Model comparison
DeepSeek V3.2 vs GPT-5.3 Codex
Head-to-head evidence from 14 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: DeepSeek V3.2 #82 (Supported); GPT-5.3 Codex #26 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. DeepSeek V3.2 and GPT-5.3 Codex share 14 comparable benchmark results. 1 of 8 categories are comparable. 5 results are unique to DeepSeek V3.2; 7 to GPT-5.3 Codex.
Updated July 20, 2026- Shared results
- 14
- DeepSeek V3.2 only
- 5
- GPT-5.3 Codex only
- 7
- Comparable categories
- 1 / 8
Pick GPT-5.3 Codex if you want the stronger benchmark profile. DeepSeek V3.2 only becomes the better choice if you want the cheaper token bill or you would rather avoid the extra latency and token burn of a reasoning model.
Confidence note. This is a partial-evidence comparison with 14 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 55.4. 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 60.9. The single biggest benchmark swing on the page is SWE-Rebench, 60.9% to 58.2%.
GPT-5.3 Codex is also the more expensive model on tokens at $1.75 input / $14.00 output per 1M tokens, versus $0.28 input / $0.42 output per 1M tokens for DeepSeek V3.2. That is roughly 33.3x on output cost alone. GPT-5.3 Codex is the reasoning model in the pair, while DeepSeek V3.2 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. GPT-5.3 Codex gives you the larger context window at 400K, compared with 128K for DeepSeek V3.2.
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 | DeepSeek V3.2 | Δ | GPT-5.3 Codex |
|---|---|---|---|
| Coding | DeepSeek V3.260.9 | Margin→ 6.3 | GPT-5.3 Codex67.2 |
| Agentic | DeepSeek V3.2Not measured | MarginNo overlap | GPT-5.3 Codex71.4 |
| Math | DeepSeek V3.217.1 | 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-Rebench
CodingA 60.9%B 58.2%Winner: DeepSeek V3.2Δ 2.7SWE-Rebench: DeepSeek V3.2 scored 60.9%; GPT-5.3 Codex scored 58.2%. DeepSeek V3.2 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | DeepSeek V3.2 | GPT-5.3 Codex | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | DeepSeek V3.2$0.28 input / $0.42 output | GPT-5.3 Codex$1.75 input / $14 output | DeepSeek V3.2 has the lower combined listed price. |
| Generation speedtokens per second | DeepSeek V3.235 tok/s | GPT-5.3 Codex79 tok/s | GPT-5.3 Codex has the higher measured throughput. |
| First-answer latencyseconds to first token | DeepSeek V3.23.75 s | GPT-5.3 Codex88.26 s | DeepSeek V3.2 reaches the first token sooner. |
| Context windowmaximum listed tokens | DeepSeek V3.2128K | GPT-5.3 Codex400K | GPT-5.3 Codex lists the larger context window. |
Benchmark Deep Dive
Agentic7 benchmarks
| Benchmark | DeepSeek V3.2 | GPT-5.3 Codex | Result |
|---|---|---|---|
| Claw-EvalSource | 40.2% | — | Not comparable |
| VITA-BenchSource | 18.5% | — | Not comparable |
| τ²-bench resultsSource | 78.9% | 86% | GPT-5.3 Codex leads |
| Gert LabsSource | 29.57% | 57.47% | GPT-5.3 Codex leads |
| 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 | DeepSeek V3.2 | GPT-5.3 Codex | Result |
|---|---|---|---|
| SWE-RebenchSource | 60.9% | 58.2% | DeepSeek V3.2 leads |
| React Native EvalsSource | 71.5% | — | Not comparable |
| AA-SciCodeSource | 38.7% | 53.2% | GPT-5.3 Codex leads |
| SWE-bench VerifiedSource | — | 85% | Not comparable |
| SWE-bench ProSource | — | 56.8% | Not comparable |
| Vibe Code BenchSource | — | 61.77% | Not comparable |
Reasoning2 benchmarks
Knowledge6 benchmarks
| Benchmark | DeepSeek V3.2 | GPT-5.3 Codex | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 24.7% | 44.3% | GPT-5.3 Codex leads |
| AA-GPQA DiamondSource | 75.1% | 91.5% | GPT-5.3 Codex leads |
| AA-HLESource | 10.5% | 39.9% | GPT-5.3 Codex leads |
| AA-Omniscience IndexSource | -46.7% | 9.9% | GPT-5.3 Codex leads |
| AA-Omniscience AccuracySource | 24.2% | 51.8% | GPT-5.3 Codex leads |
| AA-Omniscience Hallucination RateSource | 93.5% | 86.9% | GPT-5.3 Codex leads |
Math2 benchmarks
Multimodal2 benchmarks
Inst. Following1 benchmarks
| Benchmark | DeepSeek V3.2 | GPT-5.3 Codex | Result |
|---|---|---|---|
| AA-IFBenchSource | 49.0% | 75.4% | GPT-5.3 Codex leads |
Frequently Asked Questions (2)
Which is better, DeepSeek V3.2 or GPT-5.3 Codex?
GPT-5.3 Codex is ahead on BenchLM's BenchAlign leaderboard, 66.69 to 55.4. The biggest single separator in this matchup is SWE-Rebench, where the scores are 60.9% and 58.2%.
Which is better for coding, DeepSeek V3.2 or GPT-5.3 Codex?
GPT-5.3 Codex has the edge for coding in this comparison, averaging 67.2 versus 60.9. Inside this category, AA-SciCode is the benchmark that creates the most daylight between them.
Related Comparisons
Explore More
Choose a model with this week’s evidence
Join 2,000+ readers for ranking moves, pricing changes, and the claims that still need proof.
One email each week. Unsubscribe anytime.