Compare · ModelsLive · 2 picked · head to head
GLM 5.2 vs DeepSeek V3.2 Speciale
Side by side · benchmarks, pricing, and signals you can act on.
Winner summary
GLM 5.2 wins on 3/3 benchmarks
GLM 5.2 wins 3 of 3 shared benchmarks. Leads in speed.
Category leads
speed·GLM 5.2
Hype vs Reality
Attention vs performance
GLM 5.2
#11 by perf·no signal
DeepSeek V3.2 Speciale
#6 by perf·#5 by attention
Best value
DeepSeek V3.2 Speciale
3.2x better value than GLM 5.2
GLM 5.2
30.5 pts/$
$2.50/M
DeepSeek V3.2 Speciale
97.8 pts/$
$0.80/M
Vendor risk
Mixed exposure
One or more vendors flagged
z-ai
private · undisclosed
DeepSeek
$3.4B·Tier 1
Head to head
3 benchmarks · 2 models
GLM 5.2DeepSeek V3.2 Speciale
Artificial Analysis · Agentic Index
GLM 5.2 leads by +43.1
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 5.2
43.1
DeepSeek V3.2 Speciale
0.0
Artificial Analysis · Coding Index
GLM 5.2 leads by +30.9
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 5.2
68.8
DeepSeek V3.2 Speciale
37.9
Artificial Analysis · Quality Index
GLM 5.2 leads by +21.7
GLM 5.2
51.1
DeepSeek V3.2 Speciale
29.4
Full benchmark table
| Benchmark | GLM 5.2 | DeepSeek V3.2 Speciale |
|---|---|---|
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?" | 43.1 | 0.0 |
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. | 68.8 | 37.9 |
Artificial Analysis · Quality Index | 51.1 | 29.4 |
Pricing · per 1M tokens · projected $/mo at 10M tokens
| Model | Input | Output | Context | Projected $/mo |
|---|---|---|---|---|
| $1.00 | $4.00 | 1.0M tokens (~524 books) | $17.50 | |
| $0.40 | $1.20 | 164K tokens (~82 books) | $6.00 |