Model comparison
GPT-5.3 Codex vs GPT-5.4
Head-to-head evidence from 19 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GPT-5.3 Codex #26 (Supported); GPT-5.4 #8 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GPT-5.3 Codex and GPT-5.4 share 19 comparable benchmark results. 2 of 8 categories are comparable. 2 results are unique to GPT-5.3 Codex; 33 to GPT-5.4.
Updated July 20, 2026- Shared results
- 19
- GPT-5.3 Codex only
- 2
- GPT-5.4 only
- 33
- Comparable categories
- 2 / 8
Pick GPT-5.4 if you want the stronger benchmark profile. GPT-5.3 Codex only becomes the better choice if coding is the priority or you want the cheaper token bill.
Confidence note. This is a partial-evidence comparison with 19 shared benchmark results across 6 evidence categories; 2 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
GPT-5.4 is clearly ahead on the BenchAlign aggregate, 74.24 to 66.69. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.4's sharpest advantage is in agentic, where it averages 77.2 against 71.4. The single biggest benchmark swing on the page is OSWorld-Verified, 64.7% to 75%. GPT-5.3 Codex does hit back in coding, so the answer changes if that is the part of the workload you care about most.
GPT-5.4 is also the more expensive model on tokens at $2.50 input / $15.00 output per 1M tokens, versus $1.75 input / $14.00 output per 1M tokens for GPT-5.3 Codex. GPT-5.4 gives you the larger context window at 1.05M, compared with 400K for GPT-5.3 Codex.
Category breakdown
Exact category averages are shown below. Not measured means BenchLM does not have enough sourced public coverage for that model and category.
| Category | GPT-5.3 Codex | Δ | GPT-5.4 |
|---|---|---|---|
| Coding | GPT-5.3 Codex67.2 | Margin← 9.5 | GPT-5.457.7 |
| Agentic | GPT-5.3 Codex71.4 | Margin→ 5.8 | GPT-5.477.2 |
| Knowledge | GPT-5.3 CodexNot measured | MarginNo overlap | GPT-5.457.6 |
| Math | GPT-5.3 CodexNot measured | MarginNo overlap | GPT-5.442.5 |
| Multimodal | GPT-5.3 CodexNot measured | MarginNo overlap | GPT-5.473.2 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
OSWorld-Verified
AgenticA 64.7%B 75%Winner: GPT-5.4Δ 10.3OSWorld-Verified: GPT-5.3 Codex scored 64.7%; GPT-5.4 scored 75%. GPT-5.4 wins this benchmark. - Source ↗
Terminal-Bench 2.0
AgenticA 77.3%B 75.1%Winner: GPT-5.3 CodexΔ 2.2Terminal-Bench 2.0: GPT-5.3 Codex scored 77.3%; GPT-5.4 scored 75.1%. GPT-5.3 Codex wins this benchmark. - Source ↗
SWE-bench Pro
CodingA 56.8%B 57.7%Winner: GPT-5.4Δ 0.9SWE-bench Pro: GPT-5.3 Codex scored 56.8%; GPT-5.4 scored 57.7%. GPT-5.4 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GPT-5.3 Codex | GPT-5.4 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-5.3 Codex$1.75 input / $14 output | GPT-5.4$2.5 input / $15 output | GPT-5.3 Codex has the lower combined listed price. |
| Generation speedtokens per second | GPT-5.3 Codex79 tok/s | GPT-5.474 tok/s | GPT-5.3 Codex has the higher measured throughput. |
| First-answer latencyseconds to first token | GPT-5.3 Codex88.26 s | GPT-5.4151.79 s | GPT-5.3 Codex reaches the first token sooner. |
| Context windowmaximum listed tokens | GPT-5.3 Codex400K | GPT-5.41.05M | GPT-5.4 lists the larger context window. |
Benchmark Deep Dive
AgenticGPT-5.4 wins17 benchmarks
| Benchmark | GPT-5.3 Codex | GPT-5.4 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 77.3% | 75.1% | GPT-5.3 Codex leads |
| OSWorld-VerifiedSource | 64.7% | 75% | GPT-5.4 leads |
| τ²-bench resultsSource | 86% | 87.1% | GPT-5.4 leads |
| Gert LabsSource | 57.47% | 64.89% | GPT-5.4 leads |
| JobBenchSource | 33.7% | 38.9% | GPT-5.4 leads |
