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Model comparison

GLM-5.2 vs Kimi K2.6

Data verified

Head-to-head evidence from 30 shared benchmark results across 7 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.

63.96/100
Margin
7.2pts
← winning
Kimi K2.6
Moonshot AI
56.79/100
3 category wins1 category wins

Public leaderboard positions: GLM-5.2 #37 (Estimated); Kimi K2.6 #74 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.

Evidence parity. GLM-5.2 and Kimi K2.6 share 30 comparable benchmark results. 4 of 8 categories are comparable. 13 results are unique to GLM-5.2; 21 to Kimi K2.6.

Updated July 23, 2026
Shared results
30
GLM-5.2 only
13
Kimi K2.6 only
21
Comparable categories
4 / 8

Pick GLM-5.2 if you want the stronger benchmark profile. Kimi K2.6 only becomes the better choice if coding is the priority or you want the cheaper token bill.

Confidence note. This is a partial-evidence comparison with 30 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

GLM-5.2 is clearly ahead on the BenchAlign aggregate, 63.96 to 56.79. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.

GLM-5.2's sharpest advantage is in mathematics, where it averages 95.9 against 67.1. The single biggest benchmark swing on the page is HLE, 54.7% to 34.7%. Kimi K2.6 does hit back in coding, so the answer changes if that is the part of the workload you care about most.

GLM-5.2 is also the more expensive model on tokens at $1.40 input / $4.40 output per 1M tokens, versus $0.95 input / $4.00 output per 1M tokens for Kimi K2.6. GLM-5.2 gives you the larger context window at 1M, compared with 256K for Kimi K2.6.

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 GLM-5.2 and Kimi K2.6
CategoryGLM-5.2ΔKimi K2.6
MathGLM-5.295.9Margin← 28.8Kimi K2.667.1
KnowledgeGLM-5.259.6Margin← 17.4Kimi K2.642.2
AgenticGLM-5.281.0Margin← 7.5Kimi K2.673.5
CodingGLM-5.262.1Margin→ 2.3Kimi K2.664.4
MultimodalGLM-5.2Not measuredMarginNo overlapKimi K2.679.8

Decisive benchmark drivers

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

More
A · GLM-5.2B · Kimi K2.6

  1. HLE

    Knowledge
    Source ↗
    A 54.7%B 34.7%
    Winner: GLM-5.2Δ 20
    HLE: GLM-5.2 scored 54.7%; Kimi K2.6 scored 34.7%. GLM-5.2 wins this benchmark.

  2. Terminal-Bench 2.0

    Agentic
    Source ↗
    A 81%B 66.7%
    Winner: GLM-5.2Δ 14.3
    Terminal-Bench 2.0: GLM-5.2 scored 81%; Kimi K2.6 scored 66.7%. GLM-5.2 wins this benchmark.

  3. SWE-bench Pro

    Coding
    Source ↗
    A 62.1%B 58.6%
    Winner: GLM-5.2Δ 3.5
    SWE-bench Pro: GLM-5.2 scored 62.1%; Kimi K2.6 scored 58.6%. GLM-5.2 wins this benchmark.

  4. AIME26

    Math
    Source ↗
    A 99.2%B 96.4%
    Winner: GLM-5.2Δ 2.8
    AIME26: GLM-5.2 scored 99.2%; Kimi K2.6 scored 96.4%. GLM-5.2 wins this benchmark.

  5. GPQA

    Knowledge
    Source ↗
    A 91.2%B 90.5%
    Winner: GLM-5.2Δ 0.7
    GPQA: GLM-5.2 scored 91.2%; Kimi K2.6 scored 90.5%. GLM-5.2 wins this benchmark.

Operational comparison

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

MetricGLM-5.2Kimi K2.6Comparison
Input / output priceUSD per 1M tokensGLM-5.2$1.4 input / $4.4 outputKimi K2.6$0.95 input / $4 outputKimi K2.6 has the lower combined listed price.
Generation speedtokens per secondGLM-5.2Not availableKimi K2.6Not availableA complete speed comparison is not available.
First-answer latencyseconds to first tokenGLM-5.2Not availableKimi K2.6Not availableA complete latency comparison is not available.
Context windowmaximum listed tokensGLM-5.21MKimi K2.6256KGLM-5.2 lists the larger context window.

