Model comparison
GLM-5.2 vs GPT-4o mini
GLM-5.2 is the stronger model overall, scoring 51.1 to 25.5 on the Noometry Index. GPT-4o mini costs 8.2× less per token, which makes it the better buy when GLM-5.2's lead doesn't matter for your workload.
Last verified . 31 shared benchmarks.
Summary
- They share 31 benchmarks with published results for both. GLM-5.2 scores higher in 9 categories and GPT-4o mini in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-5.2 leads 55.7 to 10.4.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 86.4% for GLM-5.2 and 6.9% for GPT-4o mini.
- GPT-4o mini is cheaper at $0.15 / $0.60 per million input/output tokens, against $1.40 / $4.40 for GLM-5.2.
- GLM-5.2 accepts more context: 1M tokens versus 128K.
- GLM-5.2 has downloadable open weights; the other is API-only.
Side by side
| GLM-5.2 | GPT-4o mini | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 51.1 | 25.5 |
| Released | 2026-06-13 | 2024-07-18 |
| Weights | Open | Proprietary |
| Context window | 1M | 128K |
| Max output | 131K | 16K |
| Input $ / M tokens | $1.40 | $0.15 |
| Output $ / M tokens | $4.40 | $0.60 |
| Results tracked | 51 | 60 |
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Category by category
Coding GLM-5.2 leads
GLM-5.2: 51.3 (#41), GPT-4o mini: 22.0 (#335)
| Benchmark | GLM-5.2 | GPT-4o mini |
|---|---|---|
| WeirdML | 70.1% | 11.8% |
| LMArena Coding | 1485 | 1290 |
| SWE-bench Verified | 78.7% | — |
| DeepSWE | 43.8% | — |
| FrontierCode | 24.5% | — |
| Aider Polyglot | — | 3.6% |
| LMArena WebDev | 1603 | — |
| SciCode | 50.5% | — |
| BigCodeBench Instruct | — | 46.1% |
| LiveBench Coding | — | 43.1% |
| BigCodeBench Complete | — | 57.4% |
| ALE-Bench | 1,047 | — |
| HumanEval+ | — | 83.5% |
| MBPP+ | — | 72.2% |
Agentic & Tool Use GLM-5.2 leads
GLM-5.2: 32.4 (#63), GPT-4o mini: 27.5 (#101)
| Benchmark | GLM-5.2 | GPT-4o mini |
|---|---|---|
| APEX-Agents | 45.2% | — |
| τ²-bench Banking | 37.1% | — |
| PostTrainBench | 31.7% | — |
| BALROG | — | 17.4% |
| GBAEval | 0% | — |
| Vending-Bench 2 | 8,314 | — |
Reasoning GLM-5.2 leads
GLM-5.2: 42.3 (#52), GPT-4o mini: 8.7 (#347)
| Benchmark | GLM-5.2 | GPT-4o mini |
|---|---|---|
| ARC-AGI-2 | 22.8% | 0% |
| SimpleBench | 58.8% | 10.7% |
| Kagi LLM Benchmark | 62.6% | 28.8% |
| Chess Puzzles | 21% | 0% |
| LMArena Hard Prompts | 1480 | 1267 |
| Mystery Game Puzzles | 19% | 12% |
| DTBench | 93.6% | 54.4% |
| LMCA | 45.8% | 10.4% |
| Epoch Capabilities Index | 151.78 | 126.56 |
| NYT Connections (extended) | 74.3% | — |
| ARC-AGI-1 | 77% | — |
| CritPt | 20.9% | — |
| EBR-Bench | 9.5% | — |
| LiveBench Reasoning | — | 32.8% |
| LiveBench Data Analysis | — | 50% |
| Surface Evolver Bench | 55.6% | — |
| LiveBench | — | 41.3% |
| PIQA | — | 88.7% |
Math GLM-5.2 leads
GLM-5.2: 55.7 (#43), GPT-4o mini: 10.4 (#314)
| Benchmark | GLM-5.2 | GPT-4o mini |
|---|---|---|
| FrontierMath (Tiers 1-3) | 59.2% | 0.7% |