| CyberGymSource | — | 79.0% | Not comparable |
| BrowseCompSource | — | 82.7% | Not comparable |
| MCP AtlasSource | — | 70.6% | Not comparable |
| ToolathlonSource | — | 54.6% | Not comparable |
| Claw-EvalSource | — | 60.3% | Not comparable |
| DeepSearchQASource | — | 73.6% | Not comparable |
| AA Agentic IndexSource | — | 41.1% | Not comparable |
| APEX-Agents-AASource | — | 33.3% | Not comparable |
| GDPval-AASource | — | 44.7% | Not comparable |
| GDPval-AASource | — | 1395 | Not comparable |
| ResearchClawBenchSource | — | 15.3% | Not comparable |
| ExploitGymSource | — | 6.0% | Not comparable |
CodingGPT-5.3 Codex wins8 benchmarks
| Benchmark | GPT-5.3 Codex | GPT-5.4 | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 85% | — | Not comparable |
| SWE-bench ProSource | 56.8% | 57.7% | GPT-5.4 leads |
| SWE-RebenchSource | 58.2% | — | Not comparable |
| Vibe Code BenchSource | 61.77% | 67.42% | GPT-5.4 leads |
| AA-SciCodeSource | 53.2% | 56.6% | GPT-5.4 leads |
| LiveCodeBench ProSource | — | 87.5% | Not comparable |
| React Native EvalsSource | — | 85.3% | Not comparable |
| AA Coding IndexSource | — | 71.0% | Not comparable |
Reasoning2 benchmarks
Knowledge13 benchmarks
| Benchmark | GPT-5.3 Codex | GPT-5.4 | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 44.3% | 51.4% | GPT-5.4 leads |
| AA-GPQA DiamondSource | 91.5% | 92.0% | GPT-5.4 leads |
| AA-HLESource | 39.9% | 41.6% | GPT-5.4 leads |
| AA-Omniscience IndexSource | 9.9% | 5.7% | GPT-5.3 Codex leads |
| AA-Omniscience AccuracySource | 51.8% | 50.0% | GPT-5.3 Codex leads |
| AA-Omniscience Hallucination RateSource | 86.9% | 88.6% | GPT-5.3 Codex leads |
| GPQASource | — | 92.8% | Not comparable |
| HLESource | — | 52.1% | Not comparable |
| HLE w/o toolsSource | — | 39.8% | Not comparable |
| GPQA-DSource | — | 92.8% | Not comparable |
| HealthBench HardSource | — | 40.1% | Not comparable |
| MedXpertQA (Text)Source | — | 59.6% | Not comparable |
| HealthBench ProfessionalSource | — | 48.1% | Not comparable |
Math2 benchmarks
Multimodal11 benchmarks
| Benchmark | GPT-5.3 Codex | GPT-5.4 | Result |
|---|---|---|---|
| AA-MMMU-ProSource | 78.5% | 78.4% | GPT-5.3 Codex leads |
| Design Arena WebsiteSource | 1195 | 1252 | GPT-5.4 leads |
| MMMU-ProSource | — | 81.2% | Not comparable |
| OfficeQA ProSource | — | 53.2% | Not comparable |
| MMMU-Pro w/ PythonSource | — | 82.1% | Not comparable |
| CharXivSource | — | 82.8% | Not comparable |
| ERQASource | — | 65.4% | Not comparable |
| SimpleVQASource | — | 61.1% | Not comparable |
| ScreenSpot ProSource | — | 85.4% | Not comparable |
| ZeroBenchSource | — | 41.0% | Not comparable |
| MedXpertQA (MM)Source | — | 77.1% | Not comparable |
Inst. Following1 benchmarks
| Benchmark | GPT-5.3 Codex | GPT-5.4 | Result |
|---|---|---|---|
| AA-IFBenchSource | 75.4% | 73.9% | GPT-5.3 Codex leads |
Frequently Asked Questions (3)
Which is better, GPT-5.3 Codex or GPT-5.4?
GPT-5.4 is ahead on BenchLM's BenchAlign leaderboard, 74.24 to 66.69. The biggest single separator in this matchup is OSWorld-Verified, where the scores are 64.7% and 75%.
Which is better for coding, GPT-5.3 Codex or GPT-5.4?
GPT-5.3 Codex has the edge for coding in this comparison, averaging 67.2 versus 57.7. Inside this category, Vibe Code Bench is the benchmark that creates the most daylight between them.
Which is better for agentic tasks, GPT-5.3 Codex or GPT-5.4?
GPT-5.4 has the edge for agentic tasks in this comparison, averaging 77.2 versus 71.4. Inside this category, OSWorld-Verified is the benchmark that creates the most daylight between them.
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