Sora 2 Error 400 Invalid Argument: Complete Fix Guide (2026)

The Sora 2 error 400 invalid_argument means your API request contains a parameter with an incorrect type, format, or value. The three most common causes are: missing filename in file uploads, sending strings instead of numbers for numeric parameters, and content that triggers moderation filters. This guide provides a 60-second diagnostic to identify your specific issue and exact fixes for each cause, helping you resolve the error without burning through your API credits.

Understanding the 400 Invalid Argument Error

When your Sora 2 API request returns a 400 status code with "invalid_argument" in the response, it indicates that the server understood your request but rejected it because one or more parameters are invalid. Unlike 401 (authentication) or 429 (rate limit errors), a 400 error means the problem lies in how your request is structured, not in your account status or server capacity.

The 400 error is fundamentally different from other error types because it is permanent for that specific request. Retrying the exact same request will always fail. You must identify and fix the issue before attempting again. This is why a systematic approach to diagnosis is essential, as random troubleshooting wastes both time and API credits.

The error response typically includes a message field that provides clues about what went wrong. For example, you might see messages like "Invalid type for 'input_reference': expected file, got string" or simply "invalid_argument" without specifics. The level of detail varies depending on whether the error originates from OpenAI's direct API, Azure OpenAI, or third-party API providers. Understanding these variations helps you interpret error messages correctly regardless of your API access method.

One critical distinction to understand is that 400 errors can come from two different sources within the Sora system. Parameter validation errors occur before any processing begins and relate to incorrect data types, missing required fields, or invalid values. Content moderation errors (often appearing as "sentinel_block" or "moderation_blocked") occur when your prompt or input image triggers safety filters. Both return 400 status codes but require completely different fix approaches, which is why our diagnostic flowchart separates these immediately.

Understanding the timing of when errors occur helps with diagnosis. Parameter validation happens at the API gateway level within milliseconds of receiving your request. If you receive an error almost instantly after sending, it's likely a parameter issue. Content moderation takes slightly longer (typically 1-3 seconds) as the system analyzes your prompt and any input images. If there's a brief delay before the error, moderation is more likely the cause. This timing difference, while subtle, provides an additional diagnostic signal when error messages are vague.

The financial impact of 400 errors is worth noting for production systems. Because validation occurs before processing begins, you won't be charged credits for failed requests. This is different from errors like 1004 (generation failed), which occur after processing has started and may still consume credits depending on how far generation progressed. This credit-free failure for 400 errors makes it safe to implement aggressive validation testing during development without worrying about costs.

Quick Diagnosis: Identify Your Error Type

Before attempting any fixes, you need to identify which category your error falls into. Using the wrong fix for your error type wastes time and potentially credits. Follow this diagnostic process to identify your error in under 60 seconds.

Start by examining your error response message carefully. Copy the entire error response and look for these keywords that indicate moderation-related issues: "sentinel_block," "moderation," "policy," "content_policy," "safety," or "blocked." If any of these appear, your error is a content moderation issue, and you should skip directly to the content moderation section below. The fix involves modifying your prompt or input content, not your technical parameters.

If no moderation keywords appear, check whether the error message mentions a specific parameter name. Messages like "Invalid type for 'duration'" or "invalid value for 'resolution'" point directly to the problematic parameter. When you see a specific parameter name, the fix is usually straightforward: consult the parameter reference table below and correct that parameter's format. Common examples include sending "10" as a string instead of the integer 10, using "1920x1080" instead of "1080p" for resolution, or including extra spaces in enumerated values.

For errors without specific parameter mentions, the issue often lies in file handling. If your request involves uploading an image for image-to-video generation, the most common cause is a missing filename parameter in your multipart form data. Some HTTP libraries don't automatically include the filename when you pass a file path, and the Sora API requires it. Another common file-related issue is incorrect content types, particularly when sending base64-encoded images without proper headers.

When the error message is vague or unhelpful, hidden characters in your API key or request body may be the culprit. This is especially common when copying API keys from documents, emails, or web interfaces that may include invisible Unicode characters. The solution is to manually retype critical values rather than copy-pasting, or to use a hex editor to verify there are no hidden characters. This issue affects approximately 15% of developers who encounter unexplained 400 errors, according to community reports (OpenAI Community Forums, January 2026).

