Model comparison
Gemini 3 Pro vs GPT-5.6 Sol
Head-to-head evidence from 15 shared benchmark results across 7 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Gemini 3 Pro #19 (Supported); GPT-5.6 Sol #3 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Gemini 3 Pro and GPT-5.6 Sol share 15 comparable benchmark results. 2 of 8 categories are comparable. 12 results are unique to Gemini 3 Pro; 31 to GPT-5.6 Sol.
Updated July 23, 2026- Shared results
- 15
- Gemini 3 Pro only
- 12
- GPT-5.6 Sol only
- 31
- Comparable categories
- 2 / 8
Pick GPT-5.6 Sol if you want the stronger benchmark profile. Gemini 3 Pro only becomes the better choice if you want the cheaper token bill or you need the larger 2M context window.
Confidence note. This is a partial-evidence comparison with 15 shared benchmark results across 7 evidence categories; 2 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
GPT-5.6 Sol is clearly ahead on the BenchAlign aggregate, 81.96 to 67.73. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.6 Sol's sharpest advantage is in mathematics, where it averages 87.5 against 32.9. The single biggest benchmark swing on the page is FrontierMath v2 (Tier 4), 18.750% to 83.000%.
GPT-5.6 Sol is also the more expensive model on tokens at $5.00 input / $30.00 output per 1M tokens, versus $2.00 input / $12.00 output per 1M tokens for Gemini 3 Pro. That is roughly 2.5x on output cost alone. GPT-5.6 Sol is the reasoning model in the pair, while Gemini 3 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 3 Pro gives you the larger context window at 2M, compared with 1M for GPT-5.6 Sol.
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 3 Pro | Δ | GPT-5.6 Sol |
|---|---|---|---|
| Math | Gemini 3 Pro32.9 | Margin→ 54.6 | GPT-5.6 Sol87.5 |
| Multimodal | Gemini 3 Pro81.1 | Margin→ 1.9 | GPT-5.6 Sol83.0 |
| Agentic | Gemini 3 ProNot measured | MarginNo overlap | GPT-5.6 Sol92.0 |
| Coding | Gemini 3 ProNot measured | MarginNo overlap | GPT-5.6 Sol64.6 |
| Reasoning | Gemini 3 Pro31.1 | MarginNo overlap | GPT-5.6 SolNot measured |
| Knowledge | Gemini 3 ProNot measured | MarginNo overlap | GPT-5.6 Sol94.6 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
FrontierMath v2 (Tier 4)
MathA 18.750%B 83.000%Winner: GPT-5.6 SolΔ 64.3FrontierMath v2 (Tier 4): Gemini 3 Pro scored 18.750%; GPT-5.6 Sol scored 83.000%. GPT-5.6 Sol wins this benchmark. - Source ↗
FrontierMath v2 (Tiers 1-3)
MathA 37.600%B 89.000%Winner: GPT-5.6 SolΔ 51.4FrontierMath v2 (Tiers 1-3): Gemini 3 Pro scored 37.600%; GPT-5.6 Sol scored 89.000%. GPT-5.6 Sol wins this benchmark. - Source ↗
MMMU-Pro
MultimodalA 81%B 83%Winner: GPT-5.6 SolΔ 2MMMU-Pro: Gemini 3 Pro scored 81%; GPT-5.6 Sol scored 83%. GPT-5.6 Sol wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Gemini 3 Pro | GPT-5.6 Sol | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Gemini 3 Pro$2 input / $12 output | GPT-5.6 Sol$5 input / $30 output | Gemini 3 Pro has the lower combined listed price. |
| Generation speedtokens per second | Gemini 3 Pro109 tok/s | GPT-5.6 SolNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Gemini 3 Pro32.65 s | GPT-5.6 SolNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Gemini 3 Pro2M | GPT-5.6 Sol1M | Gemini 3 Pro lists the larger context window. |
Benchmark Deep Dive
