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Model comparison

Kimi K2.5 vs Qwen3.6 Plus

Data verified

Head-to-head evidence from 49 shared benchmark results across 8 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.

Moonshot AI
59.66/100
Margin
5.5pts
winning →
65.2/100
2 category wins6 category wins

Public leaderboard positions: Kimi K2.5 #54 (Supported); Qwen3.6 Plus #30 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.

Evidence parity. Kimi K2.5 and Qwen3.6 Plus share 49 comparable benchmark results. 8 of 8 categories are comparable. 14 results are unique to Kimi K2.5; 11 to Qwen3.6 Plus.

Updated July 23, 2026
Shared results
49
Kimi K2.5 only
14
Qwen3.6 Plus only
11
Comparable categories
8 / 8

Pick Qwen3.6 Plus if you want the stronger benchmark profile. Kimi K2.5 only becomes the better choice if instruction following is the priority or you would rather avoid the extra latency and token burn of a reasoning model.

Why this result

Qwen3.6 Plus is clearly ahead on the BenchAlign aggregate, 65.2 to 59.66. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.

Qwen3.6 Plus's sharpest advantage is in coding, where it averages 70.3 against 59.4. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 50.8% to 61.6%. Kimi K2.5 does hit back in instruction following, so the answer changes if that is the part of the workload you care about most.

Qwen3.6 Plus 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. Qwen3.6 Plus 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 Kimi K2.5 and Qwen3.6 Plus
CategoryKimi K2.5ΔQwen3.6 Plus
Inst. FollowingKimi K2.593.9Margin 11.6Qwen3.6 Plus82.3
CodingKimi K2.559.4Margin 10.9Qwen3.6 Plus70.3
AgenticKimi K2.555.0Margin 6.6Qwen3.6 Plus61.6
MultilingualKimi K2.582.3Margin 2.4Qwen3.6 Plus84.7
MultimodalKimi K2.578.5Margin 1.3Qwen3.6 Plus79.8
ReasoningKimi K2.561.0Margin 1.0Qwen3.6 Plus62.0
KnowledgeKimi K2.556.9Margin 0.2Qwen3.6 Plus57.1
MathKimi K2.560.6Margin 0.1Qwen3.6 Plus60.5

Decisive benchmark drivers

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

More
A · Kimi K2.5B · Qwen3.6 Plus
  1. Terminal-Bench 2.0

    Agentic
    Source ↗
    A 50.8%B 61.6%
    Winner: Qwen3.6 PlusΔ 10.8
    Terminal-Bench 2.0: Kimi K2.5 scored 50.8%; Qwen3.6 Plus scored 61.6%. Qwen3.6 Plus wins this benchmark.
  2. SWE-bench Pro

    Coding
    Source ↗
    A 50.7%B 56.6%
    Winner: Qwen3.6 PlusΔ 5.9
    SWE-bench Pro: Kimi K2.5 scored 50.7%; Qwen3.6 Plus scored 56.6%. Qwen3.6 Plus wins this benchmark.
  3. FrontierMath v2 (Tier 4)

    Math
    Source ↗
    A 4.200%B 8.333%
    Winner: Qwen3.6 PlusΔ 4.1
    FrontierMath v2 (Tier 4): Kimi K2.5 scored 4.200%; Qwen3.6 Plus scored 8.333%. Qwen3.6 Plus wins this benchmark.
  4. GPQA

    Knowledge
    Source ↗
    A 87.6%B 90.4%
    Winner: Qwen3.6 PlusΔ 2.8
    GPQA: Kimi K2.5 scored 87.6%; Qwen3.6 Plus scored 90.4%. Qwen3.6 Plus wins this benchmark.
  5. MMLU-ProX

    Multilingual
    Source ↗
    A 82.3%B 84.7%
    Winner: Qwen3.6 PlusΔ 2.4
    MMLU-ProX: Kimi K2.5 scored 82.3%; Qwen3.6 Plus scored 84.7%. Qwen3.6 Plus wins this benchmark.

Operational comparison

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

MetricKimi K2.5Qwen3.6 PlusComparison
Input / output priceUSD per 1M tokensKimi K2.5$0.6 input / $3 outputQwen3.6 PlusNot availableA complete price comparison is not available.
Generation speedtokens per secondKimi K2.545 tok/sQwen3.6 PlusNot availableA complete speed comparison is not available.
First-answer latencyseconds to first tokenKimi K2.52.38 sQwen3.6 PlusNot availableA complete latency comparison is not available.
Context windowmaximum listed tokensKimi K2.5256KQwen3.6 Plus1MQwen3.6 Plus lists the larger context window.

