Compare · ModelsLive · 2 picked · head to head
Qwen3.6 Plus vs GLM 5.1
Side by side · benchmarks, pricing, and signals you can act on.
Winner summary
GLM 5.1 wins on 12/20 benchmarks
GLM 5.1 wins 12 of 20 shared benchmarks. Leads in speed · arena · math.
Category leads
speed·GLM 5.1arena·GLM 5.1knowledge·Qwen3.6 Plusmath·GLM 5.1coding·Qwen3.6 Plusreasoning·Qwen3.6 Pluslanguage·GLM 5.1
Hype vs Reality
Attention vs performance
Qwen3.6 Plus
#55 by perf·no signal
GLM 5.1
#56 by perf·no signal
Best value
Qwen3.6 Plus
1.8x better value than GLM 5.1
Qwen3.6 Plus
52.7 pts/$
$1.14/M
GLM 5.1
29.9 pts/$
$2.00/M
Vendor risk
Who is behind the model
Alibaba (Qwen)
$293.0B·Tier 1
z-ai
private · undisclosed
Head to head
20 benchmarks · 2 models
Qwen3.6 PlusGLM 5.1
Artificial Analysis · Agentic Index
GLM 5.1 leads by +2.3
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?"
Qwen3.6 Plus
27.6
GLM 5.1
29.9
Artificial Analysis · Coding Index
GLM 5.1 leads by +1.3
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.
Qwen3.6 Plus
54.5
GLM 5.1
55.8
Artificial Analysis · Quality Index
GLM 5.1 leads by +0.6
Qwen3.6 Plus
39.6
GLM 5.1
40.2
Chatbot Arena Elo · Coding
GLM 5.1 leads by +67.3
Qwen3.6 Plus
1461.9
GLM 5.1
1529.2
Chatbot Arena Elo · Overall
GLM 5.1 leads by +31.0
Qwen3.6 Plus
1444.2
GLM 5.1
1475.3
Chess Puzzles
GLM 5.1 leads by +1.8
Chess Puzzles · tests strategic and tactical reasoning by having models solve chess puzzle positions, evaluating lookahead and pattern recognition abilities.
Qwen3.6 Plus
16.2
GLM 5.1
18.0
FrontierMath-2025-02-28-Private
GLM 5.1 leads by +7.2
FrontierMath (Feb 2025) · original research-level math problems created by mathematicians, testing capabilities at the boundary of current AI mathematical reasoning.
Qwen3.6 Plus
26.2
GLM 5.1
33.5
FrontierMath-Tier-4-2025-07-01-Private
GLM 5.1 leads by +4.2
FrontierMath Tier 4 (Jul 2025) · the most challenging tier of frontier mathematics, containing problems that push the absolute limits of AI mathematical reasoning.
Qwen3.6 Plus
8.3
GLM 5.1
12.5
GPQA diamond
Qwen3.6 Plus leads by +2.5
Graduate-Level Google-Proof QA (Diamond set) · expert-crafted questions in physics, biology, and chemistry that are difficult even for domain PhDs.
Qwen3.6 Plus
83.2
GLM 5.1
80.6
LiveBench · Agentic Coding
Qwen3.6 Plus
55.0
GLM 5.1
55.0
LiveBench · Coding
Qwen3.6 Plus leads by +2.8
Qwen3.6 Plus
78.2
GLM 5.1
75.4
LiveBench · Data Analysis
Qwen3.6 Plus leads by +6.7
Qwen3.6 Plus
69.9
GLM 5.1
63.2
LiveBench · If
GLM 5.1 leads by +10.1
Qwen3.6 Plus
58.3
GLM 5.1
68.5
LiveBench · Language
Qwen3.6 Plus leads by +3.2
Qwen3.6 Plus
75.0
GLM 5.1
71.8
LiveBench · Mathematics
GLM 5.1 leads by +1.2
Qwen3.6 Plus
83.7
GLM 5.1
84.9
LiveBench · Overall
Qwen3.6 Plus leads by +0.7
Qwen3.6 Plus
70.8
GLM 5.1
70.2
LiveBench · Reasoning
Qwen3.6 Plus leads by +3.3
Qwen3.6 Plus
75.8
GLM 5.1
72.5
OTIS Mock AIME 2024-2025
GLM 5.1 leads by +1.7
OTIS Mock AIME 2024-2025 · simulated American Invitational Mathematics Examination problems testing advanced problem-solving skills.
Qwen3.6 Plus
90.5
GLM 5.1
92.2
SimpleQA Verified
Qwen3.6 Plus leads by +11.8
SimpleQA Verified · short factual questions with verified answers, measuring factual accuracy and the tendency to hallucinate or provide incorrect information.
Qwen3.6 Plus
49.1
GLM 5.1
37.3
SWE-Bench verified
GLM 5.1 leads by +16.3
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.
Qwen3.6 Plus
57.9
GLM 5.1
74.2
Full benchmark table
| Benchmark | Qwen3.6 Plus | GLM 5.1 |
|---|---|---|
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?" | 27.6 | 29.9 |
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. | 54.5 | 55.8 |
Artificial Analysis · Quality Index | 39.6 | 40.2 |
Chatbot Arena Elo · Coding | 1461.9 | 1529.2 |
Chatbot Arena Elo · Overall | 1444.2 | 1475.3 |
Chess Puzzles Chess Puzzles · tests strategic and tactical reasoning by having models solve chess puzzle positions, evaluating lookahead and pattern recognition abilities. | 16.2 | 18.0 |
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. | 26.2 | 33.5 |
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. | 8.3 | 12.5 |
GPQA diamond Graduate-Level Google-Proof QA (Diamond set) · expert-crafted questions in physics, biology, and chemistry that are difficult even for domain PhDs. | 83.2 | 80.6 |
LiveBench · Agentic Coding | 55.0 | 55.0 |
LiveBench · Coding | 78.2 | 75.4 |
LiveBench · Data Analysis | 69.9 | 63.2 |
LiveBench · If | 58.3 | 68.5 |
LiveBench · Language | 75.0 | 71.8 |
LiveBench · Mathematics | 83.7 | 84.9 |
LiveBench · Overall | 70.8 | 70.2 |
LiveBench · Reasoning | 75.8 | 72.5 |
OTIS Mock AIME 2024-2025 OTIS Mock AIME 2024-2025 · simulated American Invitational Mathematics Examination problems testing advanced problem-solving skills. | 90.5 | 92.2 |
SimpleQA Verified SimpleQA Verified · short factual questions with verified answers, measuring factual accuracy and the tendency to hallucinate or provide incorrect information. | 49.1 | 37.3 |
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. | 57.9 | 74.2 |
Pricing · per 1M tokens · projected $/mo at 10M tokens
| Model | Input | Output | Context | Projected $/mo |
|---|---|---|---|---|
| $0.33 | $1.95 | 1.0M tokens (~500 books) | $7.31 | |
| $0.97 | $3.04 | 203K tokens (~101 books) | $14.83 |