Common Causes and Quick Fixes

Now that you've identified your error category, here are the specific causes and fixes for each type. These solutions are based on verified fixes from GitHub issues, OpenAI community forums, and direct API documentation.

Missing filename in multipart uploads is the single most common cause of 400 errors when using image-to-video features. When you send an image file using multipart form data, the Content-Disposition header must include a filename parameter. Many HTTP libraries like Python's requests or Node.js's form-data require you to explicitly provide this. The error message "Invalid type for 'input_reference': expected file, got string" almost always indicates this issue.

Here's the fix in Python:

python

files = {'input_reference': open('image.png', 'rb')}

# Correct - explicit filename
files = {'input_reference': ('image.png', open('image.png', 'rb'), 'image/png')}

For JavaScript/Node.js:

javascript
// Wrong - missing filename in FormData
formData.append('input_reference', fileBuffer);

// Correct - with filename
formData.append('input_reference', fileBuffer, { filename: 'image.png', contentType: 'image/png' });

String versus numeric type mismatches are the second most common cause. Environment variables are particularly prone to this issue because they're always strings. If you read duration from an environment variable and pass it directly, you're sending "10" instead of 10. The API expects exact types and will reject string representations of numbers.

python
# Wrong - duration as string
duration = os.environ.get('VIDEO_DURATION')  # Returns "10"

# Correct - convert to integer
duration = int(os.environ.get('VIDEO_DURATION', 10))

Hidden Unicode characters in API keys cause mysterious failures that are difficult to debug. When you copy your API key from the OpenAI dashboard, email confirmations, or documentation, invisible characters like zero-width spaces, byte order marks, or non-breaking spaces may be included. These characters are not visible in most text editors but cause authentication or parsing failures.

To verify and fix this issue, use your terminal to check the hex representation of your API key:

bash
echo -n "your-api-key" | xxd

Look for any unexpected hex values. A clean API key should only contain alphanumeric characters and hyphens. If you see unusual bytes, manually retype your API key character by character rather than copying it.

Content moderation triggers require a different approach entirely. When your prompt or input image triggers the safety filter, you'll see errors containing "sentinel_block" (pre-generation filter) or "moderation_blocked" (mid-generation filter). For content moderation errors, the fix involves rephrasing your prompt to avoid triggering terms.

Common trigger patterns include references to real public figures, violent or dangerous scenarios, explicit content descriptors, and medical or emergency situations. Even technical or artistic prompts can accidentally trigger filters if they contain certain word combinations. Try rephrasing with more neutral descriptive language. For example, instead of "a person getting injured," use "a character experiencing a surprise."

Schema mismatch between clip and frame_index requirements represents a less common but frustrating cause. When using storyboard or multi-scene features, the API expects specific parameter combinations. Providing frame_index without the corresponding clip structure, or using incompatible values between related parameters, triggers validation failures. The fix involves reviewing the documentation for multi-scene generation and ensuring all related parameters are provided together with compatible values.

Request size limits can trigger 400 errors when sending large image files. While the documentation doesn't always specify maximum file sizes, practical testing shows that images above 20MB consistently fail. Additionally, base64-encoded images embedded in JSON bodies are limited to approximately 10MB due to payload size restrictions. For larger files, use the dedicated file upload endpoint first, then reference the uploaded file ID in your generation request rather than embedding the file directly.

JSON structure issues beyond parameter types can cause failures. Extra fields not recognized by the API, duplicate keys in your JSON object, or trailing commas (which are invalid JSON but accepted by some parsers) can all trigger errors. Use a strict JSON validator that checks for these edge cases. The Python json module and JavaScript's JSON.parse are strict, but some YAML-to-JSON converters produce invalid JSON that appears to work locally but fails at the API.

Complete Parameter Reference

Understanding the exact format required for each parameter prevents errors before they occur. This reference table covers all Sora 2 API parameters with their expected types, valid values, and common mistakes. Keep this as a checklist when constructing your API requests.

The prompt parameter accepts a string between 1 and 1000 characters. Empty strings, null values, and strings exceeding the maximum length all trigger 400 errors. Ensure your prompt is non-empty and within limits. If you're dynamically generating prompts, add validation to check length before sending.

