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Different LLM models have different capabilities and limitations, even within the same provider. A request that works perfectly with one model may fail or produce unexpected results with another. Understanding these differences is key to building reliable applications.

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Symptoms

  • Errors like “model not found,” “parameter not supported,” or “context length exceeded”
  • Unexpected or degraded response quality from certain models
  • Requests that succeed on one model but fail on another
  • Tool calls or JSON output that work with some models but not others

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Check Model Availability

Verify that the model identifier in your preset or request matches a model that is currently available from the provider. Model names are case-sensitive and must be exact: 
<?php

use Cognesy\Messages\Messages;
use Cognesy\Polyglot\Inference\Inference;

// Correct: exact model identifier
$text = Inference::using('openai')
    ->withModel('gpt-4.1-nano')
    ->withMessages(Messages::fromString('Hello'))
    ->get();
// @doctest id="d56d"
Models are periodically deprecated or renamed by providers. If a model that previously worked suddenly fails, check the provider’s release notes for changes.

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Context Length Limits

Each model has a maximum context length (measured in tokens). If your input exceeds this limit, the provider returns an error. The contextLength field in the preset defines this limit for reference, but the actual enforcement happens at the provider. Common context windows:
ModelApproximate Context Window
GPT-4.11,000,000 tokens
GPT-4.1-nano1,000,000 tokens
Claude Haiku 4.5200,000 tokens
Gemini modelsvaries by model
Llama 3 (via Ollama)128,000 tokens
When you hit context limits, consider:
  • Summarizing or truncating the input
  • Splitting the request into smaller chunks
  • Switching to a model with a larger context window

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Tool and Function Calling Support

Not all models support tool (function) calling. If you pass tools to a model that does not support them, the provider may return an error or silently ignore the tools. When debugging tool-related failures, first confirm the request works without tools: 
<?php

use Cognesy\Messages\Messages;
use Cognesy\Polyglot\Inference\Inference;

// Step 1: Test plain text output
$text = Inference::using('openai')
    ->withModel('gpt-4.1-nano')
    ->withMessages(Messages::fromString('What is 2 + 2?'))
    ->get();

echo $text; // Verify this works first

// Step 2: Then add tools back
$text = Inference::using('openai')
    ->withModel('gpt-4.1-nano')
    ->withMessages(Messages::fromString('What is 2 + 2?'))
    ->withTools($myTools)
    ->get();
// @doctest id="5ad5"

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JSON and Structured Output Support

Models vary in their support for structured output formats:
  • JSON Schema mode — the model is constrained to output JSON matching a specific schema. Only some models support this.
  • JSON object mode — the model is instructed to output valid JSON, but without schema enforcement.
  • Plain text — all models support this.
If JSON schema output fails, try JSON object mode or plain text as a fallback. Polyglot’s responseFormat option controls this, but the actual behavior depends on the model.

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Streaming Support

Most modern models support streaming, but some do not. If enabling streaming causes errors, test with a non-streaming request first: 
<?php

use Cognesy\Messages\Messages;
use Cognesy\Polyglot\Inference\Inference;

// Test non-streaming first
$text = Inference::using('openai')
    ->withModel('gpt-4.1-nano')
    ->withMessages(Messages::fromString('Write a haiku.'))
    ->get();

// Then test streaming
$stream = Inference::using('openai')
    ->withModel('gpt-4.1-nano')
    ->withMessages(Messages::fromString('Write a haiku.'))
    ->stream();

foreach ($stream->deltas() as $delta) {
    echo $delta->contentDelta;
}
// @doctest id="a051"

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Vision and Multimodal Capabilities

Only certain models support image inputs. Sending images to a text-only model will cause an error. Check the provider’s documentation to confirm which models accept multimodal input.

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Implement Model Fallbacks

For production applications, implement a fallback strategy that tries alternative models when the preferred model fails: 
<?php

use Cognesy\Messages\Messages;
use Cognesy\Polyglot\Inference\Inference;

function withFallback(array $models, string $prompt): string {
    $lastException = null;

    foreach ($models as $model) {
        try {
            return Inference::using('openai')
                ->withModel($model)
                ->withMessages(Messages::fromString($prompt))
                ->get();
        } catch (\Exception $e) {
            $lastException = $e;
            // Log the failure and try the next model
        }
    }

    throw new \RuntimeException(
        "All models failed. Last error: " . $lastException?->getMessage(),
        previous: $lastException,
    );
}

// Try capable models first, then fall back to simpler ones
$response = withFallback(
    ['gpt-4.1', 'gpt-4.1-mini', 'gpt-4.1-nano'],
    'Explain general relativity.',
);
// @doctest id="153f"

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Debugging Approach

When a model-specific issue arises, use this systematic approach:
  1. Reduce to plain text. Remove tools, response format, and streaming. If the plain text request fails, the problem is not model-capability related (check authentication, configuration, or connection).
  2. Add features one at a time. Re-enable streaming, then response format, then tools. The first feature that causes failure identifies the unsupported capability.
  3. Check the provider’s model documentation. Verify that the specific model version supports the feature you need.
  4. Try a different model from the same provider to confirm whether the issue is model-specific or provider-wide.