Agentic19 benchmarks
| Benchmark | Gemini 3 Pro | GPT-5.6 Sol | Result |
|---|---|---|---|
| τ²-bench resultsSource | 87.1% | 85.1% | Gemini 3 Pro leads |
| Gert LabsSource | 63.23% | — | Not comparable |
| JobBenchSource | 11.4% | — | Not comparable |
| 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% | Not comparable |
| GDPval-AASource | — | 61.8% | Not comparable |
| GDPval-AASource | — | 1736 | Not comparable |
| 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 |
Coding10 benchmarks
| Benchmark | Gemini 3 Pro | GPT-5.6 Sol | Result |
|---|---|---|---|
| Vibe Code BenchSource | 14.30% | — | Not comparable |
| AA-SciCodeSource | 56.1% | 56.1% | Tie |
| AA LiveCodeBenchSource | 91.7% | — | Not comparable |
| 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% | Not comparable |
Reasoning5 benchmarks
Knowledge11 benchmarks
| Benchmark | Gemini 3 Pro | GPT-5.6 Sol | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 39.5% | 58.9% | GPT-5.6 Sol leads |
| AA-GPQA DiamondSource | 90.8% | 94.1% | GPT-5.6 Sol leads |
| AA-HLESource | 37.2% | 47.2% | GPT-5.6 Sol leads |
| AA-Omniscience IndexSource | 15.8% | 21.7% | GPT-5.6 Sol leads |
| AA-Omniscience AccuracySource | 55.9% | 58.5% | GPT-5.6 Sol leads |
| AA-Omniscience Hallucination RateSource | 90.9% | 88.8% | GPT-5.6 Sol leads |
| AA MMLU-ProSource | 89.8% | — | Not comparable |
| GPQASource | — | 94.6% | Not comparable |
| GPQA-DSource | — | 94.6% | Not comparable |
| HealthBench ProfessionalSource | — | 60.5% | Not comparable |
| HealthBench HardSource | — | 33.1% | Not comparable |
MathGPT-5.6 Sol wins3 benchmarks
Multilingual1 benchmarks
| Benchmark | Gemini 3 Pro | GPT-5.6 Sol | Result |
|---|---|---|---|
| AA Global-MMLU-LiteSource | 92.2% | — | Not comparable |
MultimodalGPT-5.6 Sol wins8 benchmarks
| Benchmark | Gemini 3 Pro | GPT-5.6 Sol | Result |
|---|---|---|---|
| MMMU-ProSource | 81% | 83% | GPT-5.6 Sol leads |
| MathVisionSource | 86.6% | — | Not comparable |
| VideoMMMUSource | 87.6% | — | Not comparable |
| ScreenSpot ProSource | 72.7% | — | Not comparable |
| CharXivSource | 81.4% | — | Not comparable |
| V*Source | 88.0% | — | Not comparable |
| AA-MMMU-ProSource | 80.2% | 83.4% | GPT-5.6 Sol leads |
| MMMU-Pro w/ PythonSource | — | 84.6% | Not comparable |
Inst. Following1 benchmarks
| Benchmark | Gemini 3 Pro | GPT-5.6 Sol | Result |
|---|---|---|---|
| AA-IFBenchSource | 70.4% | 72.7% | GPT-5.6 Sol leads |
Frequently Asked Questions (3)
Which is better, Gemini 3 Pro or GPT-5.6 Sol?
GPT-5.6 Sol is ahead on BenchLM's BenchAlign leaderboard, 81.96 to 67.73. The biggest single separator in this matchup is FrontierMath v2 (Tier 4), where the scores are 18.750% and 83.000%.
Which is better for math, Gemini 3 Pro or GPT-5.6 Sol?
GPT-5.6 Sol has the edge for math in this comparison, averaging 87.5 versus 32.9. Inside this category, FrontierMath v2 (Tier 4) is the benchmark that creates the most daylight between them.
Which is better for multimodal and grounded tasks, Gemini 3 Pro or GPT-5.6 Sol?
GPT-5.6 Sol has the edge for multimodal and grounded tasks in this comparison, averaging 83 versus 81.1. Inside this category, AA-MMMU-Pro is the benchmark that creates the most daylight between them.
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