Model comparison
Gemini 2.5 Pro vs GPT-5.3 Codex
Head-to-head evidence from 16 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Gemini 2.5 Pro #70 (Supported); GPT-5.3 Codex #26 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Gemini 2.5 Pro and GPT-5.3 Codex share 16 comparable benchmark results. 1 of 8 categories are comparable. 8 results are unique to Gemini 2.5 Pro; 5 to GPT-5.3 Codex.
Updated July 20, 2026- Shared results
- 16
- Gemini 2.5 Pro only
- 8
- GPT-5.3 Codex only
- 5
- Comparable categories
- 1 / 8
Pick GPT-5.3 Codex if you want the stronger benchmark profile. Gemini 2.5 Pro only becomes the better choice if you want the cheaper token bill or you need the larger 1M context window.
Confidence note. This is a partial-evidence comparison with 16 shared benchmark results across 6 evidence categories; 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 is clearly ahead on the BenchAlign aggregate, 66.69 to 57.25. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.3 Codex's sharpest advantage is in coding, where it averages 67.2 against 63.8. The single biggest benchmark swing on the page is SWE-bench Verified, 63.8% to 85%.
GPT-5.3 Codex is also the more expensive model on tokens at $1.75 input / $14.00 output per 1M tokens, versus $1.25 input / $10.00 output per 1M tokens for Gemini 2.5 Pro. GPT-5.3 Codex is the reasoning model in the pair, while Gemini 2.5 Pro is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. Gemini 2.5 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 | Gemini 2.5 Pro | Δ | GPT-5.3 Codex |
|---|---|---|---|
| Coding | Gemini 2.5 Pro63.8 | Margin→ 3.4 | GPT-5.3 Codex67.2 |
| Agentic | Gemini 2.5 ProNot measured | MarginNo overlap | GPT-5.3 Codex71.4 |
| Knowledge | Gemini 2.5 Pro27.4 | MarginNo overlap | GPT-5.3 CodexNot measured |
| Math | Gemini 2.5 Pro11.6 | MarginNo overlap | GPT-5.3 CodexNot measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
SWE-bench Verified
CodingA 63.8%B 85%Winner: GPT-5.3 CodexΔ 21.2SWE-bench Verified: Gemini 2.5 Pro scored 63.8%; GPT-5.3 Codex scored 85%. GPT-5.3 Codex wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Gemini 2.5 Pro | GPT-5.3 Codex | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Gemini 2.5 Pro$1.25 input / $10 output | GPT-5.3 Codex$1.75 input / $14 output | Gemini 2.5 Pro has the lower combined listed price. |
| Generation speedtokens per second | Gemini 2.5 Pro117 tok/s | GPT-5.3 Codex79 tok/s | Gemini 2.5 Pro has the higher measured throughput. |
| First-answer latencyseconds to first token | Gemini 2.5 Pro21.19 s | GPT-5.3 Codex88.26 s | Gemini 2.5 Pro reaches the first token sooner. |
| Context windowmaximum listed tokens | Gemini 2.5 Pro1M | GPT-5.3 Codex400K | Gemini 2.5 Pro lists the larger context window. |
Benchmark Deep Dive
Agentic8 benchmarks
| Benchmark | Gemini 2.5 Pro | GPT-5.3 Codex | Result |
|---|---|---|---|
| AA Agentic IndexSource | 7.1% | — | Not comparable |
| τ²-bench resultsSource | 54.1% | 86% | GPT-5.3 Codex leads |
| Gert LabsSource | 42.01% | 57.47% | GPT-5.3 Codex leads |
| GDPval-AASource | 8.3% | — | Not comparable |
| GDPval-AASource | 665 | — | Not comparable |
| Terminal-Bench 2.0Source | — | 77.3% | Not comparable |
| OSWorld-VerifiedSource | — | 64.7% | Not comparable |
| JobBenchSource | — | 33.7% | Not comparable |
CodingGPT-5.3 Codex wins6 benchmarks
| Benchmark | Gemini 2.5 Pro | GPT-5.3 Codex | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 63.8% | 85% | GPT-5.3 Codex leads |
| Vibe Code BenchSource | 0.40% | 61.77% | GPT-5.3 Codex leads |
| AA Coding IndexSource | 33.3% | — | Not comparable |
| AA-SciCodeSource | 42.8% | 53.2% | GPT-5.3 Codex leads |
| SWE-bench ProSource | — | 56.8% | Not comparable |
| SWE-RebenchSource | — | 58.2% | Not comparable |
Reasoning2 benchmarks
Knowledge8 benchmarks
| Benchmark | Gemini 2.5 Pro | GPT-5.3 Codex | Result |
|---|---|---|---|
| GPQASource | 83% | — | Not comparable |
| HLESource | 18.8% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 25.8% | 44.3% | GPT-5.3 Codex leads |
| AA-GPQA DiamondSource | 84.4% | 91.5% | GPT-5.3 Codex leads |
| AA-HLESource | 21.1% | 39.9% | GPT-5.3 Codex leads |
| AA-Omniscience IndexSource | -14.3% | 9.9% | GPT-5.3 Codex leads |
| AA-Omniscience AccuracySource | 39.0% | 51.8% | GPT-5.3 Codex leads |
| AA-Omniscience Hallucination RateSource | 87.4% | 86.9% | GPT-5.3 Codex leads |
Math2 benchmarks
Multimodal2 benchmarks
Inst. Following1 benchmarks
| Benchmark | Gemini 2.5 Pro | GPT-5.3 Codex | Result |
|---|---|---|---|
| AA-IFBenchSource | 48.7% | 75.4% | GPT-5.3 Codex leads |
Frequently Asked Questions (2)
Which is better, Gemini 2.5 Pro or GPT-5.3 Codex?
GPT-5.3 Codex is ahead on BenchLM's BenchAlign leaderboard, 66.69 to 57.25. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 63.8% and 85%.
Which is better for coding, Gemini 2.5 Pro or GPT-5.3 Codex?
GPT-5.3 Codex has the edge for coding in this comparison, averaging 67.2 versus 63.8. Inside this category, Vibe Code Bench is the benchmark that creates the most daylight between them.
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