Model comparison
GLM-4.6 vs GPT-4o mini
GLM-4.6 is the stronger model overall, scoring 40.9 to 25.4 on the Noometry Index. GPT-4o mini costs 3.8× less per token, which makes it the better buy when GLM-4.6's lead doesn't matter for your workload.
Last verified . 19 shared benchmarks.
Summary
- They share 19 benchmarks with published results for both. GLM-4.6 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-4.6 leads 36.4 to 9.9.
- The biggest single-benchmark swing is Kagi LLM Benchmark: 47.4% for GLM-4.6 and 28.8% for GPT-4o mini.
- GPT-4o mini is cheaper at $0.15 / $0.60 per million input/output tokens, against $0.60 / $2.20 for GLM-4.6.
- GLM-4.6 accepts more context: 205K tokens versus 128K.
- GLM-4.6 has downloadable open weights; the other is API-only.
Side by side
| GLM-4.6 | GPT-4o mini | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 40.9 | 25.4 |
| Released | 2025-09-30 | 2024-07-18 |
| Weights | Open | Proprietary |
| Context window | 205K | 128K |
| Max output | 131K | 16K |
| Input $ / M tokens | $0.60 | $0.15 |
| Output $ / M tokens | $2.20 | $0.60 |
| Results tracked | 29 | 60 |
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Category by category
Coding GLM-4.6 leads
GLM-4.6: 39.9 (#137), GPT-4o mini: 21.6 (#330)
| Benchmark | GLM-4.6 | GPT-4o mini |
|---|---|---|
| LMArena Coding | 1449 | 1290 |
| SWE-bench Verified (bash only) | 55.4% | — |
| Aider Polyglot | — | 3.6% |
| LMArena WebDev | 1339 | — |
| SciCode | 38.4% | — |
| WeirdML | — | 11.8% |
| BigCodeBench Instruct | — | 46.1% |
| LiveBench Coding | — | 43.1% |
| BigCodeBench Complete | — | 57.4% |
| ALE-Bench | 340.82 | — |
| HumanEval+ | — | 83.5% |
| MBPP+ | — | 72.2% |
Agentic & Tool Use GLM-4.6 leads
GLM-4.6: 32.1 (#60), GPT-4o mini: 27.2 (#95)
| Benchmark | GLM-4.6 | GPT-4o mini |
|---|---|---|
| Terminal-Bench | 24.5% | — |
| Berkeley Function Calling Leaderboard | 72.4% | — |
| BALROG | — | 17.4% |
Reasoning GLM-4.6 leads
GLM-4.6: 23.1 (#168), GPT-4o mini: 8.7 (#343)
| Benchmark | GLM-4.6 | GPT-4o mini |
|---|---|---|
| Kagi LLM Benchmark | 47.4% | 28.8% |
| LMArena Hard Prompts | 1440 | 1267 |
| ARC-AGI-2 | — | 0% |
| SimpleBench | — | 10.7% |
| CritPt | 1.1% | — |
| Chess Puzzles | — | 0% |
| LiveBench Reasoning | — | 32.8% |
| Mystery Game Puzzles | — | 12% |
| DTBench | — | 54.4% |
| LiveBench Data Analysis | — | 50% |
| LMCA | — | 10.4% |
| Epoch Capabilities Index | — | 126.56 |
| LiveBench | — | 41.3% |
| PIQA | — | 88.7% |
Math GLM-4.6 leads
GLM-4.6: 36.4 (#115), GPT-4o mini: 9.9 (#307)
| Benchmark | GLM-4.6 | GPT-4o mini |
|---|---|---|
| LMArena Math | 1431 | 1267 |
| FrontierMath (Tiers 1-3) | — | 0.7% |
| OTIS Mock AIME 2024-2025 | — | 6.9% |
| Omni-MATH | — | 28% |
| LiveBench Math | — | 36.3% |
| MATH Level 5 | — | 52.6% |
| FrontierMath (Feb 2025 set) | 3.8% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
| GSM8K | — | 91.3% |
Knowledge GLM-4.6 leads
GLM-4.6: 39.6 (#118), GPT-4o mini: 17.7 (#279)
| Benchmark | GLM-4.6 | GPT-4o mini |
|---|---|---|
| LMArena Expert | 1432 | 1235 |
| GPQA Diamond | — | 37.7% |
| SimpleQA Verified | — | 8.3% |
| MMLU-Pro | — | 60.3% |
| Confabulations | — | 37.2% |
| Vectara Hallucination Rate | 9.5% | — |
| GPQA (HELM) | — | 36.8% |
| BoolQ | — | 88.7% |
| MMLU | — | 81.8% |
Multimodal Not comparable
GLM-4.6: —, GPT-4o mini: 25.3 (#117)
| Benchmark | GLM-4.6 | GPT-4o mini |
|---|---|---|
| LMArena Vision | — | 1066 |
| Video-MME | — | 64.8% |
| GeoBench | — | 64% |
| VPCT | — | 34% |
Multilingual GLM-4.6 leads
GLM-4.6: 53.6 (#62), GPT-4o mini: 42.2 (#194)
| Benchmark | GLM-4.6 | GPT-4o mini |
|---|---|---|
| LMArena Non-English | 1426 | 1266 |
| LMArena Chinese | 1500 | 1264 |
| LMArena French | 1459 | 1296 |
| LMArena German | 1447 | 1272 |
| LMArena Japanese | 1394 | 1216 |
| LMArena Korean | 1401 | 1195 |
| LMArena Russian | 1418 | 1275 |
| LMArena Spanish | 1436 | 1276 |
Instruction Following GLM-4.6 leads
GLM-4.6: 74.5 (#93), GPT-4o mini: 62.1 (#234)
| Benchmark | GLM-4.6 | GPT-4o mini |
|---|---|---|
| LMArena Instruction Following | 1410 | 1259 |
| LiveBench Instruction Following | — | 56.8% |
| IFEval | — | 78.2% |
Long Context GLM-4.6 leads
GLM-4.6: 43.4 (#91), GPT-4o mini: 39.1 (#181)
| Benchmark | GLM-4.6 | GPT-4o mini |
|---|---|---|
| LMArena Longer Query | 1423 | 1289 |
Writing & Preference GLM-4.6 leads
GLM-4.6: 61.3 (#86), GPT-4o mini: 39.7 (#243)
| Benchmark | GLM-4.6 | GPT-4o mini |
|---|---|---|
| LMArena Text | 1440 | 1286 |
| LMArena Creative Writing | 1411 | 1268 |
| EQ-Bench Creative Writing | 1411 | 873 |
| LMArena Multi-Turn | 1427 | 1285 |
| Short-Story Creative Writing | — | 67.2% |
| WildBench | — | 79.1% |
| LiveBench Language | — | 28.6% |
Frequently asked questions
Is GLM-4.6 better than GPT-4o mini?
GLM-4.6 is the stronger model overall, scoring 40.9 to 25.4 on the Noometry Index. GPT-4o mini costs 3.8× less per token, which makes it the better buy when GLM-4.6's lead doesn't matter for your workload.
Which is cheaper, GLM-4.6 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-4.6 lists at $0.60 and $2.20.
Is GLM-4.6 or GPT-4o mini better for coding?
GLM-4.6 scores higher on coding benchmarks: 39.9 versus 21.6 in the Noometry coding category.
Which has the bigger context window?
GLM-4.6 does, with 205K tokens against 128K.
How many benchmarks do GLM-4.6 and GPT-4o mini share?
19 benchmarks have published results for both models. GLM-4.6 has 29 scored results on Noometry and GPT-4o mini has 60.