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PHP AI Model (Enum) Class

Type: enum Model: string (string-backed)

A string-backed enum cataloguing AI model identifiers. Each case name is a readable PHP identifier; its ->value is the exact API string the client expects.

The enum version extends the class version with native PHP enum capabilities: exhaustive match checking, built-in from() / tryFrom() / cases(), type-safe function signatures, and instance methods that let you interrogate any case directly.

Installation

Install via composer.

composer install luminovang/php-ai-models-enum

No additional dependencies beyond PHP 8.1.

Luminova\AI\Model (const version) — PHP constant final class.

Table of Contents

Why an Enum?

Feature const class Model enum Model: string
Eliminate raw string typos ✅ ✅
IDE autocompletion ✅ ✅
Type-safe parameter hints (Model $m) ❌ ✅
Built-in from() / tryFrom() ❌ ✅
Built-in cases() iteration ❌ ✅
Exhaustive match enforcement ❌ ✅
Instance methods on a case ❌ ✅
Requires reflection for all() ✅ ❌
Can be used in attributes ❌ ✅

Choose the enum when you want PHP to enforce correctness at the type level; choose the class when you need to target PHP < 8.1 or prefer the Model::GPT_4 constant call style without ->value.

Import

use Luminova\AI\Model;

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

Getting the API String

Every client method that accepts a model parameter expects a plain string. Use ->value to extract the API string from a case:

// Correct — pass ->value to the client
$ai->message('Hello!', ['model' => Model::GPT_4_1_MINI->value]);

// Also correct when your method accepts Model directly and extracts ->value internally
function chat(Model $model, string $prompt): array {
    return $ai->message($prompt, ['model' => $model->value]);
}

Cases Reference

OpenAI — GPT-5 Family

Case ->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

Case ->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

Case ->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)

Case ->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

Case ->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

Case ->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

Case ->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

Case ->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

Case ->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)

Case ->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

Case ->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

Case ->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

Case ->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

Case ->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)

Case ->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)

Case ->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). Optimised 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)

Case ->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

Case ->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)

Case ->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

Case ->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)

Case ->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

Case ->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

Case ->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

Case ->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.

Built-in Enum Methods

These are standard PHP 8.1 backed-enum methods available on every string-backed enum automatically.

from(string $modelId): self

Resolve a case from its API string value. Throws \ValueError when the string is not a known case — use this when the input is trusted.

$model = Model::from('gpt-4.1-mini');  // Model::GPT_4_1_MINI
$model = Model::from('unknown');        // throws \ValueError

tryFrom(string $modelId): ?self

Resolve a case from its API string value. Returns null when the string is not known — use this for user or config input.

$model = Model::tryFrom('gpt-4.1-mini'); // Model::GPT_4_1_MINI
$model = Model::tryFrom('unknown');       // null

cases(): self[]

Return all cases as an array of enum instances. The order matches declaration order in the source file.

foreach (Model::cases() as $model) {
    echo $model->name . ' => ' . $model->value . PHP_EOL;
}
// GPT_5 => gpt-5
// GPT_5_MINI => gpt-5-mini
// ...
// ALL_MINILM => all-minilm

Note: cases() returns only enum cases, never private constants like PROVIDER_MAP or CAPABILITY_MAP.

Instance Methods

Called directly on a case — no arguments needed.

client(): string

Return the client short-name. Matches the key registered in AI::$clients: 'openai', 'anthropic', or 'ollama'.

Model::GPT_4_1_MINI->client();      // 'openai'
Model::CLAUDE_SONNET_4_6->client(); // 'anthropic'
Model::LLAVA->client();             // 'ollama'
Model::DEEPSEEK_R1->client();       // 'ollama'

capabilities(): array

Return all capability tags this case supports.

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

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

Model::GPT_4_1_MINI->capabilities();
// ['chat', 'vision', 'coding', 'fine-tuning']

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

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

Model::WHISPER_1->capabilities();
// ['transcription']

isVision(): bool

Whether this case accepts image input.

Model::GPT_4_1->isVision();          // true
Model::LLAVA->isVision();            // true
Model::NOMIC_EMBED_TEXT->isVision(); // false
Model::WHISPER_1->isVision();        // false

isReasoning(): bool

Whether this is a reasoning / chain-of-thought model.

