Compare · ModelsLive · 2 picked · head to head
GLM 5.1 vs Kimi K2.6
Side by side · benchmarks, pricing, and signals you can act on.
Winner summary
Kimi K2.6 wins on 12/16 benchmarks
Kimi K2.6 wins 12 of 16 shared benchmarks. Leads in speed · arena · knowledge.
Category leads
speed·Kimi K2.6arena·Kimi K2.6knowledge·Kimi K2.6general·Kimi K2.6math·Kimi K2.6coding·Kimi K2.6
Hype vs Reality
Attention vs performance
GLM 5.1
#84 by perf·#3 by attention
Kimi K2.6
#205 by perf·#17 by attention
Best value
GLM 5.1
1.8x better value than Kimi K2.6
GLM 5.1
27.1 pts/$
$2.00/M
Kimi K2.6
15.5 pts/$
$2.48/M
Vendor risk
Who is behind the model
z-ai
private · undisclosed
Moonshot AI
$18.0B·Tier 1
Head to head
16 benchmarks · 2 models
GLM 5.1Kimi K2.6
Artificial Analysis · Agentic Index
Kimi K2.6 leads by +0.4
Artificial Analysis Agentic Index · a composite score measuring how well a model performs in agentic workflows · multi-step tool use, planning, error recovery, and autonomous task completion. Aggregates results from multiple agentic benchmarks including SWE-bench, tool-use tests, and planning evaluations. The canonical single-number metric for "how good is this model as an agent?"
GLM 5.1
29.9
Kimi K2.6
30.3
Artificial Analysis · Coding Index
Kimi K2.6 leads by +0.3
Artificial Analysis Coding Index · a composite score that aggregates performance across multiple coding benchmarks into a single index. Tracks code generation quality, debugging ability, multi-language competence, and real-world software engineering tasks. Used by Artificial Analysis to rank model coding capability in a normalized, comparable format. Useful for developers choosing between models for coding-heavy workloads.
GLM 5.1
55.8
Kimi K2.6
56.0
Artificial Analysis · Quality Index
Kimi K2.6 leads by +2.7
GLM 5.1
40.2
Kimi K2.6
42.8
Chatbot Arena Elo · Coding
Kimi K2.6 leads by +0.2
GLM 5.1
1508.5
Kimi K2.6
1508.7
Chatbot Arena Elo · Overall
GLM 5.1 leads by +3.6
GLM 5.1
1464.6
Kimi K2.6
1461.0
Chess Puzzles
Kimi K2.6 leads by +7.4
Chess Puzzles · tests strategic and tactical reasoning by having models solve chess puzzle positions, evaluating lookahead and pattern recognition abilities.
GLM 5.1
14.8
Kimi K2.6
22.1
Exploitbench
Kimi K2.6 leads by +0.3
GLM 5.1
18.1
Kimi K2.6
18.4
FrontierMath-2025-02-28-Private
Kimi K2.6 leads by +5.5
FrontierMath (Feb 2025) · original research-level math problems created by mathematicians, testing capabilities at the boundary of current AI mathematical reasoning.
GLM 5.1
33.5
Kimi K2.6
39.0
FrontierMath-Tier-4-2025-07-01-Private
GLM 5.1 leads by +6.2
FrontierMath Tier 4 (Jul 2025) · the most challenging tier of frontier mathematics, containing problems that push the absolute limits of AI mathematical reasoning.
GLM 5.1
20.8
Kimi K2.6
14.6
FrontierMath-Tiers-1-3-v2-Private
Kimi K2.6 leads by +20.3
GLM 5.1
36.8
Kimi K2.6
57.2
GPQA diamond
Kimi K2.6 leads by +1.2
Graduate-Level Google-Proof QA (Diamond set) · expert-crafted questions in physics, biology, and chemistry that are difficult even for domain PhDs.
GLM 5.1
86.5
Kimi K2.6
87.7
OTIS Mock AIME 2024-2025
Kimi K2.6 leads by +2.8
OTIS Mock AIME 2024-2025 · simulated American Invitational Mathematics Examination problems testing advanced problem-solving skills.
GLM 5.1
93.3
Kimi K2.6
96.1
Proofbench
GLM 5.1 leads by +6.2
GLM 5.1
22.2
Kimi K2.6
16.0
SimpleQA Verified
Kimi K2.6 leads by +0.9
SimpleQA Verified · short factual questions with verified answers, measuring factual accuracy and the tendency to hallucinate or provide incorrect information.