The model parameter must be an exact match to supported model names. As of January 2026, valid values include "sora-2" and "sora-turbo." Using deprecated model names from previous versions or typos will fail. The parameter is case-sensitive, so "Sora-2" or "SORA-2" will be rejected.

Duration must be an integer, not a string. Valid values are 5, 10, 15, and 20 seconds. Passing "10" as a string or using values like 12 (not in the allowed set) triggers errors. Always convert to integer and validate against allowed values before sending.

Resolution uses a specific string format. Valid values are "720p," "1080p," and "4k." Do not use pixel dimensions like "1920x1080" or "1280x720." The API expects the shorthand notation only.

Aspect ratio follows the pattern "width:height" as a string. Valid values include "16:9," "9:16," and "1:1." Do not use decimal ratios like "1.78" or percentage formats. The colon separator is required.

The input_reference parameter for image-to-video requires a file object, not a string path. Simply passing a file path string will fail. You must open the file and send it as binary data with proper multipart encoding including the filename parameter.

The n parameter (number of videos to generate) is an integer between 1 and 4. Values above 4 will fail. Note that generating multiple videos multiplies credit consumption proportionally.

For boolean parameters like loop, use actual boolean values (true/false), not strings. Sending "true" or "false" as strings will fail in strict JSON parsing.

The style parameter accepts specific enumerated values only. Valid options as of January 2026 include "natural" and "vivid." Custom style strings or values from other AI video APIs will be rejected. If you're migrating from another platform, map their style options to Sora's equivalents rather than passing through directly.

Seed parameter for reproducibility must be an integer within the valid range. While using a seed allows consistent outputs across requests, using floating-point numbers, negative values, or extremely large integers triggers validation errors. Keep seed values as positive integers within the 32-bit signed integer range (0 to 2,147,483,647).

The audio parameter structure has specific requirements when included. If you're adding audio to generated videos, the audio source must be properly formatted with correct duration and sample rate specifications. Mismatched audio duration relative to video duration is a common cause of validation failures. Ensure your audio file duration matches or is shorter than the requested video duration.

When working through API pricing and quotas, remember that parameter validation happens before any credits are consumed, so you won't be charged for requests that fail with 400 errors. However, fixing validation issues early saves time and frustration. Understanding the complete parameter landscape prevents the frustrating cycle of fixing one error only to encounter another.

Systematic Debugging Workflow

When you encounter a 400 error, follow this four-step systematic approach rather than making random changes. This workflow isolates the problem efficiently and prevents introducing new errors while fixing old ones.

Step 1: Read and categorize the error message. Spend 30 seconds carefully examining the full error response. Extract the error code, any parameter names mentioned, and keywords that indicate the error category. Write these down before proceeding. Many debugging sessions fail because developers start changing code before fully understanding the error.

Step 2: Validate your request locally before sending again. Use a JSON validator to check your request body structure. Verify all parameter types match the reference table. If you're uploading files, confirm the file exists, is readable, and is in a supported format (PNG, JPG, WebP for images). This step costs nothing and prevents wasted API credits. Tools like jq for command line or online JSON validators can identify structural issues immediately.

Step 3: Create and test a minimal request. Remove all optional parameters and construct the simplest possible valid request. Use a short, innocuous prompt like "A blue ball rolling slowly." Use default resolution and the shortest duration (5 seconds). If this minimal request succeeds, you've confirmed your authentication and basic setup work correctly. If it fails, the issue is fundamental to your configuration.

Step 4: Add parameters back incrementally. Starting from your working minimal request, add back one parameter at a time. Test after each addition. When a request fails, you've identified the problematic parameter. This binary search approach is faster than reviewing all parameters simultaneously and gives you certainty about which parameter is wrong.

This systematic approach follows the scientific method: isolate variables, test one change at a time, and document results. While it may seem slower than jumping directly to a suspected cause, it actually saves time by avoiding the common trap of making multiple changes simultaneously and not knowing which one fixed (or broke) the request. Keep notes of what you tested and the results. These notes become invaluable if you encounter similar issues later or need to explain the problem to team members.

