vs
1:9
overall capability wins (2 ties across 12 scored). GLM-5.2 leads overall.
- Reasoning1:5
- Coding0:3
- Arena0:1
- Pricing3:0
- Speed0:1
Verdict
GLM-5.2 leads coding (widest gap: +155.9 on WebDev Elo); GLM-5.2 ranks higher on LMArena (+15 Elo); GLM-5 is ~3.6x cheaper on a blended token basis than GLM-5.2.
- GLM-5.2 leads coding (widest gap: +155.9 on WebDev Elo)
- GLM-5.2 ranks higher on LMArena (+15 Elo)
- GLM-5 is ~3.6x cheaper on a blended token basis than GLM-5.2
Cost for 1M in + 250K out
Illustrative chat workload at primary-provider list prices.
- GLM-5
- $0.70
- GLM-5.2
- $2.50
GLM-5 is about 3.6x cheaper on this workload.
Price per 1M tokens
Full comparison
Organization
Zhipu AI
Zhipu AI
Family
GLM
GLM
License
MIT
MIT
Open weights
Yes
Yes
Release
Feb 1, 2026
Jun 1, 2026
Knowledge cutoff
-
-
API / provider
Zhipu / Z.ai
Zhipu / Z.ai
Modalities
text → text
text, image → text
Specs
Context window
200K
200K
Max output
33K
33K
Parameters
—
—
Pricing
Input $/1M
$0.40
$1.40
Output $/1M
$1.20
$4.40
Blended $/1M (3∶1)
$0.60
$2.15
Speed
tok/s
80
168
TTFT (s)
0.35
0.35
Reasoning
MMLU-Pro
—
—
GPQA Diamond
82.0
89.5
Humanity's Last Exam
29.3
41.1
AIME 2025
—
—
MATH-500
—
—
Humanity's Last Exam (with tools)
—
—
AA-Omniscience Accuracy
26.3
24.3
AA-LCR v1.1
75.7
78.3
CritPt
2.0
20.9
MMMU-Pro
—
—
IFBench
72.3
73.3
Chartography
—
—
Chartography (With Tools)
—
—
Coding
SWE-bench Verified
72.8
—
SWE-bench Pro
—
—
SWE-bench Multilingual
69.7
—
LiveCodeBench
—
—
Terminal-Bench 2.1
—
77.9
Aider Polyglot
—
—
Terminal-Bench 3
—
4.6
BigCodeBench
—
—
SciCode
46.2
51.2
CursorBench
—
55.0
SWE-Rebench
53.3
57.0
NL2Repo-Bench
—
—
DeepSWE
—
44.0
WebDev Arena
1,436
1,592
Terminal-Bench 4.0
—
—
CursorBench 4.0
—
—
FrontierCode v1.1 (Main)
—
—
Arena
LMArena Elo
1,458
1,472
| Metric | GLM-5 | GLM-5.2 | Delta |
|---|---|---|---|
| Identity | |||
| Organization | Zhipu AI | Zhipu AI | — |
| Family | GLM | GLM | — |
| License | MIT | MIT | — |
| Open weights | Yes | Yes | — |
| Release | Feb 1, 2026 | Jun 1, 2026 | — |
| Knowledge cutoff | - | - | — |
| API / provider | Zhipu / Z.ai | Zhipu / Z.ai | — |
| Modalities | text → text | text, image → text | — |
| Specs | |||
| Context window | 200K | 200K | tie |
| Max output | 33K | 33K | tie |
| Parameters | — | — | — |
| Pricing | |||
| Input $/1M | $0.40 | $1.40 | A +$1.00 |
| Output $/1M | $1.20 | $4.40 | A +$3.20 |
| Blended $/1M (3∶1) | $0.60 | $2.15 | A +$1.55 |
| Speed | |||
| tok/s | 80 | 168 | B +88 |
| TTFT (s) | 0.35 | 0.35 | tie |
| Reasoning | |||
| MMLU-Pro | — | — | — |
| GPQA Diamond | 82.0 | 89.5 | B +7.5 pts |
| Humanity's Last Exam | 29.3 | 41.1 | B +11.8 pts |
| AIME 2025 | — | — | — |
| MATH-500 | — | — | — |
| Humanity's Last Exam (with tools) | — | — | — |
| AA-Omniscience Accuracy | 26.3 | 24.3 | A +2.0 pts |
| AA-LCR v1.1 | 75.7 | 78.3 | B +2.6 pts |
| CritPt | 2.0 | 20.9 | B +18.9 pts |
| MMMU-Pro | — | — | — |
| IFBench | 72.3 | 73.3 | B +1.0 pts |
| Chartography | — | — | — |
| Chartography (With Tools) | — | — | — |
| Coding | |||
| SWE-bench Verified | 72.8 | — | — |
| SWE-bench Pro | — | — | — |
| SWE-bench Multilingual | 69.7 | — | — |
| LiveCodeBench | — | — | — |
| Terminal-Bench 2.1 | — | 77.9 | — |
| Aider Polyglot | — | — | — |
| Terminal-Bench 3 | — | 4.6 | — |
| BigCodeBench | — | — | — |
| SciCode | 46.2 | 51.2 | B +5.0 pts |
| CursorBench | — | 55.0 | — |
| SWE-Rebench | 53.3 | 57.0 | B +3.7 pts |
| NL2Repo-Bench | — | — | — |
| DeepSWE | — | 44.0 | — |
| WebDev Arena | 1,436 | 1,592 | B +156 Elo |
| Terminal-Bench 4.0 | — | — | — |
| CursorBench 4.0 | — | — | — |
| FrontierCode v1.1 (Main) | — | — | — |
| Arena | |||
| LMArena Elo | 1,458 | 1,472 | B +15 Elo |
GLM-5
- Context
- 200K / 33K out
- Parameters
- —
- Price
- $0.40 / $1.20
- Speed
- 80 tok/s · 0.35s TTFT
- Modalities
- text → text
- License
- MIT
GLM-5 open release - solid all-rounder before the 5.2 bump.
GLM-5.2
- Context
- 200K / 33K out
- Parameters
- —
- Price
- $1.40 / $4.40
- Speed
- 168 tok/s · 0.35s TTFT
- Modalities
- text, image → text
- License
- MIT
Open MIT GLM-5.2 - competitive open Arena Elo and strong Chinese/English bilingual performance.
Benchmark charts
Winner bars are emphasized. Per-benchmark deltas sit above each chart.