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Model comparison

DeepSeek V4 Flash (High) vs Kimi K2.5

Data verified

Head-to-head evidence from 28 shared benchmark results across 7 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.

53.95/100
Margin
5.7pts
winning →
Kimi K2.5
Moonshot AI
59.66/100
3 category wins1 category wins

Public leaderboard positions: DeepSeek V4 Flash (High) #92 (Estimated); Kimi K2.5 #54 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.

Evidence parity. DeepSeek V4 Flash (High) and Kimi K2.5 share 28 comparable benchmark results. 4 of 8 categories are comparable. 10 results are unique to DeepSeek V4 Flash (High); 35 to Kimi K2.5.

Updated July 23, 2026
Shared results
28
DeepSeek V4 Flash (High) only
10
Kimi K2.5 only
35
Comparable categories
4 / 8

Pick Kimi K2.5 if you want the stronger benchmark profile. DeepSeek V4 Flash (High) 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 28 shared benchmark results across 7 evidence categories; 4 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.

Why this result

Kimi K2.5 is clearly ahead on the BenchAlign aggregate, 59.66 to 53.95. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.

Kimi K2.5's sharpest advantage is in knowledge, where it averages 56.9 against 52.1. The single biggest benchmark swing on the page is BrowseComp, 53.5% to 60.6%. DeepSeek V4 Flash (High) does hit back in mathematics, so the answer changes if that is the part of the workload you care about most.

Kimi K2.5 is also the more expensive model on tokens at $0.60 input / $3.00 output per 1M tokens, versus $0.14 input / $0.28 output per 1M tokens for DeepSeek V4 Flash (High). That is roughly 10.7x on output cost alone. DeepSeek V4 Flash (High) is the reasoning model in the pair, while Kimi K2.5 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. DeepSeek V4 Flash (High) gives you the larger context window at 1M, compared with 256K for Kimi K2.5.

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 V4 Flash (High) and Kimi K2.5
CategoryDeepSeek V4 Flash (High)ΔKimi K2.5
MathDeepSeek V4 Flash (High)91.9Margin← 31.3Kimi K2.560.6
CodingDeepSeek V4 Flash (High)68.5Margin← 9.1Kimi K2.559.4
KnowledgeDeepSeek V4 Flash (High)52.1Margin→ 4.8Kimi K2.556.9
AgenticDeepSeek V4 Flash (High)55.3Margin← 0.3Kimi K2.555.0
ReasoningDeepSeek V4 Flash (High)Not measuredMarginNo overlapKimi K2.561.0
MultilingualDeepSeek V4 Flash (High)Not measuredMarginNo overlapKimi K2.582.3
MultimodalDeepSeek V4 Flash (High)Not measuredMarginNo overlapKimi K2.578.5
Inst. FollowingDeepSeek V4 Flash (High)Not measuredMarginNo overlapKimi K2.593.9

Decisive benchmark drivers

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

More
A · DeepSeek V4 Flash (High)B · Kimi K2.5

  1. BrowseComp

    Agentic
    Source ↗
    A 53.5%B 60.6%
    Winner: Kimi K2.5Δ 7.1
    BrowseComp: DeepSeek V4 Flash (High) scored 53.5%; Kimi K2.5 scored 60.6%. Kimi K2.5 wins this benchmark.

  2. Terminal-Bench 2.0

    Agentic
    Source ↗
    A 56.6%B 50.8%
    Winner: DeepSeek V4 Flash (High)Δ 5.8
    Terminal-Bench 2.0: DeepSeek V4 Flash (High) scored 56.6%; Kimi K2.5 scored 50.8%. DeepSeek V4 Flash (High) wins this benchmark.

  3. HMMT Feb 2026

    Math
    Source ↗
    A 91.9%B 87.1%
    Winner: DeepSeek V4 Flash (High)Δ 4.8
    HMMT Feb 2026: DeepSeek V4 Flash (High) scored 91.9%; Kimi K2.5 scored 87.1%. DeepSeek V4 Flash (High) wins this benchmark.

  4. SWE-bench Verified

    Coding
    Source ↗
    A 78.6%B 76.8%
    Winner: DeepSeek V4 Flash (High)Δ 1.8
    SWE-bench Verified: DeepSeek V4 Flash (High) scored 78.6%; Kimi K2.5 scored 76.8%. DeepSeek V4 Flash (High) wins this benchmark.