Model::O3->isReasoning();            // true
Model::O3_PRO->isReasoning();        // true
Model::DEEPSEEK_R1->isReasoning();   // true
Model::CLAUDE_SONNET_3_7->isReasoning(); // true
Model::GPT_4_1_MINI->isReasoning();  // false

isEmbedding(): bool

Whether this case produces vector embeddings.

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

isChat(): bool

Whether this case supports chat / completion.

Model::GPT_4_1_MINI->isChat(); // true
Model::LLAMA_3_2->isChat();    // true
Model::DALL_E_3->isChat();     // false
Model::WHISPER_1->isChat();    // false

isCoding(): bool

Whether this case is optimized for code generation or completion.

Model::DEEPSEEK_CODER->isCoding();   // true
Model::QWEN_2_5_CODER->isCoding();   // true
Model::CODE_LLAMA->isCoding();       // true
Model::GPT_4_1->isCoding();          // true
Model::LLAVA->isCoding();            // false

isFineTunable(): bool

Whether this case supports fine-tuning via the client API.

Model::GPT_4_1->isFineTunable();      // true
Model::GPT_4_1_MINI->isFineTunable(); // true
Model::GPT_4_1_NANO->isFineTunable(); // true
Model::O3->isFineTunable();           // false
Model::CLAUDE_SONNET_4_6->isFineTunable(); // false

Static Methods

forClient(string $client): self[]

Return all cases belonging to a specific client as an array of enum instances.

$cases = Model::forClient('openai');
// [Model::GPT_5, Model::GPT_5_MINI, ..., Model::TEXT_MODERATION]

$cases = Model::forClient('anthropic');
// [Model::CLAUDE_OPUS_4_6, Model::CLAUDE_SONNET_4_6, ...]

$cases = Model::forClient('ollama');
// [Model::LLAMA_3, Model::LLAMA_3_1, ..., Model::ALL_MINILM]

foreach (Model::forClient('ollama') as $model) {
    echo $model->name . ' = ' . $model->value . PHP_EOL;
}

forCapability(string $capability): self[]

Return all cases that support a given capability tag as an array of enum instances.

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');
$codingModels    = Model::forCapability('coding');
$imageModels     = Model::forCapability('image');

foreach (Model::forCapability('reasoning') as $model) {
    echo $model->value . ' (' . $model->client() . ')' . PHP_EOL;
}
// o3 (openai)
// o3-pro (openai)
// deepseek-r1 (ollama)
// claude-sonnet-3-7 (anthropic)
// ...

resolve(string $modelId): ?self

A readable alias for Model::tryFrom(). Returns the matching case or null — never throws. Intended to make call-site intent explicit when validating external input.

$model = Model::resolve('gpt-4.1-mini');  // Model::GPT_4_1_MINI
$model = Model::resolve('bad-string');     // null

// Safe fallback pattern
$model = Model::resolve($config['model']) ?? Model::GPT_4_1_MINI;

Usage Examples

Basic Usage

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

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

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

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

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

// Ollama vision
$reply = AI::Ollama()->vision('What is in this image?', '/tmp/photo.jpg', [
    'model' => Model::LLAVA->value,
]);

Type-Safe Function Signatures

The enum's biggest advantage — invalid model strings become impossible at the type level:

use Luminova\AI\Model;

function chat(string $prompt, Model $model = Model::GPT_4_1_MINI): array
{
    return AI::getInstance()->message($prompt, ['model' => $model->value]);
}

// Valid calls
chat('Hello!');
chat('Hello!', Model::O3);
chat('Hello!', Model::CLAUDE_OPUS_4_6);
chat('Hello!', Model::LLAMA_3_3);

// Invalid — PHP type error at call time, not a runtime client error
chat('Hello!', 'gpt-4.1-mini');  // TypeError: Argument 2 must be of type Model

Resolving from Config or User Input

// From a config file
$configured = $config->get('ai.model', 'gpt-4.1-mini');
$model = Model::resolve($configured) ?? Model::GPT_4_1_MINI;

echo "Using: {$model->value} ({$model->client()})";

// From a web request — reject unknown values
$userModel = $_POST['model'] ?? '';
$model = Model::tryFrom($userModel);

if ($model === null) {
    http_response_code(400);
    exit("Unknown model: {$userModel}");
}