Benchmark Deep Dive

AgenticGLM-5.2 wins
BenchmarkGLM-5.2Kimi K2.6Result
Terminal-Bench 2.0Source 81%66.7%GLM-5.2 leads
MCP AtlasSource 76.8%55.9%GLM-5.2 leads
ToolathlonSource 48.2%50%Kimi K2.6 leads
AA Agentic IndexSource 43.1%30.3%GLM-5.2 leads
τ²-bench resultsSource 99.1%95.9%GLM-5.2 leads
GDPval-AASource 50.7%34.5%GLM-5.2 leads
GDPval-AASource 15141189GLM-5.2 leads
APEX-Agents-AASource 33.7%28.5%GLM-5.2 leads
ResearchClawBenchSource 20.7%18.0%GLM-5.2 leads
AA BriefcaseSource 1260—Not comparable
AA AutomationBenchSource 27.8%—Not comparable
AA EnterpriseOps-GymSource 42.7%—Not comparable
AA Harvey LABSource 91.0%—Not comparable
AA ITBenchSource 42.7%—Not comparable
AA Tau3 BankingSource 26.8%—Not comparable
terminalBenchHardSource 50.8%43.9%GLM-5.2 leads
aaTerminalBench21Source 77.9%—Not comparable
BrowseCompSource —83.2%Not comparable
OSWorld-VerifiedSource —73.1%Not comparable
Claw-EvalSource —62.3%Not comparable
DeepSearchQASource —92.5%Not comparable
WideResearchSource —80.8%Not comparable
Gert LabsSource —56.82%Not comparable
OSWorld 2.0Source —4.6%Not comparable
CodingKimi K2.6 wins
BenchmarkGLM-5.2Kimi K2.6Result
SWE-bench ProSource 62.1%58.6%GLM-5.2 leads
NL2RepoSource 48.9%—Not comparable
Terminal-Bench 2.0Source 81.0%66.7%GLM-5.2 leads
ProgramBenchSource 63.7%—Not comparable
cursorBench32Source 55.0%—Not comparable
AA Coding IndexSource 68.8%61.8%GLM-5.2 leads
AA-SciCodeSource 50.5%53.5%Kimi K2.6 leads
SWE-bench VerifiedSource —80.2%Not comparable
LiveCodeBench v6Source —89.6%Not comparable
SWE MultilingualSource —76.7%Not comparable
SciCodeSource —52.2%Not comparable
Vibe Code BenchSource —37.89%Not comparable
cursorBench31Source —47.6%Not comparable
Reasoning
BenchmarkGLM-5.2Kimi K2.6Result
CritPtSource 20.9%8.0%GLM-5.2 leads
AA-LCRSource 71.3%69.7%GLM-5.2 leads
KnowledgeGLM-5.2 wins
BenchmarkGLM-5.2Kimi K2.6Result
GPQASource 91.2%90.5%GLM-5.2 leads
GPQA-DSource 91.2%90.5%GLM-5.2 leads
HLESource 54.7%34.7%GLM-5.2 leads
HLE w/o toolsSource 40.5%—Not comparable
Artificial Analysis Intelligence IndexSource 51.1%44.2%GLM-5.2 leads
AA-GPQA DiamondSource 89.5%91.1%Kimi K2.6 leads
AA-HLESource 40.1%35.9%GLM-5.2 leads
AA-Omniscience IndexSource 4.0%6.4%Kimi K2.6 leads
AA-Omniscience AccuracySource 25.1%32.8%Kimi K2.6 leads
AA-Omniscience Hallucination RateSource 28.1%39.3%GLM-5.2 leads
AA Openness IndexSource 44.4%—Not comparable
MathGLM-5.2 wins
BenchmarkGLM-5.2Kimi K2.6Result
AIME26Source 99.2%96.4%GLM-5.2 leads
HMMT Nov 2025Source 94.4%—Not comparable
HMMT Feb 2026Source 92.5%92.7%Kimi K2.6 leads
MMAnswerBenchSource 91.0%86.0%GLM-5.2 leads
FrontierMath v2 (Tiers 1-3)Source —38.966%Not comparable
FrontierMath v2 (Tier 4)Source —14.580%Not comparable
Multimodal
BenchmarkGLM-5.2Kimi K2.6Result
Design Arena WebsiteSource 13401306GLM-5.2 leads
MMMU-ProSource —79.4%Not comparable
MMMU-Pro w/ PythonSource —80.1%Not comparable
CharXivSource —80.4%Not comparable
MathVisionSource —87.4%Not comparable
V*Source —96.9%Not comparable
AA-MMMU-ProSource —79.4%Not comparable
Inst. Following
BenchmarkGLM-5.2Kimi K2.6Result
AA-IFBenchSource 73.3%76.0%Kimi K2.6 leads
Frequently Asked Questions (5)

Which is better, GLM-5.2 or Kimi K2.6?

GLM-5.2 is ahead on BenchLM's BenchAlign leaderboard, 63.96 to 56.79. The biggest single separator in this matchup is HLE, where the scores are 54.7% and 34.7%.

Which is better for knowledge tasks, GLM-5.2 or Kimi K2.6?

GLM-5.2 has the edge for knowledge tasks in this comparison, averaging 59.6 versus 42.2. Inside this category, HLE is the benchmark that creates the most daylight between them.

Which is better for coding, GLM-5.2 or Kimi K2.6?

Kimi K2.6 has the edge for coding in this comparison, averaging 64.4 versus 62.1. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.

Which is better for math, GLM-5.2 or Kimi K2.6?

GLM-5.2 has the edge for math in this comparison, averaging 95.9 versus 67.1. Inside this category, MMAnswerBench is the benchmark that creates the most daylight between them.

Which is better for agentic tasks, GLM-5.2 or Kimi K2.6?

GLM-5.2 has the edge for agentic tasks in this comparison, averaging 81 versus 73.5. 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.

GLM-5.2
API / mo$4,350
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Kimi K2.6
API / mo$3,713
Self-host / mo$18,221
Break-even326M/day
Model the full break-even

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