Compare · ModelsLive · 3 picked · head to head
Qwen3.7 Max vs GLM 5.1 vs Gemma 4 31B
Side by side · benchmarks, pricing, and signals you can act on.
Winner summary
Qwen3.7 Max wins on 14/16 benchmarks
Qwen3.7 Max wins 14 of 16 shared benchmarks. Leads in reasoning · language · math.
Category leads
coding·GLM 5.1reasoning·Qwen3.7 Maxlanguage·Qwen3.7 Maxmath·Qwen3.7 Maxknowledge·Qwen3.7 Maxspeed·Qwen3.7 Max
Hype vs Reality
Attention vs performance
Qwen3.7 Max
#28 by perf·no signal
GLM 5.1
#56 by perf·no signal
Gemma 4 31B
#45 by perf·no signal
Best value
Gemma 4 31B
8.8x better value than GLM 5.1
Qwen3.7 Max
27.2 pts/$
$2.50/M
GLM 5.1
29.9 pts/$
$2.00/M
Gemma 4 31B
262.1 pts/$
$0.23/M
Vendor risk
Who is behind the model
Alibaba (Qwen)
$293.0B·Tier 1
z-ai
private · undisclosed
Google DeepMind
$4.00T·Tier 1
Head to head
16 benchmarks · 3 models
Qwen3.7 MaxGLM 5.1Gemma 4 31B
LiveBench · Agentic Coding
GLM 5.1 leads by +3.3
Qwen3.7 Max
51.7
GLM 5.1
55.0
Gemma 4 31B
40.0
LiveBench · Coding
GLM 5.1 leads by +1.2
Qwen3.7 Max
74.2
GLM 5.1
75.4
Gemma 4 31B
60.3
LiveBench · Data Analysis
Qwen3.7 Max leads by +8.6
Qwen3.7 Max
71.8
GLM 5.1
63.2
Gemma 4 31B
58.8
LiveBench · If
Qwen3.7 Max leads by +5.6
Qwen3.7 Max
74.0
GLM 5.1
68.5
Gemma 4 31B
67.6
LiveBench · Language
Qwen3.7 Max leads by +8.0
Qwen3.7 Max
79.7
GLM 5.1
71.8
Gemma 4 31B
71.3
LiveBench · Mathematics
Qwen3.7 Max leads by +0.4
Qwen3.7 Max
85.3
GLM 5.1
84.9
Gemma 4 31B
73.9
LiveBench · Overall
Qwen3.7 Max leads by +4.1
Qwen3.7 Max
74.3
GLM 5.1
70.2
Gemma 4 31B
61.6
LiveBench · Reasoning
Qwen3.7 Max leads by +10.8
Qwen3.7 Max
83.3
GLM 5.1
72.5
Gemma 4 31B
59.4
Artificial Analysis · Agentic Index
Qwen3.7 Max leads by +0.7
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?"
Qwen3.7 Max
30.6
GLM 5.1
29.9
Artificial Analysis · Coding Index
Qwen3.7 Max leads by +10.2
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.
Qwen3.7 Max
66.0
GLM 5.1
55.8
Artificial Analysis · Quality Index
Qwen3.7 Max leads by +5.8
Qwen3.7 Max
46.0
GLM 5.1
40.2
Chess Puzzles
Qwen3.7 Max leads by +4.0
Chess Puzzles · tests strategic and tactical reasoning by having models solve chess puzzle positions, evaluating lookahead and pattern recognition abilities.
Qwen3.7 Max
22.0
GLM 5.1
18.0
GPQA diamond
Qwen3.7 Max leads by +8.2
Graduate-Level Google-Proof QA (Diamond set) · expert-crafted questions in physics, biology, and chemistry that are difficult even for domain PhDs.
Qwen3.7 Max
88.8
GLM 5.1
80.6
OTIS Mock AIME 2024-2025
Qwen3.7 Max leads by +2.8
OTIS Mock AIME 2024-2025 · simulated American Invitational Mathematics Examination problems testing advanced problem-solving skills.
Qwen3.7 Max
95.0
GLM 5.1
92.2
SimpleBench
Qwen3.7 Max leads by +14.0
SimpleBench · tests fundamental reasoning capabilities with straightforward problems designed to expose gaps in basic logical and spatial thinking.
Qwen3.7 Max
64.5
GLM 5.1
50.4
SimpleQA Verified
Qwen3.7 Max leads by +21.2
SimpleQA Verified · short factual questions with verified answers, measuring factual accuracy and the tendency to hallucinate or provide incorrect information.
Qwen3.7 Max
58.5
GLM 5.1
37.3
Full benchmark table
| Benchmark | Qwen3.7 Max | GLM 5.1 | Gemma 4 31B |
|---|---|---|---|
LiveBench · Agentic Coding | 51.7 | 55.0 | 40.0 |
LiveBench · Coding | 74.2 | 75.4 | 60.3 |
LiveBench · Data Analysis | 71.8 | 63.2 | 58.8 |
LiveBench · If | 74.0 | 68.5 | 67.6 |
LiveBench · Language | 79.7 | 71.8 | 71.3 |
LiveBench · Mathematics | 85.3 | 84.9 | 73.9 |
LiveBench · Overall | 74.3 | 70.2 | 61.6 |
LiveBench · Reasoning | 83.3 | 72.5 | 59.4 |
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?" | 30.6 | 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. | 66.0 | 55.8 | — |
Artificial Analysis · Quality Index | 46.0 | 40.2 | — |
Chess Puzzles Chess Puzzles · tests strategic and tactical reasoning by having models solve chess puzzle positions, evaluating lookahead and pattern recognition abilities. | 22.0 | 18.0 | — |
GPQA diamond Graduate-Level Google-Proof QA (Diamond set) · expert-crafted questions in physics, biology, and chemistry that are difficult even for domain PhDs. | 88.8 | 80.6 | — |
OTIS Mock AIME 2024-2025 OTIS Mock AIME 2024-2025 · simulated American Invitational Mathematics Examination problems testing advanced problem-solving skills. | 95.0 | 92.2 | — |
SimpleBench SimpleBench · tests fundamental reasoning capabilities with straightforward problems designed to expose gaps in basic logical and spatial thinking. | 64.5 | 50.4 | — |
SimpleQA Verified SimpleQA Verified · short factual questions with verified answers, measuring factual accuracy and the tendency to hallucinate or provide incorrect information. | 58.5 | 37.3 | — |
Pricing · per 1M tokens · projected $/mo at 10M tokens
| Model | Input | Output | Context | Projected $/mo |
|---|---|---|---|---|
| $1.25 | $3.75 | 1.0M tokens (~500 books) | $18.75 | |
| $0.97 | $3.04 | 203K tokens (~101 books) | $14.83 | |
| $0.12 | $0.35 | 262K tokens (~131 books) | $1.77 |