Agentic
Not comparable- GPT-4o Audio
- Not measured
- GPT-5.3 Codex
- 71.4
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Model comparison
GPT-4o Audio vs GPT-5.3 Codex
Updated August 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead.
0 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.
Which one for your work
Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.
Long documents
Prompts that approach the documented context limit
GPT-5.3 Codex
GPT-5.3 Codex has the larger documented context window.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
GPT-4o Audio
GPT-4o Audio has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Show secondary and unsupported calls
Repository review cost
50K fresh input + 3K output tokens
GPT-5.3 Codex
GPT-5.3 Codex has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
Confidence: limited
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. GPT-4o Audio does not fit this workload in one request. GPT-4o Audio has no published cached-input rate, so cached tokens use its listed input rate. GPT-5.3 Codex has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
8 GPT-5.3 Codex only
- Shared results
- 0
- GPT-4o Audio only
- 0
- GPT-5.3 Codex only
- 8
- Like-for-like categories
- 0 / 8
Category results, on a stated basis
Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.
Coding
Not comparable- GPT-4o Audio
- Not measured
- GPT-5.3 Codex
- 67.2
- Weighted basis
- 0 vs 3 rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT-4o Audio
- Not measured
- GPT-5.3 Codex
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- GPT-4o Audio
- Not measured
- GPT-5.3 Codex
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Math
Not comparable- GPT-4o Audio
- Not measured
- GPT-5.3 Codex
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-4o Audio
- Not measured
- GPT-5.3 Codex
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-4o Audio
- Not measured
- GPT-5.3 Codex
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-4o Audio
- Not measured
- GPT-5.3 Codex
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | GPT-4o Audio | GPT-5.3 Codex | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | Not measured | 71.4 | Not comparable0 vs 2 rows | Not comparable |
| Coding | Not measured | 67.2 | Not comparable0 vs 3 rows | Not comparable |
| Reasoning | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Knowledge | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Math | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Instruction following | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
Shape of the matched evidence
Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.
A shared-evidence shape is not available.
BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.
What each workload costs
Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.
Chat turn
1K fresh input + 500 output tokens
- GPT-4o Audio
- $0.0075
- Fits in one request
- GPT-5.3 Codex
- $0.00875
- Fits in one request
GPT-4o Audio has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- GPT-4o Audio
- $0.155
- Fits in one request
- GPT-5.3 Codex
- $0.1295
- Fits in one request
GPT-5.3 Codex has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- GPT-4o Audio
- $0.65
- Does not fit in one request
- Cached input priced at the published list-input rate
- GPT-5.3 Codex
- $0.525
- Fits in one request
- Cached input priced at the published list-input rate
GPT-4o Audio does not fit this workload in one request. GPT-4o Audio has no published cached-input rate, so cached tokens use its listed input rate. GPT-5.3 Codex has no published cached-input rate, so cached tokens use its listed input rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGPT-4o AudioGPT-5.3 Codex
Context window
Maximum documented context; output-token limits may be lower.
GPT-4o Audio
GPT-5.3 Codex
API model ID
GPT-4o Audio
gpt-4o-audio-preview
OpenAI model documentationGPT-5.3 Codex
gpt-5.3-codex
OpenAI GPT-5.3 Codex model documentationCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-4o Audio
Not published
OpenAI model documentationGPT-5.3 Codex
Not published
Documented inputs
GPT-4o Audio
Not sourced
GPT-5.3 Codex
text, image
OpenAI model catalogDocumented outputs
GPT-4o Audio
Not sourced
GPT-5.3 Codex
Provider availability
GPT-4o Audio
Not sourced
GPT-5.3 Codex
Generally Available · OpenAI Responses API
OpenAI model catalogReasoning profile
GPT-4o Audio
Non-Reasoning
GPT-5.3 Codex
Reasoning
Weight access
GPT-4o Audio
Proprietary
GPT-5.3 Codex
Proprietary
License
GPT-4o Audio
Proprietary
GPT-5.3 Codex
Proprietary
Release date
GPT-4o Audio
Not sourced
GPT-5.3 Codex
2026-02-05
If you already use one of these models
- Deployment change
- Both entries list OpenAI as the provider. Confirm endpoint, model ID, limits, and feature support before switching.
- Quality signal
- The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
- Workload cost
- Repository review: $0.155 vs $0.1295. Cache-heavy agent loop: $0.65 vs $0.525.
- Context tradeoff
- GPT-5.3 Codex has the larger documented window (400K).
Run the same representative tasks against both endpoints before changing production traffic.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence8 rows
Agentic
Coding
SWE-bench Verified
GPT-4o Audio—GPT-5.3 Codex85%SourceNot directly comparable
SWE-bench Pro
GPT-4o Audio—GPT-5.3 Codex56.8%SourceNot directly comparable
SWE-Rebench
GPT-4o Audio—GPT-5.3 Codex58.2%SourceNot directly comparable
Vibe Code Bench
GPT-4o Audio—GPT-5.3 Codex61.77%SourceNot directly comparable
Frequently asked questions
Which is better, GPT-4o Audio or GPT-5.3 Codex?
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, GPT-4o Audio or GPT-5.3 Codex?
The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.
Which is better for agentic tasks, GPT-4o Audio or GPT-5.3 Codex?
The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.
Which costs less, GPT-4o Audio or GPT-5.3 Codex?
For the stated presets, chat costs $0.0075 on GPT-4o Audio and $0.00875 on GPT-5.3 Codex; repository review costs $0.155 and $0.1295; the cache-heavy agent loop costs $0.65 and $0.525. GPT-4o Audio does not fit this workload in one request. GPT-4o Audio has no published cached-input rate, so cached tokens use its listed input rate. GPT-5.3 Codex has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, GPT-4o Audio or GPT-5.3 Codex?
GPT-5.3 Codex has the larger documented context window: 400K, compared with 128K.
Related comparisons
Last updated August 4, 2026
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