Benchmark Deep Dive

AgenticQwen3.6 Plus wins
BenchmarkKimi K2.5Qwen3.6 PlusResult
Terminal-Bench 2.0Source 50.8%61.6%Qwen3.6 Plus leads
BrowseCompSource 60.6%Not comparable
Claw-EvalSource 52.3%58.8%Qwen3.6 Plus leads
QwenClawBenchSource 54.3%57.2%Qwen3.6 Plus leads
τ³-bench resultsSource 65.7%70.7%Qwen3.6 Plus leads
DeepSearchQASource 77.1%Not comparable
DeepPlanningSource 14.4%41.5%Qwen3.6 Plus leads
ToolathlonSource 27.8%39.8%Qwen3.6 Plus leads
MCP AtlasSource 29.5%48.2%Qwen3.6 Plus leads
MCP-TasksSource 59.1%74.1%Qwen3.6 Plus leads
WideResearchSource 72.7%74.3%Qwen3.6 Plus leads
τ²-bench resultsSource 95.9%97.7%Qwen3.6 Plus leads
APEX-Agents-AASource 11.5%Not comparable
Gert LabsSource 45.88%50.60%Qwen3.6 Plus leads
ResearchClawBenchSource 14.0%18.0%Qwen3.6 Plus leads
JobBenchSource 8.7%Not comparable
AA Agentic IndexSource 21.7%27.6%Qwen3.6 Plus leads
GDPval-AASource 25.4%31.8%Qwen3.6 Plus leads
GDPval-AASource 10091135Qwen3.6 Plus leads
VITA-BenchSource 44.3%Not comparable
CodingQwen3.6 Plus wins
BenchmarkKimi K2.5Qwen3.6 PlusResult
SWE-bench VerifiedSource 76.8%78.8%Qwen3.6 Plus leads
SWE-bench Verified*Source 70.8%Not comparable
LiveCodeBench v6Source 85.0%87.1%Qwen3.6 Plus leads
SWE-bench ProSource 50.7%56.6%Qwen3.6 Plus leads
SWE MultilingualSource 73%73.8%Qwen3.6 Plus leads
SWE-RebenchSource 58.5%Not comparable
React Native EvalsSource 77.2%Not comparable
SciCodeSource 48.7%Not comparable
AA-SciCodeSource 49.0%40.7%Kimi K2.5 leads
AA Coding IndexSource 46.8%54.5%Qwen3.6 Plus leads
Vibe Code BenchSource 25.56%Not comparable
ReasoningQwen3.6 Plus wins
BenchmarkKimi K2.5Qwen3.6 PlusResult
LongBench v2Source 61%62%Qwen3.6 Plus leads
AA-LCRSource 65.3%69.7%Qwen3.6 Plus leads
CritPtSource 3.1%2.9%Kimi K2.5 leads
AI-NeedleSource 68.3%Not comparable
KnowledgeQwen3.6 Plus wins
BenchmarkKimi K2.5Qwen3.6 PlusResult
GPQASource 87.6%90.4%Qwen3.6 Plus leads
GPQA-DSource 87.6%Not comparable
SuperGPQASource 69.2%71.6%Qwen3.6 Plus leads
MMLU-ProSource 87.1%88.5%Qwen3.6 Plus leads
MMLU-Pro (Arcee)Source 87.1%Not comparable
HLESource 30.1%28.8%Kimi K2.5 leads
Artificial Analysis Intelligence IndexSource 35.4%39.6%Qwen3.6 Plus leads
AA-GPQA DiamondSource 87.9%88.2%Qwen3.6 Plus leads
AA-HLESource 29.4%25.7%Kimi K2.5 leads
AA-Omniscience IndexSource -8.1%2.7%Qwen3.6 Plus leads
AA-Omniscience AccuracySource 34.3%26.2%Kimi K2.5 leads
AA-Omniscience Hallucination RateSource 64.6%32.0%Qwen3.6 Plus leads
MMLU-ReduxSource 94.5%Not comparable
C-EvalSource 93.3%Not comparable
MathKimi K2.5 wins
BenchmarkKimi K2.5Qwen3.6 PlusResult
AIME 2025Source 96.1%Not comparable
AIME26Source 95.8%95.3%Kimi K2.5 leads
AIME25 (Arcee)Source 96.3%Not comparable
HMMT Feb 2025Source 95.4%96.7%Qwen3.6 Plus leads
HMMT Nov 2025Source 91.1%94.6%Qwen3.6 Plus leads
HMMT Feb 2026Source 87.1%87.8%Qwen3.6 Plus leads
MMAnswerBenchSource 81.8%83.8%Qwen3.6 Plus leads
FrontierMath v2 (Tiers 1-3)Source 27.900%26.207%Kimi K2.5 leads
FrontierMath v2 (Tier 4)Source 4.200%8.333%Qwen3.6 Plus leads
MultilingualQwen3.6 Plus wins
BenchmarkKimi K2.5Qwen3.6 PlusResult
MMLU-ProXSource 82.3%84.7%Qwen3.6 Plus leads
NOVA-63Source 56.0%57.9%Qwen3.6 Plus leads
MultimodalQwen3.6 Plus wins
BenchmarkKimi K2.5Qwen3.6 PlusResult
MMMU-ProSource 78.5%78.8%Qwen3.6 Plus leads
Video-MMESource 87.4%Not comparable
MMVUSource 80.4%Not comparable
VideoMMMUSource 86.6%84.0%Kimi K2.5 leads
AA-MMMU-ProSource 75.4%78.0%Qwen3.6 Plus leads
Design Arena WebsiteSource 12791249Kimi K2.5 leads
MMMUSource 86.0%Not comparable
MathVisionSource 88.0%Not comparable
ScreenSpot ProSource 68.2%Not comparable
CharXivSource 81.5%Not comparable
V*Source 96.9%Not comparable
Inst. FollowingKimi K2.5 wins
BenchmarkKimi K2.5Qwen3.6 PlusResult
IFEvalSource 93.9%94.3%Qwen3.6 Plus leads
AA-IFBenchSource 70.2%75.2%Qwen3.6 Plus leads
IFBenchSource 75.8%Not comparable
Frequently Asked Questions (9)

