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Model comparison

GLM-5 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.

GLM-5
Z.AI
66.06/100
Margin
9.3pts
← winning
Kimi K2.6
Moonshot AI
56.79/100
2 category wins2 category wins

Public leaderboard positions: GLM-5 #28 (Supported); Kimi K2.6 #74 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.

Evidence parity. GLM-5 and Kimi K2.6 share 30 comparable benchmark results. 4 of 8 categories are comparable. 19 results are unique to GLM-5; 21 to Kimi K2.6.

Updated July 23, 2026
Shared results
30
GLM-5 only
19
Kimi K2.6 only
21
Comparable categories
4 / 8

Pick GLM-5 if you want the stronger benchmark profile. Kimi K2.6 only becomes the better choice if agentic is the priority or you need the larger 256K context window.

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 is clearly ahead on the BenchAlign aggregate, 66.06 to 56.79. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.

GLM-5's sharpest advantage is in knowledge, where it averages 66.4 against 42.2. The single biggest benchmark swing on the page is FrontierMath v2 (Tiers 1-3), 16.434% to 38.966%. Kimi K2.6 does hit back in agentic, so the answer changes if that is the part of the workload you care about most.

Kimi K2.6 is also the more expensive model on tokens at $0.95 input / $4.00 output per 1M tokens, versus $1.00 input / $3.20 output per 1M tokens for GLM-5. Kimi K2.6 is the reasoning model in the pair, while GLM-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. Kimi K2.6 gives you the larger context window at 256K, compared with 200K for GLM-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 GLM-5 and Kimi K2.6
CategoryGLM-5ΔKimi K2.6
KnowledgeGLM-566.4Margin← 24.2Kimi K2.642.2
AgenticGLM-556.2Margin→ 17.3Kimi K2.673.5
MathGLM-556.3Margin→ 10.8Kimi K2.667.1
CodingGLM-566.3Margin← 1.9Kimi K2.664.4
ReasoningGLM-560.8MarginNo overlapKimi K2.6Not measured
MultilingualGLM-583.1MarginNo overlapKimi K2.6Not measured
MultimodalGLM-5Not measuredMarginNo overlapKimi K2.679.8
Inst. FollowingGLM-592.6MarginNo overlapKimi K2.6Not measured

Decisive benchmark drivers

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

More
A · GLM-5B · Kimi K2.6

  1. FrontierMath v2 (Tiers 1-3)

    Math
    Source ↗
    A 16.434%B 38.966%
    Winner: Kimi K2.6Δ 22.5
    FrontierMath v2 (Tiers 1-3): GLM-5 scored 16.434%; Kimi K2.6 scored 38.966%. Kimi K2.6 wins this benchmark.

  2. HLE

    Knowledge
    Source ↗
    A 50.4%B 34.7%
    Winner: GLM-5Δ 15.7
    HLE: GLM-5 scored 50.4%; Kimi K2.6 scored 34.7%. GLM-5 wins this benchmark.

  3. FrontierMath v2 (Tier 4)

    Math
    Source ↗
    A 2.100%B 14.580%
    Winner: Kimi K2.6Δ 12.5
    FrontierMath v2 (Tier 4): GLM-5 scored 2.100%; Kimi K2.6 scored 14.580%. Kimi K2.6 wins this benchmark.

  4. Terminal-Bench 2.0

    Agentic
    Source ↗
    A 56.2%B 66.7%
    Winner: Kimi K2.6Δ 10.5
    Terminal-Bench 2.0: GLM-5 scored 56.2%; Kimi K2.6 scored 66.7%. Kimi K2.6 wins this benchmark.

  5. HMMT Feb 2026

    Math
    Source ↗
    A 86.4%B 92.7%
    Winner: Kimi K2.6Δ 6.3
    HMMT Feb 2026: GLM-5 scored 86.4%; Kimi K2.6 scored 92.7%. Kimi K2.6 wins this benchmark.

Operational comparison

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

MetricGLM-5Kimi K2.6Comparison
Input / output priceUSD per 1M tokensGLM-5$1 input / $3.2 outputKimi K2.6$0.95 input / $4 outputGLM-5 has the lower combined listed price.
Generation speedtokens per secondGLM-574 tok/sKimi K2.6Not availableA complete speed comparison is not available.
First-answer latencyseconds to first tokenGLM-51.64 sKimi K2.6Not availableA complete latency comparison is not available.
Context windowmaximum listed tokensGLM-5200KKimi K2.6256KKimi K2.6 lists the larger context window.

