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GPT-4o Audio vs GPT-5.3-Codex

GPT-4o Audio (2024) and GPT-5.3-Codex (2026) are agentic coding models from OpenAI. GPT-4o Audio ships a 128K-token context window, while GPT-5.3-Codex ships a 400K-token context window. On pricing, GPT-5.3-Codex costs $1.75/1M input tokens versus $2.5/1M 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-5.3-Codex is ~43% cheaper at $1.75/1M; pay for GPT-4o Audio only for provider fit.

Decision scorecard

Local evidence first
SignalGPT-4o AudioGPT-5.3-Codex
Decision fitLong contextCoding, RAG, and Agents
Context window128K400K
Cheapest output$10/1M tokens$14/1M tokens
Provider routes1 tracked2 tracked
Shared benchmarks0 rows0 rows

Decision tradeoffs

Choose GPT-4o Audio when...
  • GPT-4o Audio has the lower cheapest tracked output price at $10/1M tokens.
  • Local decision data tags GPT-4o Audio for Long context.
Choose GPT-5.3-Codex when...
  • GPT-5.3-Codex has the larger context window for long prompts, retrieval packs, or transcript analysis.
  • GPT-5.3-Codex has broader tracked provider coverage for fallback and procurement flexibility.
  • GPT-5.3-Codex uniquely exposes Vision, Reasoning, and Function calling in local model data.
  • Local decision data tags GPT-5.3-Codex for Coding, RAG, and Agents.

Monthly cost at traffic

Estimate token spend from the cheapest tracked input and output prices on this page.

Lower estimate GPT-4o Audio

GPT-4o Audio

$4,500

Cheapest tracked route: OpenRouter

GPT-5.3-Codex

$4,900

Cheapest tracked route: OpenRouter

Estimated monthly gap: $400. Batch, cache, and negotiated pricing are excluded from this local estimate.

Switch friction

GPT-4o Audio -> GPT-5.3-Codex
  • Provider overlap exists on OpenRouter; start route-level A/B tests there.
  • GPT-5.3-Codex is $4/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
  • GPT-5.3-Codex adds Vision, Reasoning, and Function calling in local capability data.
GPT-5.3-Codex -> GPT-4o Audio
  • Provider overlap exists on OpenRouter; start route-level A/B tests there.
  • GPT-4o Audio is $4/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
  • Check replacement coverage for Vision, Reasoning, and Function calling before moving production traffic.

Specs

Specification
Released2024-10-012026-02-05
Context window128K400K
Parameters
Architecturedecoder onlydecoder only
LicenseUnknownProprietary
Knowledge cutoff-2025-08

Pricing and availability

Pricing attributeGPT-4o AudioGPT-5.3-Codex
Input price$2.5/1M tokens$1.75/1M tokens
Output price$10/1M tokens$14/1M tokens
Providers

Capabilities

CapabilityGPT-4o AudioGPT-5.3-Codex
VisionNoYes
MultimodalNoNo
ReasoningNoYes
Function callingNoYes
Tool useNoYes
Structured outputsNoYes
Code executionNoYes

Benchmarks

No shared benchmark rows are currently sourced for this pair.

Deep dive

The capability footprint differs most on vision: GPT-5.3-Codex, reasoning mode: GPT-5.3-Codex, function calling: GPT-5.3-Codex, tool use: GPT-5.3-Codex, structured outputs: GPT-5.3-Codex, and code execution: GPT-5.3-Codex. 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 GPT-5.3-Codex lists $1.75/1M input and $14/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts GPT-4o Audio lower by about $0.67 per million blended tokens. Availability is 1 providers versus 2, so concentration risk also matters.

Choose GPT-4o Audio when provider fit are central to the workload. Choose GPT-5.3-Codex when coding workflow support, larger context windows, and lower input-token cost 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 GPT-5.3-Codex?

GPT-5.3-Codex supports 400K tokens, while GPT-4o Audio supports 128K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.

Which is cheaper, GPT-4o Audio or GPT-5.3-Codex?

GPT-5.3-Codex is cheaper on tracked token pricing. GPT-4o Audio costs $2.5/1M input and $10/1M output tokens. GPT-5.3-Codex costs $1.75/1M input and $14/1M output tokens. Provider discounts or batch pricing can still change the final bill.

Is GPT-4o Audio or GPT-5.3-Codex open source?

GPT-4o Audio is listed under Unknown. GPT-5.3-Codex 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 vision, GPT-4o Audio or GPT-5.3-Codex?

GPT-5.3-Codex has the clearer documented vision signal in this comparison. If vision is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.

Which is better for reasoning mode, GPT-4o Audio or GPT-5.3-Codex?

GPT-5.3-Codex 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.

Where can I run GPT-4o Audio and GPT-5.3-Codex?

GPT-4o Audio is available on OpenRouter. GPT-5.3-Codex is available on OpenRouter 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.

Continue comparing

Last reviewed: 2026-05-11. Data sourced from public model cards and provider documentation.