Model comparison
GLM-4.6 vs o4-mini
GLM-4.6 and o4-mini score almost the same on the Noometry Index (41.4 vs 41.6), so choose on price, context window or the category you care about most.
Last verified . 25 shared benchmarks.
Summary
- They share 25 benchmarks with published results for both. GLM-4.6 scores higher in 2 categories and o4-mini in 7 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where GLM-4.6 leads 61.1 to 54.0.
- The biggest single-benchmark swing is Kagi LLM Benchmark: 47.4% for GLM-4.6 and 67.6% for o4-mini.
- GLM-4.6 is cheaper at $0.60 / $2.20 per million input/output tokens, against $1.10 / $4.40 for o4-mini.
- GLM-4.6 accepts more context: 205K tokens versus 200K.
- GLM-4.6 has downloadable open weights; the other is API-only.
Side by side
| GLM-4.6 | o4-mini | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 41.4 | 41.6 |
| Released | 2025-09-30 | 2025-04-16 |
| Weights | Open | Proprietary |
| Context window | 205K | 200K |
| Max output | 131K | 100K |
| Input $ / M tokens | $0.60 | $1.10 |
| Output $ / M tokens | $2.20 | $4.40 |
| Results tracked | 29 | 60 |
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Category by category
Coding Too close to call
GLM-4.6: 40.1 (#148), o4-mini: 40.9 (#127)
| Benchmark | GLM-4.6 | o4-mini |
|---|---|---|
| SWE-bench Verified (bash only) | 55.4% | 45% |
| LMArena Coding | 1449 | 1368 |
| ALE-Bench | 340.82 | 826.17 |
| Aider Polyglot | — | 72% |
| LMArena WebDev | 1340 | — |
| SciCode | 38.4% | — |
| GSO | — | 3.6% |
| WeirdML | — | 52.6% |
| CadEval | — | 62% |
| AlgoTune | — | 1.72 |
Agentic & Tool Use Too close to call
GLM-4.6: 32.3 (#66), o4-mini: 32.6 (#61)
| Benchmark | GLM-4.6 | o4-mini |
|---|---|---|
| Berkeley Function Calling Leaderboard | 72.4% | 53.2% |
| Terminal-Bench | 24.5% | — |
| GDPval | — | 25.3% |
| METR Time Horizons | — | 63.9% |
Reasoning Too close to call
GLM-4.6: 23.7 (#172), o4-mini: 24.6 (#162)
| Benchmark | GLM-4.6 | o4-mini |
|---|---|---|
| Kagi LLM Benchmark | 47.4% | 67.6% |
| CritPt | 1.1% | 0.6% |
| LMArena Hard Prompts | 1440 | 1351 |
| ARC-AGI-2 | — | 6.1% |
| SimpleBench | — | 38.7% |
| ARC-AGI-1 | — | 58.7% |
| Chess Puzzles | — | 26% |
| EnigmaEval | — | 9.2% |
| Mystery Game Puzzles | — | 5% |
| DTBench | — | 77.6% |
| LMCA | — | 26.5% |
| Epoch Capabilities Index | — | 145.64 |
| ForecastBench | — | 61.8 |
Math o4-mini leads
GLM-4.6: 39.1 (#111), o4-mini: 40.8 (#89)
| Benchmark | GLM-4.6 | o4-mini |
|---|---|---|
| LMArena Math | 1432 | 1389 |
| FrontierMath (Feb 2025 set) | 3.8% | 24.8% |
| FrontierMath Tier 4 (v1) | 2.1% | 6.3% |
| FrontierMath (Tiers 1-3) | — | 36.1% |
| FrontierMath Tier 4 | — | 4.9% |
| OTIS Mock AIME 2024-2025 | — | 81.7% |
| Omni-MATH | — | 72% |
| MATH Level 5 | — | 97.8% |
Knowledge o4-mini leads
GLM-4.6: 40.2 (#124), o4-mini: 43.6 (#91)
| Benchmark | GLM-4.6 | o4-mini |
|---|---|---|
| Vectara Hallucination Rate | 9.5% | 18.6% |
| LMArena Expert | 1431 | 1343 |
| GPQA Diamond | — | 79.6% |
| Humanity's Last Exam | — | 18.1% |
| SimpleQA Verified | — | 19.6% |
| MMLU-Pro | — | 82% |
| Confabulations | — | 15.8% |
| GPQA (HELM) | — | 73.5% |
Multimodal Not comparable
GLM-4.6: —, o4-mini: 40.2 (#49)
| Benchmark | GLM-4.6 | o4-mini |
|---|---|---|
| LMArena Vision | — | 1194 |
| GeoBench | — | 64% |
| VPCT | — | 57.5% |
Multilingual GLM-4.6 leads
GLM-4.6: 53.5 (#66), o4-mini: 47.0 (#154)
| Benchmark | GLM-4.6 | o4-mini |
|---|---|---|
| LMArena Non-English | 1426 | 1337 |
| LMArena Chinese | 1499 | 1354 |
| LMArena French | 1459 | 1364 |
| LMArena German | 1447 | 1336 |
| LMArena Japanese | 1393 | 1308 |
| LMArena Korean | 1400 | 1312 |
| LMArena Russian | 1419 | 1334 |
| LMArena Spanish | 1436 | 1347 |
Instruction Following Too close to call
GLM-4.6: 74.3 (#98), o4-mini: 75.2 (#68)
| Benchmark | GLM-4.6 | o4-mini |
|---|---|---|
| LMArena Instruction Following | 1410 | 1321 |
| IFEval | — | 92.8% |
Long Context o4-mini leads
GLM-4.6: 43.4 (#94), o4-mini: 45.5 (#33)
| Benchmark | GLM-4.6 | o4-mini |
|---|---|---|
| LMArena Longer Query | 1422 | 1315 |
| Fiction.LiveBench | — | 77.8% |
Writing & Preference GLM-4.6 leads
GLM-4.6: 61.1 (#90), o4-mini: 54.0 (#152)
| Benchmark | GLM-4.6 | o4-mini |
|---|---|---|
| LMArena Text | 1440 | 1353 |
| LMArena Creative Writing | 1411 | 1294 |
| LMArena Multi-Turn | 1427 | 1350 |
| Short-Story Creative Writing | — | 75% |
| EQ-Bench Creative Writing | 1411 | — |
| WildBench | — | 85.4% |
Frequently asked questions
Is GLM-4.6 better than o4-mini?
GLM-4.6 and o4-mini score almost the same on the Noometry Index (41.4 vs 41.6), so choose on price, context window or the category you care about most.
Which is cheaper, GLM-4.6 or o4-mini?
GLM-4.6 is cheaper. It lists at $0.60 per million input tokens and $2.20 per million output tokens; o4-mini lists at $1.10 and $4.40.
Is GLM-4.6 or o4-mini better for coding?
They score almost the same on coding (40.1 vs 40.9); test both on your own repository before choosing.
Which has the bigger context window?
GLM-4.6 does, with 205K tokens against 200K.
How many benchmarks do GLM-4.6 and o4-mini share?
25 benchmarks have published results for both models. GLM-4.6 has 29 scored results on Noometry and o4-mini has 60.