Model comparison
GPT-5.3 Codex vs GPT-5.5 Pro
Head-to-head evidence from 1 shared benchmark result across 1 category. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GPT-5.3 Codex #26 (Supported); GPT-5.5 Pro #38 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GPT-5.3 Codex and GPT-5.5 Pro share 1 comparable benchmark result. 1 of 8 categories are comparable. 20 results are unique to GPT-5.3 Codex; 6 to GPT-5.5 Pro.
Updated July 23, 2026- Shared results
- 1
- GPT-5.3 Codex only
- 20
- GPT-5.5 Pro only
- 6
- Comparable categories
- 1 / 8
Pick GPT-5.3 Codex if you want the stronger benchmark profile. GPT-5.5 Pro only becomes the better choice if agentic is the priority or you need the larger 1M context window.
Confidence note. This is a partial-evidence comparison with 1 shared benchmark result across 1 evidence category; 1 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
GPT-5.3 Codex has the cleaner BenchAlign overall profile here, landing at 66.69 versus 63.69. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
GPT-5.5 Pro is also the more expensive model on tokens at $30.00 input / $180.00 output per 1M tokens, versus $1.75 input / $14.00 output per 1M tokens for GPT-5.3 Codex. That is roughly 12.9x on output cost alone. GPT-5.5 Pro gives you the larger context window at 1M, 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-5.5 Pro |
|---|---|---|---|
| Agentic | GPT-5.3 Codex71.4 | Margin→ 18.7 | GPT-5.5 Pro90.1 |
| Coding | GPT-5.3 Codex67.2 | MarginNo overlap | GPT-5.5 ProNot measured |
| Knowledge | GPT-5.3 CodexNot measured | MarginNo overlap | GPT-5.5 Pro57.2 |
| Math | GPT-5.3 CodexNot measured | MarginNo overlap | GPT-5.5 Pro48.1 |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GPT-5.3 Codex | GPT-5.5 Pro | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-5.3 Codex$1.75 input / $14 output | GPT-5.5 Pro$30 input / $180 output | GPT-5.3 Codex has the lower combined listed price. |
| Generation speedtokens per second | GPT-5.3 Codex79 tok/s | GPT-5.5 ProNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GPT-5.3 Codex88.26 s | GPT-5.5 ProNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GPT-5.3 Codex400K | GPT-5.5 Pro1M | GPT-5.5 Pro lists the larger context window. |
Benchmark Deep Dive
AgenticGPT-5.5 Pro wins6 benchmarks
Coding5 benchmarks
Reasoning2 benchmarks
Knowledge8 benchmarks
| Benchmark | GPT-5.3 Codex | GPT-5.5 Pro | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 44.3% | — | Not comparable |
| AA-GPQA DiamondSource | 91.5% | — | Not comparable |
| AA-HLESource | 39.9% | — | Not comparable |
| AA-Omniscience IndexSource | 9.9% | — | Not comparable |
| AA-Omniscience AccuracySource | 51.8% | — | Not comparable |
| AA-Omniscience Hallucination RateSource | 86.9% | — | Not comparable |
| HLESource | — | 57.2% | Not comparable |
| HLE w/o toolsSource | — | 43.1% | Not comparable |
Math3 benchmarks
Multimodal2 benchmarks
Inst. Following1 benchmarks
| Benchmark | GPT-5.3 Codex | GPT-5.5 Pro | Result |
|---|---|---|---|
| AA-IFBenchSource | 75.4% | — | Not comparable |
Frequently Asked Questions (2)
Which is better, GPT-5.3 Codex or GPT-5.5 Pro?
GPT-5.3 Codex is ahead on BenchLM's BenchAlign leaderboard, 66.69 to 63.69.
Which is better for agentic tasks, GPT-5.3 Codex or GPT-5.5 Pro?
GPT-5.5 Pro has the edge for agentic tasks in this comparison, averaging 90.1 versus 71.4. GPT-5.3 Codex stays close enough that the answer can still flip depending on your workload.
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