OpenAI offers two text-to-speech models with different trade-offs between quality, speed, and cost. The standard tts-1 model is optimized for real-time applications with lower latency, while tts-1-hd delivers higher audio fidelity for production content. This comprehensive comparison will help you understand the technical differences and make an informed choice for your specific use case.
Listen to the same text rendered by both models. Technical content is used to highlight quality differences in pronunciation and clarity.
Key technical details for both OpenAI TTS models.
| Specification | tts-1 | tts-1-hd |
|---|---|---|
| Available Voices | 9 voices (Alloy, Ash, Coral, Echo, Fable, Nova, Onyx, Sage, Shimmer) | Same 9 voices |
| Speed Range | 0.25x to 4.0x | 0.25x to 4.0x |
| Output Formats | MP3, WAV, OPUS, AAC, FLAC, PCM | MP3, WAV, OPUS, AAC, FLAC, PCM |
| Max Input Length | 4,096 characters per request | 4,096 characters per request |
| Streaming Support | Yes (optimized) | Yes |
| Typical Latency (TTFB) | <200ms | 300-500ms |
| Pricing | $15 per 1M characters | $30 per 1M characters |
| Sample Rate | 24kHz | 24kHz (higher bitrate encoding) |
tts-1-hd produces noticeably cleaner audio with better high-frequency response. Sibilants (s, sh, ch sounds) are crisper and there's less compression artifacting. The difference is most apparent in technical speech, names, and content with complex phonetics.
tts-1 is still high quality for most applications, but on careful listening you may notice slight muddiness in complex audio passages or faint digital artifacts. For most users listening through phone speakers or laptop audio, the difference is minimal.
The quality gap becomes more pronounced in professional audio contexts: studio headphones, high-end speakers, or quiet listening environments. Content that will be consumed in these settings benefits most from the HD model. Conversely, content consumed in noisy environments (commuting, gym) may not need HD quality.
tts-1 is optimized for real-time streaming. Time-to-first-byte is typically under 200ms, making it suitable for conversational AI and live applications where users expect immediate responses.
tts-1-hd takes longer to generate. For short clips the difference is minor, but for longer content expect noticeably slower processing. The HD model is better suited for pre-rendered content where latency doesn't impact user experience.
For streaming applications, the standard model's faster TTFB means users hear audio begin sooner, creating a more responsive experience. The HD model's additional processing time is worthwhile when content will be cached or delivered asynchronously.
The HD model costs exactly twice as much: $30 per million characters compared to $15 for the standard model. This pricing difference can significantly impact your budget at scale, so understanding your volume requirements is essential for making the right choice.
Here are real-world cost examples across different content types and scales:
| Content Type | Characters | tts-1 | tts-1-hd |
|---|---|---|---|
| Short notification | ~100 | $0.0015 | $0.003 |
| Chatbot response (avg) | ~300 | $0.0045 | $0.009 |
| Blog post (1,000 words) | ~5,500 | $0.08 | $0.16 |
| 5-minute video script | ~7,500 | $0.11 | $0.22 |
| 10-minute podcast script | ~15,000 | $0.23 | $0.45 |
| 30-minute e-learning module | ~45,000 | $0.68 | $1.35 |
| Audiobook chapter | ~40,000 | $0.60 | $1.20 |
| Full audiobook (80,000 words) | ~440,000 | $6.60 | $13.20 |
| 10,000 chatbot responses/month | ~3,000,000 | $45.00 | $90.00 |
Common questions about choosing between OpenAI's TTS models.
Yes, switching between models is trivial. The only difference in the API call is the model name parameter - change tts-1 to tts-1-hd and everything else remains the same. This makes it easy to use the standard model for development and testing, then switch to HD for production content. You can also A/B test both models with real users to measure whether the quality difference impacts your specific metrics before committing to the higher cost.
Yes, all 9 voices (Alloy, Ash, Coral, Echo, Fable, Nova, Onyx, Sage, Shimmer) are available in both models with identical voice characteristics. The tone, personality, and speaking style remain consistent between tts-1 and tts-1-hd - only the audio fidelity differs. This consistency means you can confidently test voice selection with the cheaper model and know the voice will sound the same (just higher quality) when you switch to HD for production.
It depends on the listening context. For casual listening through phone speakers or laptop audio, many users won't notice a significant difference. The gap becomes more apparent with headphones, on high-quality speakers, or in quiet environments where subtle audio artifacts are more perceptible. Technical content with complex pronunciation (names, numbers, abbreviations) and content with many sibilants (s, sh, ch sounds) tends to reveal the quality difference more clearly than simple conversational text. If your users will consume content in professional audio settings, the HD model is worth the investment.
Both models handle speed adjustments (0.25x to 4.0x) using similar algorithms, so the relative quality difference remains consistent across speeds. At extreme speed settings (below 0.5x or above 2.0x), you may notice more artifacts in the standard model, but for normal speed ranges (0.75x to 1.5x) the proportional difference is minimal. If you plan to use extreme speeds, testing with both models is advisable, as the HD model's higher baseline quality provides more headroom for speed manipulation.
Both models support the same output formats: MP3, WAV, OPUS, AAC, FLAC, and PCM. The format choice doesn't affect the quality difference between models - the HD model produces higher-quality audio regardless of output format. For most web applications, MP3 or AAC provides good compression with broad compatibility. For archival or professional production, WAV or FLAC preserves full quality. OPUS offers excellent compression efficiency for streaming applications.
Start with tts-1 for most applications - it provides excellent quality for the vast majority of use cases. Consider upgrading to tts-1-hd if: (1) your users consume content in high-quality audio environments like studios or with premium headphones, (2) you're producing content that will be published professionally (podcasts, audiobooks, commercials), (3) the content has complex pronunciation requirements, or (4) quality is a competitive differentiator for your product. For real-time applications like voice assistants and chatbots, the standard model's lower latency is often more valuable than the HD model's quality improvement.
Absolutely - this is a smart cost-optimization strategy. Many applications use the standard model for ephemeral content (real-time responses, notifications, drafts) and the HD model for persistent content (published podcasts, marketing videos, e-learning modules). Since switching models is just a parameter change, you can implement routing logic to select the appropriate model based on content type, user tier, or output destination. This hybrid approach lets you optimize both cost and quality where each matters most.
For content longer than 4,096 characters, you'll need to split your text into chunks and make multiple API calls. Both models have the same limit, so your chunking strategy works identically for either. Best practice is to split at natural boundaries (sentence or paragraph breaks) to avoid awkward audio transitions. For seamless playback, generate all chunks, then concatenate the audio files. Consider adding brief silence between chunks to create natural-sounding pauses. The character limit is per-request, not per-project, so there's no limit on total content length.