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Model comparison

DeepSeek V3.1 vs Gemini 1.5 Pro

Data verified

Head-to-head evidence from 4 shared benchmark results across 2 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.

53.64/100
Margin
17.9pts
← winning
35.71/100
0 category wins0 category wins

Public leaderboard positions: DeepSeek V3.1 #95 (Supported); Gemini 1.5 Pro #185 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.

Evidence parity. DeepSeek V3.1 and Gemini 1.5 Pro share 4 comparable benchmark results. 0 of 8 categories are comparable. 8 results are unique to DeepSeek V3.1; 2 to Gemini 1.5 Pro.

Updated July 20, 2026
Shared results
4
DeepSeek V3.1 only
8
Gemini 1.5 Pro only
2
Comparable categories
0 / 8

Benchmark data for DeepSeek V3.1 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 $0.00 input / $0.00 output per 1M tokens for DeepSeek V3.1. Gemini 1.5 Pro has the larger context window at 2M, compared with 128K for DeepSeek V3.1.

Category breakdown

Exact category averages are shown below. Not measured means BenchLM does not have enough sourced public coverage for that model and category.

Operational comparison

Runtime and commercial metrics are compared only when both models have a complete sourced value.

MetricDeepSeek V3.1Gemini 1.5 ProComparison
Input / output priceUSD per 1M tokensDeepSeek V3.1$0 input / $0 outputGemini 1.5 Pro$1.25 input / $5 outputDeepSeek V3.1 has the lower combined listed price.
Generation speedtokens per secondDeepSeek V3.1Not availableGemini 1.5 ProNot availableA complete speed comparison is not available.
First-answer latencyseconds to first tokenDeepSeek V3.1Not availableGemini 1.5 ProNot availableA complete latency comparison is not available.
Context windowmaximum listed tokensDeepSeek V3.1128KGemini 1.5 Pro2MGemini 1.5 Pro lists the larger context window.

Benchmark Deep Dive

Agentic
BenchmarkDeepSeek V3.1Gemini 1.5 ProResult
τ²-bench resultsSource 34.8%Not comparable
Coding
BenchmarkDeepSeek V3.1Gemini 1.5 ProResult
AA-SciCodeSource 36.7%29.5%DeepSeek V3.1 leads
AA Coding IndexSource 23.6%Not comparable
Reasoning
BenchmarkDeepSeek V3.1Gemini 1.5 ProResult
AA-LCRSource 45.0%Not comparable
CritPtSource 0.0%Not comparable
Knowledge
BenchmarkDeepSeek V3.1Gemini 1.5 ProResult
Artificial Analysis Intelligence IndexSource 21.1%10.0%DeepSeek V3.1 leads
AA-GPQA DiamondSource 73.5%58.9%DeepSeek V3.1 leads
AA-HLESource 6.3%4.9%DeepSeek V3.1 leads
AA-Omniscience IndexSource -41.1%Not comparable
AA-Omniscience AccuracySource 23.1%Not comparable
AA-Omniscience Hallucination RateSource 83.5%Not comparable
Multimodal
BenchmarkDeepSeek V3.1Gemini 1.5 ProResult
Design Arena WebsiteSource 1155Not comparable
AA-MMMU-ProSource 55.0%Not comparable
Inst. Following
BenchmarkDeepSeek V3.1Gemini 1.5 ProResult
AA-IFBenchSource 37.8%Not comparable
Frequently Asked Questions (3)

Can I compare DeepSeek V3.1 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 V3.1 and Gemini 1.5 Pro today?

DeepSeek V3.1: $0.00 input / $0.00 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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Last updated: July 20, 2026

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