| OTIS Mock AIME 2024-2025 | 86.4% | 6.9% |
| LMArena Math | 1482 | 1267 |
| FrontierMath Tier 4 | 29.3% | — |
| MathArena Final-Answer Competitions | 67.6% | — |
| ProofBench | 35% | — |
| Omni-MATH | — | 28% |
| LiveBench Math | — | 36.3% |
| MATH Level 5 | — | 52.6% |
| GSM8K | — | 91.3% |
Knowledge GLM-5.2 leads
GLM-5.2: 57.1 (#40), GPT-4o mini: 17.7 (#284)
| Benchmark | GLM-5.2 | GPT-4o mini |
|---|---|---|
| GPQA Diamond | 91.9% | 37.7% |
| SimpleQA Verified | 34.2% | 8.3% |
| LMArena Expert | 1486 | 1235 |
| MMLU-Pro | — | 60.3% |
| Confabulations | — | 37.2% |
| GPQA (HELM) | — | 36.8% |
| BoolQ | — | 88.7% |
| MMLU | — | 81.8% |
Multimodal Not comparable
GLM-5.2: —, GPT-4o mini: 25.9 (#122)
| Benchmark | GLM-5.2 | GPT-4o mini |
|---|---|---|
| LMArena Vision | — | 1066 |
| Video-MME | — | 64.8% |
| GeoBench | — | 64% |
| VPCT | — | 34% |
Multilingual GLM-5.2 leads
GLM-5.2: 55.8 (#26), GPT-4o mini: 42.0 (#199)
| Benchmark | GLM-5.2 | GPT-4o mini |
|---|---|---|
| LMArena Non-English | 1459 | 1266 |
| LMArena Chinese | 1519 | 1265 |
| LMArena French | 1479 | 1297 |
| LMArena German | 1468 | 1272 |
| LMArena Japanese | 1451 | 1216 |
| LMArena Korean | 1445 | 1195 |
| LMArena Russian | 1466 | 1275 |
| LMArena Spanish | 1477 | 1276 |
Instruction Following GLM-5.2 leads
GLM-5.2: 76.9 (#34), GPT-4o mini: 61.9 (#239)
| Benchmark | GLM-5.2 | GPT-4o mini |
|---|---|---|
| LMArena Instruction Following | 1465 | 1258 |
| LiveBench Instruction Following | — | 56.8% |
| IFEval | — | 78.2% |
Long Context GLM-5.2 leads
GLM-5.2: 45.3 (#43), GPT-4o mini: 39.1 (#186)
| Benchmark | GLM-5.2 | GPT-4o mini |
|---|---|---|
| LMArena Longer Query | 1479 | 1289 |
Writing & Preference GLM-5.2 leads
GLM-5.2: 70.4 (#21), GPT-4o mini: 39.5 (#248)
| Benchmark | GLM-5.2 | GPT-4o mini |
|---|---|---|
| LMArena Text | 1470 | 1286 |
| LMArena Creative Writing | 1462 | 1268 |
| EQ-Bench Creative Writing | 1757 | 873 |
| LMArena Multi-Turn | 1469 | 1285 |
| Short-Story Creative Writing | — | 67.2% |
| WildBench | — | 79.1% |
| EQ-Bench 4 | 1222 | — |
| LiveBench Language | — | 28.6% |
Frequently asked questions
Is GLM-5.2 better than GPT-4o mini?
GLM-5.2 is the stronger model overall, scoring 51.1 to 25.5 on the Noometry Index. GPT-4o mini costs 8.2× less per token, which makes it the better buy when GLM-5.2's lead doesn't matter for your workload.
Which is cheaper, GLM-5.2 or GPT-4o mini?
GPT-4o mini is cheaper. It lists at $0.15 per million input tokens and $0.60 per million output tokens; GLM-5.2 lists at $1.40 and $4.40.
Is GLM-5.2 or GPT-4o mini better for coding?
GLM-5.2 scores higher on coding benchmarks: 51.3 versus 22.0 in the Noometry coding category.
Which has the bigger context window?
GLM-5.2 does, with 1M tokens against 128K.
How many benchmarks do GLM-5.2 and GPT-4o mini share?
31 benchmarks have published results for both models. GLM-5.2 has 51 scored results on Noometry and GPT-4o mini has 60.