Benchmark Deep Dive

AgenticKimi K2.6 wins
BenchmarkGLM-5Kimi K2.6Result
Terminal-Bench 2.0Source 56.2%66.7%Kimi K2.6 leads
Claw-EvalSource 57.7%62.3%Kimi K2.6 leads
QwenClawBenchSource 54.1%—Not comparable
τ³-bench resultsSource 65.6%—Not comparable
DeepPlanningSource 14.6%—Not comparable
ToolathlonSource 38%50%Kimi K2.6 leads
MCP AtlasSource 31.1%55.9%Kimi K2.6 leads
MCP-TasksSource 60.8%—Not comparable
WideResearchSource 69.8%80.8%Kimi K2.6 leads
τ²-bench resultsSource 98.2%95.9%GLM-5 leads
CyberGymSource 43.2%—Not comparable
APEX-Agents-AASource 14.5%28.5%Kimi K2.6 leads
Gert LabsSource 50.99%56.82%Kimi K2.6 leads
BrowseCompSource —83.2%Not comparable
OSWorld-VerifiedSource —73.1%Not comparable
DeepSearchQASource —92.5%Not comparable
AA Agentic IndexSource —30.3%Not comparable
GDPval-AASource —34.5%Not comparable
GDPval-AASource —1189Not comparable
ResearchClawBenchSource —18.0%Not comparable
OSWorld 2.0Source —4.6%Not comparable
terminalBenchHardSource —43.9%Not comparable
CodingGLM-5 wins
BenchmarkGLM-5Kimi K2.6Result
SWE-bench VerifiedSource 77.8%80.2%Kimi K2.6 leads
SWE-bench Verified*Source 72.8%—Not comparable
SWE-bench ProSource 55.1%58.6%Kimi K2.6 leads
SWE MultilingualSource 73.3%76.7%Kimi K2.6 leads
SWE-RebenchSource 62.8%—Not comparable
React Native EvalsSource 74.8%—Not comparable
AA-SciCodeSource 46.2%53.5%Kimi K2.6 leads
LiveCodeBench v6Source —89.6%Not comparable
SciCodeSource —52.2%Not comparable
Terminal-Bench 2.0Source —66.7%Not comparable
Vibe Code BenchSource —37.89%Not comparable
cursorBench31Source —47.6%Not comparable
AA Coding IndexSource —61.8%Not comparable
Reasoning
BenchmarkGLM-5Kimi K2.6Result
LongBench v2Source 60.8%—Not comparable
AI-NeedleSource 63.3%—Not comparable
AA-LCRSource 63.3%69.7%Kimi K2.6 leads
CritPtSource 2.0%8.0%Kimi K2.6 leads
KnowledgeGLM-5 wins
BenchmarkGLM-5Kimi K2.6Result
GPQASource 86%90.5%Kimi K2.6 leads
GPQA-DSource 86.0%90.5%Kimi K2.6 leads
SuperGPQASource 66.8%—Not comparable
MMLU-ProSource 85.7%—Not comparable
MMLU-Pro (Arcee)Source 85.8%—Not comparable
HLESource 50.4%34.7%GLM-5 leads
Artificial Analysis Intelligence IndexSource 39.5%44.2%Kimi K2.6 leads
AA-GPQA DiamondSource 82.0%91.1%Kimi K2.6 leads
AA-HLESource 27.2%35.9%Kimi K2.6 leads
AA-Omniscience IndexSource 2.0%6.4%Kimi K2.6 leads
AA-Omniscience AccuracySource 26.9%32.8%Kimi K2.6 leads
AA-Omniscience Hallucination RateSource 34.0%39.3%GLM-5 leads
MathKimi K2.6 wins
BenchmarkGLM-5Kimi K2.6Result
AIME26Source 95.8%96.4%Kimi K2.6 leads
AIME25 (Arcee)Source 93.3%—Not comparable
HMMT Feb 2025Source 97.5%—Not comparable
HMMT Nov 2025Source 96.9%—Not comparable
HMMT Feb 2026Source 86.4%92.7%Kimi K2.6 leads
MMAnswerBenchSource 82.5%86.0%Kimi K2.6 leads
FrontierMath v2 (Tiers 1-3)Source 16.434%38.966%Kimi K2.6 leads
FrontierMath v2 (Tier 4)Source 2.100%14.580%Kimi K2.6 leads
Multilingual
BenchmarkGLM-5Kimi K2.6Result
MMLU-ProXSource 83.1%—Not comparable
NOVA-63Source 55.1%—Not comparable
Multimodal
BenchmarkGLM-5Kimi K2.6Result
Design Arena WebsiteSource 12781306Kimi K2.6 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-5Kimi K2.6Result
IFEvalSource 92.6%—Not comparable
AA-IFBenchSource 72.3%76.0%Kimi K2.6 leads
Frequently Asked Questions (5)

Which is better, GLM-5 or Kimi K2.6?

GLM-5 is ahead on BenchLM's BenchAlign leaderboard, 66.06 to 56.79. The biggest single separator in this matchup is FrontierMath v2 (Tiers 1-3), where the scores are 16.434% and 38.966%.

Which is better for knowledge tasks, GLM-5 or Kimi K2.6?

GLM-5 has the edge for knowledge tasks in this comparison, averaging 66.4 versus 42.2. Inside this category, HLE is the benchmark that creates the most daylight between them.

Which is better for coding, GLM-5 or Kimi K2.6?

GLM-5 has the edge for coding in this comparison, averaging 66.3 versus 64.4. Inside this category, AA-SciCode is the benchmark that creates the most daylight between them.

Which is better for math, GLM-5 or Kimi K2.6?

Kimi K2.6 has the edge for math in this comparison, averaging 67.1 versus 56.3. Inside this category, FrontierMath v2 (Tiers 1-3) is the benchmark that creates the most daylight between them.

Which is better for agentic tasks, GLM-5 or Kimi K2.6?

Kimi K2.6 has the edge for agentic tasks in this comparison, averaging 73.5 versus 56.2. Inside this category, MCP Atlas 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
API / mo$3,150
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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