For complex requests with many parameters, you can use a binary search variation. Instead of adding parameters one at a time, add half of them at once. If the request fails, the problem is in that half—discard the other half and repeat. If it succeeds, add half of the remaining parameters. This approach reduces the number of test requests logarithmically, which is especially valuable when each test consumes credits or requires significant wait time.

Regarding retry strategy, understand the difference between transient and permanent errors. Error 400 is permanent for that specific request, retrying won't help. However, error 1002 (queue full) and 5xx errors are transient and may succeed on retry. For rate limit errors (429), use exponential backoff. A simple implementation waits 1 second after the first failure, 2 seconds after the second, 4 seconds after the third, and so on up to a maximum of 60 seconds.

Prevention and Best Practices

After fixing your current error, implement these practices to prevent future 400 errors. Prevention is always cheaper than debugging.

Build a local validation layer that runs before every API call. This layer should check parameter types, validate required fields exist, verify file paths are accessible, and ensure values are within allowed ranges. A 10-line validation function can save hours of debugging. Here's a minimal example:

python
def validate_sora_request(params):
    errors = []

    if not params.get('prompt') or len(params['prompt']) > 1000:
        errors.append('prompt must be 1-1000 characters')

    if params.get('duration') and params['duration'] not in [5, 10, 15, 20]:
        errors.append('duration must be 5, 10, 15, or 20')

    if params.get('resolution') and params['resolution'] not in ['720p', '1080p', '4k']:
        errors.append('resolution must be 720p, 1080p, or 4k')

    if errors:
        raise ValueError(f"Validation failed: {', '.join(errors)}")

    return True

Create a pre-submission checklist for your API integration. Before deploying any code that calls the Sora API, verify: all environment variables are converted to correct types, file handling includes explicit filenames, API keys are typed manually (not copy-pasted), and error handling captures and logs full response bodies.

For teams and production systems, consider using API testing through a proxy like laozhang.ai for validation during development. Testing your request structure against a validation endpoint before sending to production APIs saves credits and provides clearer error messages. The proxy can catch common mistakes and provide suggestions for fixes.

Implement comprehensive logging that captures full request and response bodies. When errors occur in production, you need the complete picture to debug effectively. Log the exact payload you sent (redacting sensitive data like API keys) and the complete error response. Many 400 errors are intermittent because of variable input data, and logs help identify patterns.

Use strongly-typed languages or schema validation for request construction. TypeScript interfaces, Python dataclasses with validation, or JSON Schema validation catch type mismatches at build time rather than runtime. This investment pays off quickly as your integration grows more complex.

Here's a comprehensive TypeScript interface that enforces correct types:

typescript
interface SoraRequest {
  prompt: string;
  model: 'sora-2' | 'sora-turbo';
  duration?: 5 | 10 | 15 | 20;
  resolution?: '720p' | '1080p' | '4k';
  aspect_ratio?: '16:9' | '9:16' | '1:1';
  n?: 1 | 2 | 3 | 4;
  style?: 'natural' | 'vivid';
  loop?: boolean;
}

function createRequest(params: SoraRequest): Record<string, unknown> {
  // TypeScript will enforce correct types at compile time
  return { ...params };
}

Establish a testing pipeline that catches errors before production deployment. Create a suite of test requests covering all parameter combinations you use. Run these tests against a staging environment or with minimal-credit test requests before deploying new code. Automated tests that run on each code change prevent regressions where working code breaks due to seemingly unrelated changes.

Document your integration's parameter choices and the reasoning behind them. When you encounter an error six months from now, or when a new team member needs to debug an issue, documentation explaining why you chose specific parameter values and what you tried that didn't work saves significant time. Include links to any GitHub issues or forum threads that informed your decisions.

Advanced Troubleshooting

When standard fixes don't resolve your error, these advanced techniques address edge cases and platform-specific issues.

Azure OpenAI and direct OpenAI API have subtle differences in error responses. Azure wraps errors differently and may use different field names in error objects. If you're using Azure, check for errors in both the top-level response and nested under an "error" key. Azure also requires an API version parameter in the URL (e.g., api-version=preview), which must be current.

Rate limiting can masquerade as validation errors in some edge cases. When you hit rate limits on specific parameter combinations, some API gateways return 400 instead of 429. If you're seeing 400 errors only during high-traffic periods, implement rate limiting on your client side to stay within quotas.

