Model comparison
DeepSeek V3.2 vs GPT-5.6 Sol
Head-to-head evidence from 13 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-5.6 Sol #3 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. DeepSeek V3.2 and GPT-5.6 Sol share 13 comparable benchmark results. 2 of 8 categories are comparable. 6 results are unique to DeepSeek V3.2; 33 to GPT-5.6 Sol.
Updated July 23, 2026- Shared results
- 13
- DeepSeek V3.2 only
- 6
- GPT-5.6 Sol only
- 33
- Comparable categories
- 2 / 8
Pick GPT-5.6 Sol 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 13 shared benchmark results across 6 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 55.4. 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 17.1. The single biggest benchmark swing on the page is FrontierMath v2 (Tier 4), 2.100% 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 $0.28 input / $0.42 output per 1M tokens for DeepSeek V3.2. That is roughly 71.4x on output cost alone. GPT-5.6 Sol 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-5.6 Sol gives you the larger context window at 1M, 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-5.6 Sol |
|---|---|---|---|
| Math | DeepSeek V3.217.1 | Margin→ 70.4 | GPT-5.6 Sol87.5 |
| Coding | DeepSeek V3.260.9 | Margin→ 3.7 | GPT-5.6 Sol64.6 |
| Agentic | DeepSeek V3.2Not measured | MarginNo overlap | GPT-5.6 Sol92.0 |
| Knowledge | DeepSeek V3.2Not measured | MarginNo overlap | GPT-5.6 Sol94.6 |
| Multimodal | DeepSeek V3.2Not measured | MarginNo overlap | GPT-5.6 Sol83.0 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
FrontierMath v2 (Tier 4)
MathA 2.100%B 83.000%Winner: GPT-5.6 SolΔ 80.9FrontierMath v2 (Tier 4): DeepSeek V3.2 scored 2.100%; GPT-5.6 Sol scored 83.000%. GPT-5.6 Sol wins this benchmark. - Source ↗
FrontierMath v2 (Tiers 1-3)
MathA 22.100%B 89.000%Winner: GPT-5.6 SolΔ 66.9FrontierMath v2 (Tiers 1-3): DeepSeek V3.2 scored 22.100%; GPT-5.6 Sol scored 89.000%. 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 | DeepSeek V3.2 | GPT-5.6 Sol | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | DeepSeek V3.2$0.28 input / $0.42 output | GPT-5.6 Sol$5 input / $30 output | DeepSeek V3.2 has the lower combined listed price. |
| Generation speedtokens per second | DeepSeek V3.235 tok/s | GPT-5.6 SolNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | DeepSeek V3.23.75 s | GPT-5.6 SolNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | DeepSeek V3.2128K | GPT-5.6 Sol1M | GPT-5.6 Sol lists the larger context window. |
Benchmark Deep Dive
Agentic20 benchmarks
| Benchmark | DeepSeek V3.2 | GPT-5.6 Sol | Result |
|---|---|---|---|
| Claw-EvalSource | 40.2% | — | Not comparable |
| VITA-BenchSource | 18.5% | — | Not comparable |
| τ²-bench resultsSource | 78.9% | 85.1% | GPT-5.6 Sol leads |
| Gert LabsSource | 29.57% | — | 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 |
CodingGPT-5.6 Sol wins10 benchmarks
| Benchmark | DeepSeek V3.2 | GPT-5.6 Sol | Result |
|---|---|---|---|
| SWE-RebenchSource | 60.9% | — | Not comparable |
| React Native EvalsSource | 71.5% | — | Not comparable |
| AA-SciCodeSource | 38.7% | 56.1% | GPT-5.6 Sol leads |
| 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 |
Reasoning4 benchmarks
Knowledge10 benchmarks
| Benchmark | DeepSeek V3.2 | GPT-5.6 Sol | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 24.7% | 58.9% | GPT-5.6 Sol leads |
| AA-GPQA DiamondSource | 75.1% | 94.1% | GPT-5.6 Sol leads |
| AA-HLESource | 10.5% | 47.2% | GPT-5.6 Sol leads |
| AA-Omniscience IndexSource | -46.7% | 21.7% | GPT-5.6 Sol leads |
| AA-Omniscience AccuracySource | 24.2% | 58.5% | GPT-5.6 Sol leads |
| AA-Omniscience Hallucination RateSource | 93.5% | 88.8% | GPT-5.6 Sol leads |
| 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
Multimodal4 benchmarks
Inst. Following1 benchmarks
| Benchmark | DeepSeek V3.2 | GPT-5.6 Sol | Result |
|---|---|---|---|
| AA-IFBenchSource | 49.0% | 72.7% | GPT-5.6 Sol leads |
Frequently Asked Questions (3)
Which is better, DeepSeek V3.2 or GPT-5.6 Sol?
GPT-5.6 Sol is ahead on BenchLM's BenchAlign leaderboard, 81.96 to 55.4. The biggest single separator in this matchup is FrontierMath v2 (Tier 4), where the scores are 2.100% and 83.000%.
Which is better for coding, DeepSeek V3.2 or GPT-5.6 Sol?
GPT-5.6 Sol has the edge for coding in this comparison, averaging 64.6 versus 60.9. Inside this category, AA-SciCode is the benchmark that creates the most daylight between them.
Which is better for math, DeepSeek V3.2 or GPT-5.6 Sol?
GPT-5.6 Sol has the edge for math in this comparison, averaging 87.5 versus 17.1. Inside this category, FrontierMath v2 (Tier 4) is the benchmark that creates the most daylight between them.
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