Model comparison
DeepSeek V3.2 vs Gemini 1.5 Pro
Head-to-head evidence from 4 shared benchmark results across 2 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: DeepSeek V3.2 #82 (Supported); Gemini 1.5 Pro #185 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. DeepSeek V3.2 and Gemini 1.5 Pro share 4 comparable benchmark results. 0 of 8 categories are comparable. 15 results are unique to DeepSeek V3.2; 2 to Gemini 1.5 Pro.
Updated July 20, 2026- Shared results
- 4
- DeepSeek V3.2 only
- 15
- Gemini 1.5 Pro only
- 2
- Comparable categories
- 0 / 8
Benchmark data for DeepSeek V3.2 and Gemini 1.5 Pro is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 4 shared benchmark results across 2 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.
Gemini 1.5 Pro is priced at $1.25 input / $5.00 output per 1M tokens, versus $0.28 input / $0.42 output per 1M tokens for DeepSeek V3.2. Gemini 1.5 Pro has the larger context window at 2M, 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 1.5 Pro |
|---|---|---|---|
| Coding | DeepSeek V3.260.9 | MarginNo overlap | Gemini 1.5 ProNot measured |
| Math | DeepSeek V3.217.1 | MarginNo overlap | Gemini 1.5 ProNot measured |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | DeepSeek V3.2 | Gemini 1.5 Pro | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | DeepSeek V3.2$0.28 input / $0.42 output | Gemini 1.5 Pro$1.25 input / $5 output | DeepSeek V3.2 has the lower combined listed price. |
| Generation speedtokens per second | DeepSeek V3.235 tok/s | Gemini 1.5 ProNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | DeepSeek V3.23.75 s | Gemini 1.5 ProNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | DeepSeek V3.2128K | Gemini 1.5 Pro2M | Gemini 1.5 Pro lists the larger context window. |
Benchmark Deep Dive
Agentic4 benchmarks
Coding4 benchmarks
Reasoning2 benchmarks
Knowledge6 benchmarks
| Benchmark | DeepSeek V3.2 | Gemini 1.5 Pro | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 24.7% | 10.0% | DeepSeek V3.2 leads |
| AA-GPQA DiamondSource | 75.1% | 58.9% | DeepSeek V3.2 leads |
| AA-HLESource | 10.5% | 4.9% | DeepSeek V3.2 leads |
| AA-Omniscience IndexSource | -46.7% | — | Not comparable |
| AA-Omniscience AccuracySource | 24.2% | — | Not comparable |
| AA-Omniscience Hallucination RateSource | 93.5% | — | Not comparable |
Math2 benchmarks
Multimodal2 benchmarks
Inst. Following1 benchmarks
| Benchmark | DeepSeek V3.2 | Gemini 1.5 Pro | Result |
|---|---|---|---|
| AA-IFBenchSource | 49.0% | — | Not comparable |
Frequently Asked Questions (3)
Can I compare DeepSeek V3.2 and Gemini 1.5 Pro 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 DeepSeek V3.2 and Gemini 1.5 Pro today?
DeepSeek V3.2: $0.28 input / $0.42 output per 1M tokens Gemini 1.5 Pro: $1.25 input / $5.00 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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