  5. SWE-bench Pro

    Coding
    Source ↗
    A 52.3%B 50.7%
    Winner: DeepSeek V4 Flash (High)Δ 1.6
    SWE-bench Pro: DeepSeek V4 Flash (High) scored 52.3%; Kimi K2.5 scored 50.7%. DeepSeek V4 Flash (High) wins this benchmark.

Operational comparison

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

MetricDeepSeek V4 Flash (High)Kimi K2.5Comparison
Input / output priceUSD per 1M tokensDeepSeek V4 Flash (High)$0.14 input / $0.28 outputKimi K2.5$0.6 input / $3 outputDeepSeek V4 Flash (High) has the lower combined listed price.
Generation speedtokens per secondDeepSeek V4 Flash (High)Not availableKimi K2.545 tok/sA complete speed comparison is not available.
First-answer latencyseconds to first tokenDeepSeek V4 Flash (High)Not availableKimi K2.52.38 sA complete latency comparison is not available.
Context windowmaximum listed tokensDeepSeek V4 Flash (High)1MKimi K2.5256KDeepSeek V4 Flash (High) lists the larger context window.

Benchmark Deep Dive

AgenticDeepSeek V4 Flash (High) wins
BenchmarkDeepSeek V4 Flash (High)Kimi K2.5Result
Terminal-Bench 2.0Source 56.6%50.8%DeepSeek V4 Flash (High) leads
BrowseCompSource 53.5%60.6%Kimi K2.5 leads
HLE w/ toolsSource 40.3%—Not comparable
MCP AtlasSource 67.4%29.5%DeepSeek V4 Flash (High) leads
ToolathlonSource 43.5%27.8%DeepSeek V4 Flash (High) leads
τ²-bench resultsSource 95.6%95.9%Kimi K2.5 leads
AA Agentic IndexSource 28.2%21.7%DeepSeek V4 Flash (High) leads
GDPval-AASource 32.4%25.4%DeepSeek V4 Flash (High) leads
GDPval-AASource 11471009DeepSeek V4 Flash (High) leads
Claw-EvalSource —52.3%Not comparable
QwenClawBenchSource —54.3%Not comparable
τ³-bench resultsSource —65.7%Not comparable
DeepSearchQASource —77.1%Not comparable
DeepPlanningSource —14.4%Not comparable
MCP-TasksSource —59.1%Not comparable
WideResearchSource —72.7%Not comparable
APEX-Agents-AASource —11.5%Not comparable
Gert LabsSource —45.88%Not comparable
ResearchClawBenchSource —14.0%Not comparable
JobBenchSource —8.7%Not comparable
CodingDeepSeek V4 Flash (High) wins
BenchmarkDeepSeek V4 Flash (High)Kimi K2.5Result
CodeforcesSource 2816.0—Not comparable
SWE-bench VerifiedSource 78.6%76.8%DeepSeek V4 Flash (High) leads
SWE-bench ProSource 52.3%50.7%DeepSeek V4 Flash (High) leads
SWE MultilingualSource 70.2%73%Kimi K2.5 leads
Terminal-Bench 2.0Source 56.6%—Not comparable
AA-SciCodeSource 42.0%49.0%Kimi K2.5 leads
AA Coding IndexSource 52.0%46.8%DeepSeek V4 Flash (High) leads
SWE-bench Verified*Source —70.8%Not comparable
LiveCodeBench v6Source —85.0%Not comparable
SWE-RebenchSource —58.5%Not comparable
React Native EvalsSource —77.2%Not comparable
SciCodeSource —48.7%Not comparable
Reasoning
BenchmarkDeepSeek V4 Flash (High)Kimi K2.5Result
MRCR 1MSource 76.9%—Not comparable
CorpusQA 1MSource 59.3%—Not comparable
AA-LCRSource 62.7%65.3%Kimi K2.5 leads
CritPtSource 3.4%3.1%DeepSeek V4 Flash (High) leads
LongBench v2Source —61%Not comparable
KnowledgeKimi K2.5 wins
BenchmarkDeepSeek V4 Flash (High)Kimi K2.5Result
MMLU-ProSource 86.4%87.1%Kimi K2.5 leads
SimpleQASource 28.9%—Not comparable
Chinese-SimpleQASource 73.2%—Not comparable
GPQASource 87.4%87.6%Kimi K2.5 leads
GPQA-DSource 87.4%87.6%Kimi K2.5 leads
HLESource 29.4%30.1%Kimi K2.5 leads
Artificial Analysis Intelligence IndexSource 37.5%35.4%DeepSeek V4 Flash (High) leads
AA-GPQA DiamondSource 86.7%87.9%Kimi K2.5 leads
AA-HLESource 27.8%29.4%Kimi K2.5 leads
AA-Omniscience IndexSource -22.3%-8.1%Kimi K2.5 leads
AA-Omniscience AccuracySource 35.5%34.3%DeepSeek V4 Flash (High) leads
AA-Omniscience Hallucination RateSource 89.7%64.6%Kimi K2.5 leads
SuperGPQASource —69.2%Not comparable
MMLU-Pro (Arcee)Source —87.1%Not comparable
MathDeepSeek V4 Flash (High) wins
BenchmarkDeepSeek V4 Flash (High)Kimi K2.5Result
HMMT Feb 2026Source 91.9%87.1%DeepSeek V4 Flash (High) leads
IMOAnswerBenchSource 85.1%—Not comparable
ApexSource 19.1%—Not comparable
Apex ShortlistSource 72.1%—Not comparable
AIME 2025Source —96.1%Not comparable
AIME26Source —95.8%Not comparable
AIME25 (Arcee)Source —96.3%Not comparable
HMMT Feb 2025Source —95.4%Not comparable
HMMT Nov 2025Source —91.1%Not comparable
MMAnswerBenchSource —81.8%Not comparable
FrontierMath v2 (Tiers 1-3)Source —27.900%Not comparable
FrontierMath v2 (Tier 4)Source —4.200%Not comparable
Multilingual
BenchmarkDeepSeek V4 Flash (High)Kimi K2.5Result
MMLU-ProXSource —82.3%Not comparable
NOVA-63Source —56.0%Not comparable
Multimodal
BenchmarkDeepSeek V4 Flash (High)Kimi K2.5Result
Design Arena WebsiteSource 12381279Kimi K2.5 leads
MMMU-ProSource —78.5%Not comparable
Video-MMESource —87.4%Not comparable
MMVUSource —80.4%Not comparable
VideoMMMUSource —86.6%Not comparable
AA-MMMU-ProSource —75.4%Not comparable
Inst. Following
BenchmarkDeepSeek V4 Flash (High)Kimi K2.5Result
AA-IFBenchSource 73.5%70.2%DeepSeek V4 Flash (High) leads
IFEvalSource —93.9%Not comparable
Frequently Asked Questions (5)