Image dimension requirements are stricter than documentation suggests. While the API accepts various image sizes, certain aspect ratios combined with certain resolutions cause failures. For most reliable results, use images that match your target video aspect ratio. A 16:9 video works best with a 16:9 input image.

Character encoding issues in prompts can trigger validation failures. If your prompt contains non-ASCII characters, ensure your HTTP client sends the request with UTF-8 encoding explicitly specified. Some libraries default to Latin-1 or other encodings that corrupt special characters.

When all else fails, use the dry-run approach with alternative endpoints. Some API providers including laozhang.ai offer validation endpoints that check your request format without actually generating video. This is particularly useful for debugging complex requests with multiple parameters and file uploads.

SDK version mismatches can introduce subtle parameter handling differences. If you're using an official or community SDK rather than raw HTTP requests, ensure the SDK version matches the API version you're targeting. Older SDKs may use deprecated parameter names or formats that the current API rejects. Check the SDK's changelog for breaking changes when updating, and pin your dependency versions to avoid unexpected changes.

Network-level issues can corrupt request bodies in ways that appear as validation errors. Proxies that modify content, corporate firewalls that inject headers, or load balancers with aggressive timeout settings can all alter your request before it reaches the API. If you've verified your request is correct but still get errors, try sending the same request from a different network or bypassing any intermediary systems. Comparing request bodies captured at the client versus what the API logs receive can reveal discrepancies.

Regional API endpoints may have different validation behavior. If you're using region-specific endpoints (common with Azure OpenAI), the validation rules may vary slightly between regions. An error you encounter on one regional endpoint might not occur on another. While this shouldn't happen in theory, practical experience shows minor differences exist. If you have access to multiple regions, testing across them can help determine if the issue is region-specific or universal.

FAQ

What does "sentinel_block" mean in a Sora 2 error?

Sentinel_block indicates that your prompt or input triggered the pre-generation content filter. This happens before any video generation begins. The filter analyzes your text prompt and input image for potentially problematic content. To fix this, rephrase your prompt using more neutral language, avoid references to real people or sensitive topics, and ensure input images don't contain restricted content. This is not a technical error with your parameters—your request format is correct, but the content needs modification.

Why does my request work sometimes but fail other times?

Intermittent 400 errors typically indicate one of three issues: variable input data that sometimes triggers validation and sometimes doesn't, race conditions in your code that occasionally send incomplete data, or server-side changes in validation rules. Log your full request payloads to identify what differs between successful and failed attempts. If the requests are identical, the API's content moderation may have variable sensitivity thresholds, in which case slight prompt modifications can help.

Can I retry a 400 error, or should I fix it first?

Always fix first. A 400 error indicates a permanent problem with that specific request. Retrying the exact same request will always fail. However, if you fix the issue and get a different error (like 429 rate limit), that new error may be retriable. The only exception is if you believe the error is incorrectly returned by an unstable API gateway, in which case a single retry with a delay is reasonable before investigating further.

How do I know if my API key is the problem?

API key issues typically return 401 Unauthorized, not 400 Bad Request. However, hidden characters in API keys can cause 400 errors if the malformed key prevents proper request parsing. To test, try your API key with the simplest possible request (just a prompt, no files or optional parameters). If that works, your key is fine. If you still get 400 with a minimal request, manually retype your API key to eliminate hidden characters.

What's the difference between error codes 400, 1004, 1002, and 1006?

Error 400 (HTTP status) means invalid request parameters. Error 1004 (API response code) indicates generation failed after starting, usually due to content that passed initial moderation but failed during generation. Error 1002 means the generation queue is full—this is transient and should be retried. Error 1006 indicates prompt rejection at a deeper validation layer than sentinel_block. For 400 and 1006, fix your request. For 1002, retry with backoff. For 1004, modify your content approach.

How can I test my requests without using production credits?

Use a local validation layer to catch parameter errors before sending. For more comprehensive testing, some API providers offer sandbox environments or validation-only endpoints. You can also use the minimal request approach: test with the shortest duration (5 seconds) and simplest prompt to verify your integration works, then scale up parameters once confirmed. This minimizes credit usage during debugging.