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Model comparison

GLM-4.7 vs Kimi K2.6

Data verified

Head-to-head evidence from 26 shared benchmark results across 7 categories. Overall scores shown here use BenchLM's provisional ranking lane.

63/100
Margin
11.0pts
winning →
Kimi K2.6
Moonshot AI
74/100
2 category wins2 category wins

Verified leaderboard positions: GLM-4.7 #34; Kimi K2.6 #13

Evidence parity. GLM-4.7 and Kimi K2.6 share 26 comparable benchmark results. 4 of 8 categories are comparable. 5 results are unique to GLM-4.7; 34 to Kimi K2.6.

Updated July 13, 2026
Shared results
26
GLM-4.7 only
5
Kimi K2.6 only
34
Comparable categories
4 / 8

Pick Kimi K2.6 if you want the stronger benchmark profile. GLM-4.7 only becomes the better choice if knowledge is the priority or you want the cheaper token bill.

Confidence note. This is a partial-evidence comparison with 26 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 K2.6 is clearly ahead on the provisional aggregate, 74 to 63. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.

Kimi K2.6's sharpest advantage is in mathematics, where it averages 67.1 against 1.8. The single biggest benchmark swing on the page is FrontierMath v2 (Tiers 1-3), 2.439% to 38.966%. GLM-4.7 does hit back in knowledge, 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 $0.00 input / $0.00 output per 1M tokens for GLM-4.7. That is roughly Infinityx on output cost alone. Kimi K2.6 gives you the larger context window at 256K, compared with 200K for GLM-4.7.

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-4.7 and Kimi K2.6
CategoryGLM-4.7ΔKimi K2.6
MathGLM-4.71.8Margin→ 65.3Kimi K2.667.1
AgenticGLM-4.745.7Margin→ 27.8Kimi K2.673.5
KnowledgeGLM-4.752.1Margin← 9.9Kimi K2.642.2
CodingGLM-4.773.8Margin← 1.2Kimi K2.672.6
MultimodalGLM-4.7Not measuredMarginNo overlapKimi K2.679.8

Decisive benchmark drivers

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

More
A · GLM-4.7B · Kimi K2.6

  1. FrontierMath v2 (Tiers 1-3)

    Math
    Source ↗
    A 2.439%B 38.966%
    Winner: Kimi K2.6Δ 36.5
    FrontierMath v2 (Tiers 1-3): GLM-4.7 scored 2.439%; Kimi K2.6 scored 38.966%. Kimi K2.6 wins this benchmark.

  2. BrowseComp

    Agentic
    Source ↗
    A 52%B 83.2%
    Winner: Kimi K2.6Δ 31.2
    BrowseComp: GLM-4.7 scored 52%; Kimi K2.6 scored 83.2%. Kimi K2.6 wins this benchmark.

  3. Terminal-Bench 2.0

    Agentic
    Source ↗
    A 41%B 66.7%
    Winner: Kimi K2.6Δ 25.7
    Terminal-Bench 2.0: GLM-4.7 scored 41%; Kimi K2.6 scored 66.7%. Kimi K2.6 wins this benchmark.

  4. FrontierMath v2 (Tier 4)

    Math
    Source ↗
    A 0.000%B 14.580%
    Winner: Kimi K2.6Δ 14.6
    FrontierMath v2 (Tier 4): GLM-4.7 scored 0.000%; Kimi K2.6 scored 14.580%. Kimi K2.6 wins this benchmark.

  5. HLE

    Knowledge
    Source ↗
    A 24.8%B 34.7%
    Winner: Kimi K2.6Δ 9.9
    HLE: GLM-4.7 scored 24.8%; Kimi K2.6 scored 34.7%. Kimi K2.6 wins this benchmark.

Operational comparison

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

MetricGLM-4.7Kimi K2.6Comparison
Input / output priceUSD per 1M tokensGLM-4.7$0 input / $0 outputKimi K2.6$0.95 input / $4 outputGLM-4.7 has the lower combined listed price.
Generation speedtokens per secondGLM-4.782 tok/sKimi K2.6Not availableA complete speed comparison is not available.
First-answer latencyseconds to first tokenGLM-4.71.10 sKimi K2.6Not availableA complete latency comparison is not available.
Context windowmaximum listed tokensGLM-4.7200KKimi K2.6256KKimi K2.6 lists the larger context window.