$reply = $ai->message($prompt, ['model' => $model->value]);

Exhaustive match on Cases

PHP enforces that all cases in a match are handled when matching on an enum. This prevents silent omissions as you add new cases:

$model = Model::CLAUDE_SONNET_4_6;

$tier = match ($model) {
    Model::GPT_5, Model::CLAUDE_OPUS_4_6, Model::O3_PRO     => 'flagship',
    Model::GPT_4_1, Model::CLAUDE_SONNET_4_6, Model::O3     => 'standard',
    Model::GPT_4_1_MINI, Model::CLAUDE_HAIKU_4_5, Model::O4_MINI => 'efficient',
    default => 'other',
};

Routing by Provider

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

function chat(string $prompt, Model $model): array
{
    return match ($model->client()) {
        'openai'    => AI::Openai($_ENV['OPENAI_KEY'])->message($prompt, ['model' => $model->value]),
        'anthropic' => AI::Anthropic($_ENV['ANTHROPIC_KEY'])->message($prompt, ['model' => $model->value]),
        'ollama'    => AI::Ollama()->message($prompt, ['model' => $model->value]),
    };
}

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

Guarding Capability Requirements

use Luminova\AI\Model;

function analyzeImage(string $prompt, string $imagePath, Model $model): array
{
    if (!$model->isVision()) {
        throw new RuntimeException(
            "Model '{$model->value}' does not support vision. " .
            'Try Model::GPT_4_1, Model::LLAVA, or Model::LLAMA_3_2_VISION.'
        );
    }

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

analyzeImage('What breed is this?', '/tmp/dog.jpg', Model::GPT_4_1);   // OK
analyzeImage('What breed is this?', '/tmp/dog.jpg', Model::WHISPER_1); // throws
function embed(string $text, Model $model = Model::TEXT_EMBEDDING_3_SMALL): array
{
    if (!$model->isEmbedding()) {
        throw new RuntimeException("'{$model->value}' is not an embedding model.");
    }

    return AI::getInstance()->embed($text, ['model' => $model->value]);
}

Building UI Select Lists

// All available models grouped by client for a settings page
$grouped = [];

foreach (Model::cases() as $model) {
    $grouped[$model->client()][] = [
        'value' => $model->value,
        'label' => str_replace('_', ' ', ucfirst(strtolower($model->name))),
        'tags'  => $model->capabilities(),
    ];
}

// Only offer vision-capable models in a vision task dropdown
$visionOptions = array_map(
    fn(Model $m): array => ['value' => $m->value, 'label' => $m->name],
    Model::forCapability('vision')
);

Pinned vs. Alias Snapshots

For Claude models, Anthropic recommends using versioned snapshot strings in production to guarantee reproducible behavior. Luminova provides both:

// Always-latest alias — may quietly change behavior when Anthropic updates it
$model = Model::CLAUDE_OPUS_4_5;        // 'claude-opus-4-5'

// Pinned snapshot — behavior is frozen to the exact release
$model = Model::CLAUDE_OPUS_4_5_SNAP;   // 'claude-opus-4-5-20251101'

Use the alias in development for the newest behavior; use the snapshot in staging/production for determinism.

Comparison with the Class Version

Aspect class Model enum Model: string
Access a model string Model::GPT_4_1_MINI Model::GPT_4_1_MINI->value
Type-hint a parameter string $model Model $model
Resolve from a string Model::exists($s) + use $s Model::tryFrom($s) → `Model
Iterate all models Model::all() (reflection) Model::cases() (built-in)
Check client Model::client($id) Model::GPT_4_1->client()
Check capability Model::isVision($id) Model::GPT_4_1->isVision()
Filter by client Model::forProvider('openai') → string[] Model::forProvider('openai') → Model[]
Filter by capability Model::forCapability('vision') → string[] Model::forCapability('vision') → Model[]
match exhaustiveness ❌ ✅
Use in PHP attributes ❌ ✅
PHP requirement 8.0+ (reflection only) 8.1+

Both classes expose identical client data and capability tags. The enum is recommended for all new code.

See Also

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PHP AI Model Helper Enum Class

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Mar 15, 2026

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