GPT-4o Audio vs o1 (12-17)
GPT-4o Audio (2024) and o1 (12-17) (2024) are frontier reasoning models from OpenAI. GPT-4o Audio ships a 128K-token context window, while o1 (12-17) ships a 128K-token context window. On pricing, GPT-4o Audio costs $2.5/1M input tokens versus $15/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing.
GPT-4o Audio is ~500% cheaper at $2.5/1M; pay for o1 (12-17) only for coding workflow support.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-10-01 | 2024-12-17 |
| Context window | 128K | 128K |
| Parameters | — | — |
| Architecture | decoder only | decoder only |
| License | Unknown | Proprietary |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | GPT-4o Audio | o1 (12-17) |
|---|---|---|
| Input price | $2.5/1M tokens | $15/1M tokens |
| Output price | $10/1M tokens | $60/1M tokens |
| Providers |
Capabilities
| Capability | GPT-4o Audio | o1 (12-17) |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | Yes |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | No | No |
| Code execution | No | Yes |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on reasoning mode: o1 (12-17) and code execution: o1 (12-17). Both models share the core language-model surface, so the practical split is not just feature count. Use those differences to decide whether the page is about raw model quality, agentic coding support, multimodal ingestion, or predictable structured API behavior.
For cost, GPT-4o Audio lists $2.5/1M input and $10/1M output tokens, while o1 (12-17) lists $15/1M input and $60/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts GPT-4o Audio lower by about $23.75 per million blended tokens. Availability is 1 providers versus 2, so concentration risk also matters.
Choose GPT-4o Audio when provider fit and lower input-token cost are central to the workload. Choose o1 (12-17) when coding workflow support and broader provider choice are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship. This keeps the decision grounded in measurable tradeoffs instead of brand-level assumptions. It also helps separate model capability from provider packaging, which can change cost and latency.
FAQ
Which has a larger context window, GPT-4o Audio or o1 (12-17)?
GPT-4o Audio supports 128K tokens, while o1 (12-17) supports 128K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Which is cheaper, GPT-4o Audio or o1 (12-17)?
GPT-4o Audio is cheaper on tracked token pricing. GPT-4o Audio costs $2.5/1M input and $10/1M output tokens. o1 (12-17) costs $15/1M input and $60/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is GPT-4o Audio or o1 (12-17) open source?
GPT-4o Audio is listed under Unknown. o1 (12-17) is listed under Proprietary. License labels affect whether you can self-host, redistribute weights, or rely only on hosted APIs, so confirm the upstream license before deployment.
Which is better for reasoning mode, GPT-4o Audio or o1 (12-17)?
o1 (12-17) has the clearer documented reasoning mode signal in this comparison. If reasoning mode is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for code execution, GPT-4o Audio or o1 (12-17)?
o1 (12-17) has the clearer documented code execution signal in this comparison. If code execution is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Where can I run GPT-4o Audio and o1 (12-17)?
GPT-4o Audio is available on OpenRouter. o1 (12-17) is available on Replicate API and OpenAI API. Provider coverage can affect latency, region availability, compliance posture, and fallback options. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
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Last reviewed: 2026-05-11. Data sourced from public model cards and provider documentation.