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Model comparison

DeepSeek V3.2 vs Kimi K2

Data verified

Head-to-head evidence from 14 shared benchmark results across 7 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.

55.4/100
Margin
28.2pts
← winning
Kimi K2
Moonshot AI
27.19/100
1 category wins0 category wins

Public leaderboard positions: DeepSeek V3.2 #82 (Supported); Kimi K2 #189 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.

Evidence parity. DeepSeek V3.2 and Kimi K2 share 14 comparable benchmark results. 1 of 8 categories are comparable. 5 results are unique to DeepSeek V3.2; 0 to Kimi K2.

Updated July 20, 2026
Shared results
14
DeepSeek V3.2 only
5
Kimi K2 only
0
Comparable categories
1 / 8

Pick DeepSeek V3.2 if you want the stronger benchmark profile. Kimi K2 only becomes the better choice if its workflow or ecosystem matters more than the raw scoreboard.

Confidence note. This is a partial-evidence comparison with 14 shared benchmark results across 7 evidence categories; 1 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.

Why this result

DeepSeek V3.2 is clearly ahead on the BenchAlign aggregate, 55.4 to 27.19. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.

DeepSeek V3.2's sharpest advantage is in mathematics, where it averages 17.1 against 16.1. The single biggest benchmark swing on the page is FrontierMath v2 (Tier 4), 2.100% to 0.000%.

Kimi K2 is also the more expensive model on tokens at $0.60 input / $2.50 output per 1M tokens, versus $0.28 input / $0.42 output per 1M tokens for DeepSeek V3.2. That is roughly 6.0x on output cost alone.

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 scores and score margins for DeepSeek V3.2 and Kimi K2
CategoryDeepSeek V3.2ΔKimi K2
MathDeepSeek V3.217.1Margin← 1.0Kimi K216.1
CodingDeepSeek V3.260.9MarginNo overlapKimi K2Not measured

Decisive benchmark drivers

The largest measured benchmark gaps in this matchup, with exact reported values.

More
A · DeepSeek V3.2B · Kimi K2

  1. FrontierMath v2 (Tier 4)

    Math
    Source ↗
    A 2.100%B 0.000%
    Winner: DeepSeek V3.2Δ 2.1
    FrontierMath v2 (Tier 4): DeepSeek V3.2 scored 2.100%; Kimi K2 scored 0.000%. DeepSeek V3.2 wins this benchmark.

  2. FrontierMath v2 (Tiers 1-3)

    Math
    Source ↗
    A 22.100%B 21.404%
    Winner: DeepSeek V3.2Δ 0.7
    FrontierMath v2 (Tiers 1-3): DeepSeek V3.2 scored 22.100%; Kimi K2 scored 21.404%. DeepSeek V3.2 wins this benchmark.

Operational comparison

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

MetricDeepSeek V3.2Kimi K2Comparison
Input / output priceUSD per 1M tokensDeepSeek V3.2$0.28 input / $0.42 outputKimi K2$0.6 input / $2.5 outputDeepSeek V3.2 has the lower combined listed price.
Generation speedtokens per secondDeepSeek V3.235 tok/sKimi K243 tok/sKimi K2 has the higher measured throughput.
First-answer latencyseconds to first tokenDeepSeek V3.23.75 sKimi K21.51 sKimi K2 reaches the first token sooner.
Context windowmaximum listed tokensDeepSeek V3.2128KKimi K2128KListed context windows are equal.

Benchmark Deep Dive

Agentic
BenchmarkDeepSeek V3.2Kimi K2Result
Claw-EvalSource 40.2%—Not comparable
VITA-BenchSource 18.5%—Not comparable
τ²-bench resultsSource 78.9%61.1%DeepSeek V3.2 leads
Gert LabsSource 29.57%—Not comparable
Coding
BenchmarkDeepSeek V3.2Kimi K2Result
SWE-RebenchSource 60.9%—Not comparable
React Native EvalsSource 71.5%—Not comparable
AA-SciCodeSource 38.7%34.5%DeepSeek V3.2 leads
Reasoning
BenchmarkDeepSeek V3.2Kimi K2Result
AA-LCRSource 39.0%51.0%Kimi K2 leads
CritPtSource 0.9%0.0%DeepSeek V3.2 leads
Knowledge
BenchmarkDeepSeek V3.2Kimi K2Result
Artificial Analysis Intelligence IndexSource 24.7%19.4%DeepSeek V3.2 leads
AA-GPQA DiamondSource 75.1%76.6%Kimi K2 leads
AA-HLESource 10.5%7.0%DeepSeek V3.2 leads
AA-Omniscience IndexSource -46.7%-27.5%Kimi K2 leads
AA-Omniscience AccuracySource 24.2%26.8%Kimi K2 leads
AA-Omniscience Hallucination RateSource 93.5%74.2%Kimi K2 leads
MathDeepSeek V3.2 wins
BenchmarkDeepSeek V3.2Kimi K2Result
FrontierMath v2 (Tiers 1-3)Source 22.100%21.404%DeepSeek V3.2 leads
FrontierMath v2 (Tier 4)Source 2.100%0.000%DeepSeek V3.2 leads
Multimodal
BenchmarkDeepSeek V3.2Kimi K2Result
Design Arena WebsiteSource 12061083DeepSeek V3.2 leads
Inst. Following
BenchmarkDeepSeek V3.2Kimi K2Result
AA-IFBenchSource 49.0%41.5%DeepSeek V3.2 leads
Frequently Asked Questions (2)

Which is better, DeepSeek V3.2 or Kimi K2?

DeepSeek V3.2 is ahead on BenchLM's BenchAlign leaderboard, 55.4 to 27.19. The biggest single separator in this matchup is FrontierMath v2 (Tier 4), where the scores are 2.100% and 0.000%.

Which is better for math, DeepSeek V3.2 or Kimi K2?

DeepSeek V3.2 has the edge for math in this comparison, averaging 17.1 versus 16.1. Inside this category, FrontierMath v2 (Tier 4) is the benchmark that creates the most daylight between them.

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