Model comparison
GPT-5.3 Codex vs Nemotron 3 Nano Omni 30B A3B
Head-to-head evidence from 12 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GPT-5.3 Codex #30 (Supported); Nemotron 3 Nano Omni 30B A3B #159 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GPT-5.3 Codex and Nemotron 3 Nano Omni 30B A3B share 12 comparable benchmark results. 1 of 8 categories are comparable. 9 results are unique to GPT-5.3 Codex; 17 to Nemotron 3 Nano Omni 30B A3B.
Updated July 27, 2026- Shared results
- 12
- GPT-5.3 Codex only
- 9
- Nemotron 3 Nano Omni 30B A3B only
- 17
- Comparable categories
- 1 / 8
Pick GPT-5.3 Codex if you want the stronger benchmark profile. Nemotron 3 Nano Omni 30B A3B only becomes the better choice if you want the cheaper token bill.
Confidence note. This is a partial-evidence comparison with 12 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, 65.75 to 43.32. 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 32.
GPT-5.3 Codex is also the more expensive model on tokens at $1.75 input / $14.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Nemotron 3 Nano Omni 30B A3B. That is roughly Infinityx on output cost alone. GPT-5.3 Codex gives you the larger context window at 400K, compared with 256K for Nemotron 3 Nano Omni 30B A3B.
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 | Nemotron 3 Nano Omni 30B A3B |
|---|---|---|---|
| Coding | GPT-5.3 Codex67.2 | Margin← 35.2 | Nemotron 3 Nano Omni 30B A3B32.0 |
| Agentic | GPT-5.3 Codex71.4 | MarginNo overlap | Nemotron 3 Nano Omni 30B A3BNot measured |
| Knowledge | GPT-5.3 CodexNot measured | MarginNo overlap | Nemotron 3 Nano Omni 30B A3B76.3 |
| Multimodal | GPT-5.3 CodexNot measured | MarginNo overlap | Nemotron 3 Nano Omni 30B A3B76.3 |
| Inst. Following | GPT-5.3 CodexNot measured | MarginNo overlap | Nemotron 3 Nano Omni 30B A3B74.2 |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GPT-5.3 Codex | Nemotron 3 Nano Omni 30B A3B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-5.3 Codex$1.75 input / $14 output | Nemotron 3 Nano Omni 30B A3B$0 input / $0 output | Nemotron 3 Nano Omni 30B A3B has the lower combined listed price. |
| Generation speedtokens per second | GPT-5.3 Codex79 tok/s | Nemotron 3 Nano Omni 30B A3BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GPT-5.3 Codex88.26 s | Nemotron 3 Nano Omni 30B A3BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GPT-5.3 Codex400K | Nemotron 3 Nano Omni 30B A3B256K | GPT-5.3 Codex lists the larger context window. |
Benchmark Deep Dive
Agentic8 benchmarks
| Benchmark | GPT-5.3 Codex | Nemotron 3 Nano Omni 30B A3B | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 77.3% | — | Not comparable |
| OSWorld-VerifiedSource | 64.7% | — | Not comparable |
| τ²-bench resultsSource | 86% | 45.3% | GPT-5.3 Codex leads |
| Gert LabsSource | 57.47% | — | Not comparable |
| JobBenchSource | 33.7% | — | Not comparable |
| OSWorldSource | — | 47.4% | Not comparable |
| GDPval-AASource | — | 0.0% | Not comparable |
| GDPval-AASource | — | 465 | Not comparable |
CodingGPT-5.3 Codex wins7 benchmarks
| Benchmark | GPT-5.3 Codex | Nemotron 3 Nano Omni 30B A3B | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 85% | — | Not comparable |
| SWE-bench ProSource | 56.8% | — | Not comparable |
| SWE-RebenchSource | 58.2% | — | Not comparable |
| Vibe Code BenchSource | 61.77% | — | Not comparable |
| AA-SciCodeSource | 53.2% | 27.8% | GPT-5.3 Codex leads |
| SciCodeSource | — | 32% | Not comparable |
| AA Coding IndexSource | — | 13.8% | Not comparable |
Reasoning2 benchmarks
Knowledge9 benchmarks
| Benchmark | GPT-5.3 Codex | Nemotron 3 Nano Omni 30B A3B | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 44.3% | 14.9% | GPT-5.3 Codex leads |
| AA-GPQA DiamondSource | 91.5% | 46.9% | GPT-5.3 Codex leads |
| AA-HLESource | 39.9% | 5.3% | GPT-5.3 Codex leads |
| AA-Omniscience IndexSource | 9.9% | -56.0% | GPT-5.3 Codex leads |
| AA-Omniscience AccuracySource | 51.8% | 14.8% | GPT-5.3 Codex leads |
| AA-Omniscience Hallucination RateSource | 86.9% | 83.1% | Nemotron 3 Nano Omni 30B A3B leads |
| MMLU-ProSource | — | 77.3% | Not comparable |
| GPQASource | — | 72.2% | Not comparable |
| GPQA-DSource | — | 72.2% | Not comparable |
Math1 benchmarks
| Benchmark | GPT-5.3 Codex | Nemotron 3 Nano Omni 30B A3B | Result |
|---|---|---|---|
| AIME 2025Source | — | 82.1% | Not comparable |
Multimodal9 benchmarks
| Benchmark | GPT-5.3 Codex | Nemotron 3 Nano Omni 30B A3B | Result |
|---|---|---|---|
| AA-MMMU-ProSource | 78.5% | 53.2% | GPT-5.3 Codex leads |
| Design Arena WebsiteSource | 1187 | — | Not comparable |
| MMMUSource | — | 70.8% | Not comparable |
| MMLongBench-DocSource | — | 57.5% | Not comparable |
| CharXivSource | — | 76.3% | Not comparable |
| ScreenSpot ProSource | — | 57.8% | Not comparable |
| Video-MME (w/o subtitle)Source | — | 72.2% | Not comparable |
| AI2D_TESTSource | — | 88.5% | Not comparable |
| RefCOCO (avg)Source | — | 90.5% | Not comparable |
Frequently Asked Questions (2)
Which is better, GPT-5.3 Codex or Nemotron 3 Nano Omni 30B A3B?
GPT-5.3 Codex is ahead on BenchLM's BenchAlign leaderboard, 65.75 to 43.32.
Which is better for coding, GPT-5.3 Codex or Nemotron 3 Nano Omni 30B A3B?
GPT-5.3 Codex has the edge for coding in this comparison, averaging 67.2 versus 32. Inside this category, AA-SciCode is the benchmark that creates the most daylight between them.
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