Model comparison
GPT-5.6 Sol vs Nemotron 3 Nano 30B
Head-to-head evidence from 15 shared benchmark results across 5 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GPT-5.6 Sol #3 (Supported); Nemotron 3 Nano 30B #98 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GPT-5.6 Sol and Nemotron 3 Nano 30B share 15 comparable benchmark results. 0 of 8 categories are comparable. 31 results are unique to GPT-5.6 Sol; 0 to Nemotron 3 Nano 30B.
Updated July 23, 2026- Shared results
- 15
- GPT-5.6 Sol only
- 31
- Nemotron 3 Nano 30B only
- 0
- Comparable categories
- 0 / 8
Benchmark data for GPT-5.6 Sol and Nemotron 3 Nano 30B is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 15 shared benchmark results across 5 evidence categories; 0 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
BenchLM has partial data for these models, but not enough overlapping benchmark coverage to produce a fair score-level comparison yet.
GPT-5.6 Sol is priced at $5.00 input / $30.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Nemotron 3 Nano 30B. GPT-5.6 Sol has the larger context window at 1M, compared with 32K for Nemotron 3 Nano 30B.
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-5.6 Sol | Δ | Nemotron 3 Nano 30B |
|---|---|---|---|
| Agentic | GPT-5.6 Sol92.0 | MarginNo overlap | Nemotron 3 Nano 30BNot measured |
| Coding | GPT-5.6 Sol64.6 | MarginNo overlap | Nemotron 3 Nano 30BNot measured |
| Knowledge | GPT-5.6 Sol94.6 | MarginNo overlap | Nemotron 3 Nano 30BNot measured |
| Math | GPT-5.6 Sol87.5 | MarginNo overlap | Nemotron 3 Nano 30BNot measured |
| Multimodal | GPT-5.6 Sol83.0 | MarginNo overlap | Nemotron 3 Nano 30BNot measured |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GPT-5.6 Sol | Nemotron 3 Nano 30B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-5.6 Sol$5 input / $30 output | Nemotron 3 Nano 30B$0 input / $0 output | Nemotron 3 Nano 30B has the lower combined listed price. |
| Generation speedtokens per second | GPT-5.6 SolNot available | Nemotron 3 Nano 30B152 tok/s | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GPT-5.6 SolNot available | Nemotron 3 Nano 30B1.90 s | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GPT-5.6 Sol1M | Nemotron 3 Nano 30B32K | GPT-5.6 Sol lists the larger context window. |
Benchmark Deep Dive
Agentic17 benchmarks
| Benchmark | GPT-5.6 Sol | Nemotron 3 Nano 30B | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 91.9% | — | Not comparable |
| BrowseCompSource | 92.2% | — | Not comparable |
| OSWorld 2.0Source | 62.6% | — | Not comparable |
| CyberGymSource | 84.5% | — | Not comparable |
| ExploitGymSource | 33.7% | — | Not comparable |
| ToolathlonSource | 58% | — | Not comparable |
| AA Agentic IndexSource | 54.0% | 2.0% | GPT-5.6 Sol leads |
| τ²-bench resultsSource | 85.1% | 40.9% | GPT-5.6 Sol leads |
| GDPval-AASource | 61.8% | 0.0% | GPT-5.6 Sol leads |
| GDPval-AASource | 1736 | 484 | GPT-5.6 Sol leads |
| AA BriefcaseSource | 1501 | — | Not comparable |
| AA ITBenchSource | 56.2% | — | Not comparable |
| AA Tau3 BankingSource | 33.0% | — | Not comparable |
| AA AutomationBenchSource | 51.2% | — | Not comparable |
| AA Harvey LABSource | 87.2% | — | Not comparable |
| terminalBenchHardSource | 65.9% | — | Not comparable |
| aaTerminalBench21Source | 88% | — | Not comparable |
Coding8 benchmarks
| Benchmark | GPT-5.6 Sol | Nemotron 3 Nano 30B | Result |
|---|---|---|---|
| SWE-bench ProSource | 64.6% | — | Not comparable |
| Terminal-Bench 2.0Source | 91.9% | — | Not comparable |
| deepSweSource | 72.7% | — | Not comparable |
| FrontierCode 1.1 ExtendedSource | 60.6% | — | Not comparable |
| cursorBench32Source | 67.2% | — | Not comparable |
| VulcanBench v3Source | 87.0% | — | Not comparable |
| AA Coding IndexSource | 77.4% | 14.4% | GPT-5.6 Sol leads |
| AA-SciCodeSource | 56.1% | 29.6% | GPT-5.6 Sol leads |
Reasoning4 benchmarks
Knowledge10 benchmarks
| Benchmark | GPT-5.6 Sol | Nemotron 3 Nano 30B | Result |
|---|---|---|---|
| GPQASource | 94.6% | — | Not comparable |
| GPQA-DSource | 94.6% | — | Not comparable |
| HealthBench ProfessionalSource | 60.5% | — | Not comparable |
| HealthBench HardSource | 33.1% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 58.9% | 14.2% | GPT-5.6 Sol leads |
| AA-GPQA DiamondSource | 94.1% | 75.7% | GPT-5.6 Sol leads |
| AA-HLESource | 47.2% | 10.2% | GPT-5.6 Sol leads |
| AA-Omniscience IndexSource | 21.7% | -51.6% | GPT-5.6 Sol leads |
| AA-Omniscience AccuracySource | 58.5% | 17.1% | GPT-5.6 Sol leads |
| AA-Omniscience Hallucination RateSource | 88.8% | 82.9% | Nemotron 3 Nano 30B leads |
Math3 benchmarks
Multimodal3 benchmarks
Inst. Following1 benchmarks
| Benchmark | GPT-5.6 Sol | Nemotron 3 Nano 30B | Result |
|---|---|---|---|
| AA-IFBenchSource | 72.7% | 71.1% | GPT-5.6 Sol leads |
Frequently Asked Questions (3)
Can I compare GPT-5.6 Sol and Nemotron 3 Nano 30B on BenchLM yet?
Not fully yet. BenchLM is tracking both models, but the sourced benchmark breakdown for this comparison is still coming soon.
Why does this comparison show “coming soon”?
BenchLM only shows category winners and benchmark-level calls when we have sourced results that can be compared fairly. For these models, the public benchmark coverage is not complete enough yet.
What data is available for GPT-5.6 Sol and Nemotron 3 Nano 30B today?
GPT-5.6 Sol: $5.00 input / $30.00 output per 1M tokens Nemotron 3 Nano 30B: $0.00 input / $0.00 output per 1M tokens Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.
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