GLM 5.1
34.0
Kimi K2.6
34.9
SWE-Bench verified
Kimi K2.6 leads by +2.5
SWE-bench Verified · 500 human-validated tasks from 12 real Python repositories (Django, Flask, scikit-learn, sympy, and others). Each task requires the model to produce a git patch that resolves a real GitHub issue and passes the test suite. The verified subset eliminates ambiguous tasks from the original SWE-bench. Claude Mythos Preview leads at 93.9%, crossing 90% for the first time in 2026. Opus 4.6 scores 80.8%. The benchmark remains the most-cited evaluation for code-generation capability.
GLM 5.1
74.2
Kimi K2.6
76.7
WeirdML
GLM 5.1 leads by +1.2
WeirdML · tests models on unusual and adversarial machine learning tasks that require creative problem-solving beyond standard patterns.
GLM 5.1
57.1
Kimi K2.6
55.9
Full benchmark table
| Benchmark | GLM 5.1 | Kimi K2.6 |
|---|---|---|
Artificial Analysis · Agentic Index Artificial Analysis Agentic Index · a composite score measuring how well a model performs in agentic workflows · multi-step tool use, planning, error recovery, and autonomous task completion. Aggregates results from multiple agentic benchmarks including SWE-bench, tool-use tests, and planning evaluations. The canonical single-number metric for "how good is this model as an agent?" | 29.9 | 30.3 |
Artificial Analysis · Coding Index Artificial Analysis Coding Index · a composite score that aggregates performance across multiple coding benchmarks into a single index. Tracks code generation quality, debugging ability, multi-language competence, and real-world software engineering tasks. Used by Artificial Analysis to rank model coding capability in a normalized, comparable format. Useful for developers choosing between models for coding-heavy workloads. | 55.8 | 56.0 |
Artificial Analysis · Quality Index | 40.2 | 42.8 |
Chatbot Arena Elo · Coding | 1508.5 | 1508.7 |
Chatbot Arena Elo · Overall | 1464.6 | 1461.0 |
Chess Puzzles Chess Puzzles · tests strategic and tactical reasoning by having models solve chess puzzle positions, evaluating lookahead and pattern recognition abilities. | 14.8 | 22.1 |
Exploitbench | 18.1 | 18.4 |
FrontierMath-2025-02-28-Private FrontierMath (Feb 2025) · original research-level math problems created by mathematicians, testing capabilities at the boundary of current AI mathematical reasoning. | 33.5 | 39.0 |
FrontierMath-Tier-4-2025-07-01-Private FrontierMath Tier 4 (Jul 2025) · the most challenging tier of frontier mathematics, containing problems that push the absolute limits of AI mathematical reasoning. | 20.8 | 14.6 |
FrontierMath-Tiers-1-3-v2-Private | 36.8 | 57.2 |
GPQA diamond Graduate-Level Google-Proof QA (Diamond set) · expert-crafted questions in physics, biology, and chemistry that are difficult even for domain PhDs. | 86.5 | 87.7 |
OTIS Mock AIME 2024-2025 OTIS Mock AIME 2024-2025 · simulated American Invitational Mathematics Examination problems testing advanced problem-solving skills. | 93.3 | 96.1 |
Proofbench | 22.2 | 16.0 |
SimpleQA Verified SimpleQA Verified · short factual questions with verified answers, measuring factual accuracy and the tendency to hallucinate or provide incorrect information. | 34.0 | 34.9 |
SWE-Bench verified SWE-bench Verified · 500 human-validated tasks from 12 real Python repositories (Django, Flask, scikit-learn, sympy, and others). Each task requires the model to produce a git patch that resolves a real GitHub issue and passes the test suite. The verified subset eliminates ambiguous tasks from the original SWE-bench. Claude Mythos Preview leads at 93.9%, crossing 90% for the first time in 2026. Opus 4.6 scores 80.8%. The benchmark remains the most-cited evaluation for code-generation capability. | 74.2 | 76.7 |
WeirdML WeirdML · tests models on unusual and adversarial machine learning tasks that require creative problem-solving beyond standard patterns. | 57.1 | 55.9 |