Model comparison
DeepSeek V3.2 vs Gemini 2.5 Pro
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: DeepSeek V3.2 #82 (Supported); Gemini 2.5 Pro #70 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. DeepSeek V3.2 and Gemini 2.5 Pro share 15 comparable benchmark results. 2 of 8 categories are comparable. 4 results are unique to DeepSeek V3.2; 9 to Gemini 2.5 Pro.
Updated July 20, 2026- Shared results
- 15
- DeepSeek V3.2 only
- 4
- Gemini 2.5 Pro only
- 9
- Comparable categories
- 2 / 8
Pick Gemini 2.5 Pro if you want the stronger benchmark profile. DeepSeek V3.2 only becomes the better choice if mathematics is the priority or you want the cheaper token bill.
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
Gemini 2.5 Pro has the cleaner BenchAlign overall profile here, landing at 57.25 versus 55.4. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
Gemini 2.5 Pro's sharpest advantage is in coding, where it averages 63.8 against 60.9. The single biggest benchmark swing on the page is FrontierMath v2 (Tiers 1-3), 22.100% to 14.138%. DeepSeek V3.2 does hit back in mathematics, so the answer changes if that is the part of the workload you care about most.
Gemini 2.5 Pro is also the more expensive model on tokens at $1.25 input / $10.00 output per 1M tokens, versus $0.28 input / $0.42 output per 1M tokens for DeepSeek V3.2. That is roughly 23.8x on output cost alone. Gemini 2.5 Pro 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 | Gemini 2.5 Pro |
|---|---|---|---|
| Math | DeepSeek V3.217.1 | Margin← 5.5 | Gemini 2.5 Pro11.6 |
| Coding | DeepSeek V3.260.9 | Margin→ 2.9 | Gemini 2.5 Pro63.8 |
| Knowledge | DeepSeek V3.2Not measured | MarginNo overlap | Gemini 2.5 Pro27.4 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
FrontierMath v2 (Tiers 1-3)
MathA 22.100%B 14.138%Winner: DeepSeek V3.2Δ 8FrontierMath v2 (Tiers 1-3): DeepSeek V3.2 scored 22.100%; Gemini 2.5 Pro scored 14.138%. DeepSeek V3.2 wins this benchmark. - Source ↗
FrontierMath v2 (Tier 4)
MathA 2.100%B 4.167%Winner: Gemini 2.5 ProΔ 2.1FrontierMath v2 (Tier 4): DeepSeek V3.2 scored 2.100%; Gemini 2.5 Pro scored 4.167%. Gemini 2.5 Pro wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | DeepSeek V3.2 | Gemini 2.5 Pro | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | DeepSeek V3.2$0.28 input / $0.42 output | Gemini 2.5 Pro$1.25 input / $10 output | DeepSeek V3.2 has the lower combined listed price. |
| Generation speedtokens per second | DeepSeek V3.235 tok/s | Gemini 2.5 Pro117 tok/s | Gemini 2.5 Pro has the higher measured throughput. |
| First-answer latencyseconds to first token | DeepSeek V3.23.75 s | Gemini 2.5 Pro21.19 s | DeepSeek V3.2 reaches the first token sooner. |
| Context windowmaximum listed tokens | DeepSeek V3.2128K | Gemini 2.5 Pro1M | Gemini 2.5 Pro lists the larger context window. |
Benchmark Deep Dive
Agentic7 benchmarks
| Benchmark | DeepSeek V3.2 | Gemini 2.5 Pro | Result |
|---|---|---|---|
| Claw-EvalSource | 40.2% | — | Not comparable |
| VITA-BenchSource | 18.5% | — | Not comparable |
| τ²-bench resultsSource | 78.9% | 54.1% | DeepSeek V3.2 leads |
| Gert LabsSource | 29.57% | 42.01% | Gemini 2.5 Pro leads |
| AA Agentic IndexSource | — | 7.1% | Not comparable |
| GDPval-AASource | — | 8.3% | Not comparable |
| GDPval-AASource | — | 665 | Not comparable |
CodingGemini 2.5 Pro wins6 benchmarks
| Benchmark | DeepSeek V3.2 | Gemini 2.5 Pro | Result |
|---|---|---|---|
| SWE-RebenchSource | 60.9% | — | Not comparable |
| React Native EvalsSource | 71.5% | — | Not comparable |
| AA-SciCodeSource | 38.7% | 42.8% | Gemini 2.5 Pro leads |
| SWE-bench VerifiedSource | — | 63.8% | Not comparable |
| Vibe Code BenchSource | — | 0.40% | Not comparable |
| AA Coding IndexSource | — | 33.3% | Not comparable |
Reasoning2 benchmarks
Knowledge8 benchmarks
| Benchmark | DeepSeek V3.2 | Gemini 2.5 Pro | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 24.7% | 25.8% | Gemini 2.5 Pro leads |
| AA-GPQA DiamondSource | 75.1% | 84.4% | Gemini 2.5 Pro leads |
| AA-HLESource | 10.5% | 21.1% | Gemini 2.5 Pro leads |
| AA-Omniscience IndexSource | -46.7% | -14.3% | Gemini 2.5 Pro leads |
| AA-Omniscience AccuracySource | 24.2% | 39.0% | Gemini 2.5 Pro leads |
| AA-Omniscience Hallucination RateSource | 93.5% | 87.4% | Gemini 2.5 Pro leads |
| GPQASource | — | 83% | Not comparable |
| HLESource | — | 18.8% | Not comparable |
MathDeepSeek V3.2 wins2 benchmarks
Multimodal2 benchmarks
Inst. Following1 benchmarks
| Benchmark | DeepSeek V3.2 | Gemini 2.5 Pro | Result |
|---|---|---|---|
| AA-IFBenchSource | 49.0% | 48.7% | DeepSeek V3.2 leads |
Frequently Asked Questions (3)
Which is better, DeepSeek V3.2 or Gemini 2.5 Pro?
Gemini 2.5 Pro is ahead on BenchLM's BenchAlign leaderboard, 57.25 to 55.4. The biggest single separator in this matchup is FrontierMath v2 (Tiers 1-3), where the scores are 22.100% and 14.138%.
Which is better for coding, DeepSeek V3.2 or Gemini 2.5 Pro?
Gemini 2.5 Pro has the edge for coding in this comparison, averaging 63.8 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 Gemini 2.5 Pro?
DeepSeek V3.2 has the edge for math in this comparison, averaging 17.1 versus 11.6. Inside this category, FrontierMath v2 (Tiers 1-3) is the benchmark that creates the most daylight between them.
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