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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

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

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Mar 15, 2026

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