Which is better, DeepSeek V4 Flash (High) or Kimi K2.5?

Kimi K2.5 is ahead on BenchLM's BenchAlign leaderboard, 59.66 to 53.95. The biggest single separator in this matchup is BrowseComp, where the scores are 53.5% and 60.6%.

Which is better for knowledge tasks, DeepSeek V4 Flash (High) or Kimi K2.5?

Kimi K2.5 has the edge for knowledge tasks in this comparison, averaging 56.9 versus 52.1. Inside this category, AA-Omniscience Hallucination Rate is the benchmark that creates the most daylight between them.

Which is better for coding, DeepSeek V4 Flash (High) or Kimi K2.5?

DeepSeek V4 Flash (High) has the edge for coding in this comparison, averaging 68.5 versus 59.4. Inside this category, AA-SciCode is the benchmark that creates the most daylight between them.

Which is better for math, DeepSeek V4 Flash (High) or Kimi K2.5?

DeepSeek V4 Flash (High) has the edge for math in this comparison, averaging 91.9 versus 60.6. Inside this category, HMMT Feb 2026 is the benchmark that creates the most daylight between them.

Which is better for agentic tasks, DeepSeek V4 Flash (High) or Kimi K2.5?

DeepSeek V4 Flash (High) has the edge for agentic tasks in this comparison, averaging 55.3 versus 55. Inside this category, GDPval-AA is the benchmark that creates the most daylight between them.

Self-host vs API cost

Estimates at 50,000 req/day · 1000 tokens/req average.

DeepSeek V4 Flash (High)
API / mo$315
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Kimi K2.5
API / mo$2,700
Self-host / mo$5,221
Break-even132M/day
Model the full break-even

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