model]);
}

analyzeImage('Describe this chart.', '/tmp/q4.png', Model::GPT_4_1);   // OK
analyzeImage('Describe this chart.', '/tmp/q4.png', Model::WHISPER_1); // throws

Routing by Client

use Luminova\AI\AI;
use Luminova\AI\Model;

function chat(string $prompt, string $model): array
{
    $client = Model::client($model);

    return match ($client) {
        'openai'    => AI::Openai($_ENV['OPENAI_KEY'])->message($prompt, ['model' => $model]),
        'anthropic' => AI::Anthropic($_ENV['ANTHROPIC_KEY'])->message($prompt, ['model' => $model]),
        'ollama'    => AI::Ollama()->message($prompt, ['model' => $model]),
        default     => throw new RuntimeException("Unsupported client: {$client}"),
    };
}

chat('Tell me a joke.', Model::GPT_4_1_MINI);   // routed to OpenAI
chat('Tell me a joke.', Model::CLAUDE_SONNET_4_6); // routed to Anthropic
chat('Tell me a joke.', Model::LLAMA_3_2);      // routed to Ollama

See Also

• Luminova\AI\Model (enum version) ? PHP 8.1+ string-backed enum with instance methods and type-safe signatures.