Benchmark Deep Dive

AgenticKimi K2.6 wins
BenchmarkGLM-4.7Kimi K2.6Result
Terminal-Bench 2.0Source 41%66.7%Kimi K2.6 leads
BrowseCompSource 52%83.2%Kimi K2.6 leads
VITA-BenchSource 15.5%—Not comparable
AA Agentic IndexSource 25.4%30.3%Kimi K2.6 leads
Tau2-TelecomSource 95.9%95.9%Tie
Gert LabsSource 39.95%56.82%Kimi K2.6 leads
GDPval-AASource 33.1%34.5%Kimi K2.6 leads
GDPval-AASource 11631190Kimi K2.6 leads
OSWorld-VerifiedSource —73.1%Not comparable
ToolathlonSource —50%Not comparable
MCP AtlasSource —55.9%Not comparable
Claw-EvalSource —62.3%Not comparable
DeepSearchQASource —92.5%Not comparable
WideResearchSource —80.8%Not comparable
APEX-Agents-AASource —28.5%Not comparable
ResearchClawBenchSource —18.0%Not comparable
OSWorld 2.0Source —4.6%Not comparable
AA BriefcaseSource —809Not comparable
AA AutomationBenchSource —19.6%Not comparable
AA EnterpriseOps-GymSource —38.5%Not comparable
AA Harvey LABSource —0.0%Not comparable
AA ITBenchSource —31.2%Not comparable
AA Tau3 BankingSource —20.6%Not comparable
CodingGLM-4.7 wins
BenchmarkGLM-4.7Kimi K2.6Result
SWE-bench VerifiedSource 73.8%80.2%Kimi K2.6 leads
LiveCodeBenchSource 84.9%89.6%Kimi K2.6 leads
SWE-RebenchSource 58.7%—Not comparable
AA Coding IndexSource 45.3%61.8%Kimi K2.6 leads
Terminal-Bench HardSource 31.8%43.9%Kimi K2.6 leads
AA-SciCodeSource 45.1%53.5%Kimi K2.6 leads
AA LiveCodeBenchSource 89.4%—Not comparable
LiveCodeBench v6Source —89.6%Not comparable
SWE-bench ProSource —58.6%Not comparable
SWE MultilingualSource —76.7%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 Terminal-Bench 2.1Source —65.9%Not comparable
Reasoning
BenchmarkGLM-4.7Kimi K2.6Result
AA-LCRSource 64.0%69.7%Kimi K2.6 leads
CritPtSource 1.7%8.0%Kimi K2.6 leads
KnowledgeGLM-4.7 wins
BenchmarkGLM-4.7Kimi K2.6Result
GPQASource 85.7%90.5%Kimi K2.6 leads
MMLU-ProSource 84.3%—Not comparable
HLESource 24.8%34.7%Kimi K2.6 leads
Artificial Analysis Intelligence IndexSource 33.7%44.2%Kimi K2.6 leads
AA-GPQA DiamondSource 85.9%91.1%Kimi K2.6 leads
AA-HLESource 25.1%35.9%Kimi K2.6 leads
AA-Omniscience IndexSource -34.6%6.4%Kimi K2.6 leads
AA-Omniscience AccuracySource 29.3%32.8%Kimi K2.6 leads
AA-Omniscience Hallucination RateSource 90.3%39.3%Kimi K2.6 leads
GPQA-DSource —90.5%Not comparable
AA Openness IndexSource —33.3%Not comparable
MathKimi K2.6 wins
BenchmarkGLM-4.7Kimi K2.6Result
AIME 2025Source 95.7%—Not comparable
FrontierMath v2 (Tiers 1-3)Source 2.439%38.966%Kimi K2.6 leads
FrontierMath v2 (Tier 4)Source 0.000%14.580%Kimi K2.6 leads
AIME26Source —96.4%Not comparable
HMMT Feb 2026Source —92.7%Not comparable
MMAnswerBenchSource —86.0%Not comparable
Multimodal
BenchmarkGLM-4.7Kimi K2.6Result
Design Arena WebsiteSource 12681318Kimi 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-4.7Kimi K2.6Result
AA-IFBenchSource 67.9%76.0%Kimi K2.6 leads
Frequently Asked Questions (5)

Which is better, GLM-4.7 or Kimi K2.6?

Kimi K2.6 is ahead on BenchLM's provisional leaderboard, 74 to 63. The biggest single separator in this matchup is FrontierMath v2 (Tiers 1-3), where the scores are 2.439% and 38.966%.

Which is better for knowledge tasks, GLM-4.7 or Kimi K2.6?

GLM-4.7 has the edge for knowledge tasks in this comparison, averaging 52.1 versus 42.2. Inside this category, AA-Omniscience Hallucination Rate is the benchmark that creates the most daylight between them.

Which is better for coding, GLM-4.7 or Kimi K2.6?

GLM-4.7 has the edge for coding in this comparison, averaging 73.8 versus 72.6. Inside this category, AA Coding Index is the benchmark that creates the most daylight between them.

Which is better for math, GLM-4.7 or Kimi K2.6?

Kimi K2.6 has the edge for math in this comparison, averaging 67.1 versus 1.8. 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-4.7 or Kimi K2.6?

Kimi K2.6 has the edge for agentic tasks in this comparison, averaging 73.5 versus 45.7. Inside this category, BrowseComp 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-4.7
API / mo$0
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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