Which is better, Kimi K2.5 or Qwen3.6 Plus?

Qwen3.6 Plus is ahead on BenchLM's BenchAlign leaderboard, 65.2 to 59.66. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 50.8% and 61.6%.

Which is better for knowledge tasks, Kimi K2.5 or Qwen3.6 Plus?

Qwen3.6 Plus has the edge for knowledge tasks in this comparison, averaging 57.1 versus 56.9. Inside this category, AA-Omniscience Hallucination Rate is the benchmark that creates the most daylight between them.

Which is better for coding, Kimi K2.5 or Qwen3.6 Plus?

Qwen3.6 Plus has the edge for coding in this comparison, averaging 70.3 versus 59.4. Inside this category, AA-SciCode is the benchmark that creates the most daylight between them.

Which is better for math, Kimi K2.5 or Qwen3.6 Plus?

Kimi K2.5 has the edge for math in this comparison, averaging 60.6 versus 60.5. Inside this category, FrontierMath v2 (Tier 4) is the benchmark that creates the most daylight between them.

Which is better for reasoning, Kimi K2.5 or Qwen3.6 Plus?

Qwen3.6 Plus has the edge for reasoning in this comparison, averaging 62 versus 61. Inside this category, AA-LCR is the benchmark that creates the most daylight between them.

Which is better for agentic tasks, Kimi K2.5 or Qwen3.6 Plus?

Qwen3.6 Plus has the edge for agentic tasks in this comparison, averaging 61.6 versus 55. Inside this category, GDPval-AA is the benchmark that creates the most daylight between them.

Which is better for multimodal and grounded tasks, Kimi K2.5 or Qwen3.6 Plus?

Qwen3.6 Plus has the edge for multimodal and grounded tasks in this comparison, averaging 79.8 versus 78.5. Inside this category, Design Arena Website is the benchmark that creates the most daylight between them.

Which is better for instruction following, Kimi K2.5 or Qwen3.6 Plus?

Kimi K2.5 has the edge for instruction following in this comparison, averaging 93.9 versus 82.3. Inside this category, AA-IFBench is the benchmark that creates the most daylight between them.

Which is better for multilingual tasks, Kimi K2.5 or Qwen3.6 Plus?

Qwen3.6 Plus has the edge for multilingual tasks in this comparison, averaging 84.7 versus 82.3. Inside this category, MMLU-ProX 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.

Kimi K2.5
API / mo$2,700
Self-host / mo$5,221
Break-even132M/day
Qwen3.6 Plus
API / mo$0
Self-host / moNot listed
Break-even
Proprietary model — self-hosting not applicable.
Model the full break-even

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Last updated: July 23, 2026

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