Model comparison
GLM-5.1 vs o4-mini
GLM-5.1 is the stronger model overall, scoring 47.8 to 41.6 on the Noometry Index.
Last verified . 29 shared benchmarks.
Summary
- They share 29 benchmarks with published results for both. GLM-5.1 scores higher in 7 categories and o4-mini in 2 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GLM-5.1 leads 39.1 to 24.6.
- The biggest single-benchmark swing is SimpleBench: 55.1% for GLM-5.1 and 38.7% for o4-mini.
- o4-mini is cheaper at $1.10 / $4.40 per million input/output tokens, against $1.40 / $4.40 for GLM-5.1.
- GLM-5.1 has downloadable open weights; the other is API-only.
Side by side
| GLM-5.1 | o4-mini | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 47.8 | 41.6 |
| Released | 2026-04-07 | 2025-04-16 |
| Weights | Open | Proprietary |
| Context window | 200K | 200K |
| Max output | 131K | 100K |
| Input $ / M tokens | $1.40 | $1.10 |
| Output $ / M tokens | $4.40 | $4.40 |
| Results tracked | 41 | 60 |
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Category by category
Coding GLM-5.1 leads
GLM-5.1: 48.7 (#55), o4-mini: 40.9 (#127)
| Benchmark | GLM-5.1 | o4-mini |
|---|---|---|
| WeirdML | 57.1% | 52.6% |
| LMArena Coding | 1485 | 1368 |
| ALE-Bench | 887.1 | 826.17 |
| SWE-bench Verified | 74.2% | — |
| SWE-bench Verified (bash only) | — | 45% |
| Aider Polyglot | — | 72% |
| LMArena WebDev | 1508 | — |
| SciCode | 43.8% | — |
| GSO | — | 3.6% |
| CadEval | — | 62% |
| AlgoTune | — | 1.72 |
Agentic & Tool Use o4-mini leads
GLM-5.1: 24.9 (#113), o4-mini: 32.6 (#61)
| Benchmark | GLM-5.1 | o4-mini |
|---|---|---|
| APEX-Agents | 40.9% | — |
| Berkeley Function Calling Leaderboard | — | 53.2% |
| GDPval | — | 25.3% |
| ExploitBench | 18.1% | — |
| GBAEval | 0% | — |
| METR Time Horizons | — | 63.9% |
| Vending-Bench 2 | 5,634 | — |
Reasoning GLM-5.1 leads
GLM-5.1: 39.1 (#60), o4-mini: 24.6 (#162)
| Benchmark | GLM-5.1 | o4-mini |
|---|---|---|
| SimpleBench | 55.1% | 38.7% |
| CritPt | 4.6% | 0.6% |
| Chess Puzzles | 19% | 26% |
| LMArena Hard Prompts | 1472 | 1351 |
| Epoch Capabilities Index | 149.84 | 145.64 |
| ARC-AGI-2 | — | 6.1% |
| Kagi LLM Benchmark | — | 67.6% |
| NYT Connections (extended) | 77.7% | — |
| ARC-AGI-1 | — | 58.7% |
| EnigmaEval | — | 9.2% |
| Thematic Generalization | 69.8% | — |
| Mystery Game Puzzles | — | 5% |
| DTBench | — | 77.6% |
| LMCA | — | 26.5% |
| ForecastBench | — | 61.8 |
Math GLM-5.1 leads
GLM-5.1: 49.7 (#60), o4-mini: 40.8 (#89)
| Benchmark | GLM-5.1 | o4-mini |
|---|---|---|
| FrontierMath (Tiers 1-3) | 36.8% | 36.1% |
| OTIS Mock AIME 2024-2025 | 93.3% | 81.7% |
| LMArena Math | 1473 | 1389 |
| FrontierMath (Feb 2025 set) | 33.4% | 24.8% |
| FrontierMath Tier 4 (v1) | 12.5% | 6.3% |
| FrontierMath Tier 4 | — | 4.9% |
| MathArena Final-Answer Competitions | 67.1% | — |
| ProofBench | 22.2% | — |
| Omni-MATH | — | 72% |
| MATH Level 5 | — | 97.8% |
Knowledge GLM-5.1 leads
GLM-5.1: 54.9 (#50), o4-mini: 43.6 (#91)
| Benchmark | GLM-5.1 | o4-mini |
|---|---|---|
| GPQA Diamond | 89.9% | 79.6% |
| SimpleQA Verified | 34% | 19.6% |
| LMArena Expert | 1476 | 1343 |
| Humanity's Last Exam | — | 18.1% |
| MMLU-Pro | — | 82% |
| Confabulations | — | 15.8% |
| Vectara Hallucination Rate | — | 18.6% |
| GPQA (HELM) | — | 73.5% |
Multimodal Not comparable
GLM-5.1: —, o4-mini: 40.2 (#49)
| Benchmark | GLM-5.1 | o4-mini |
|---|---|---|
| LMArena Vision | — | 1194 |
| GeoBench | — | 64% |
| VPCT | — | 57.5% |
Multilingual GLM-5.1 leads
GLM-5.1: 55.0 (#36), o4-mini: 47.0 (#154)
| Benchmark | GLM-5.1 | o4-mini |
|---|---|---|
| LMArena Non-English | 1447 | 1337 |
| LMArena Chinese | 1515 | 1354 |
| LMArena French | 1474 | 1364 |
| LMArena German | 1465 | 1336 |
| LMArena Japanese | 1434 | 1308 |
| LMArena Korean | 1418 | 1312 |
| LMArena Russian | 1454 | 1334 |
| LMArena Spanish | 1469 | 1347 |
Instruction Following GLM-5.1 leads
GLM-5.1: 76.3 (#42), o4-mini: 75.2 (#68)
| Benchmark | GLM-5.1 | o4-mini |
|---|---|---|
| LMArena Instruction Following | 1451 | 1321 |
| IFEval | — | 92.8% |
Long Context Too close to call
GLM-5.1: 44.9 (#53), o4-mini: 45.5 (#33)
| Benchmark | GLM-5.1 | o4-mini |
|---|---|---|
| LMArena Longer Query | 1466 | 1315 |
| Fiction.LiveBench | — | 77.8% |
Writing & Preference GLM-5.1 leads
GLM-5.1: 66.9 (#31), o4-mini: 54.0 (#152)
| Benchmark | GLM-5.1 | o4-mini |
|---|---|---|
| LMArena Text | 1461 | 1353 |
| LMArena Creative Writing | 1453 | 1294 |
| LMArena Multi-Turn | 1472 | 1350 |
| Short-Story Creative Writing | — | 75% |
| EQ-Bench Creative Writing | 1592 | — |
| WildBench | — | 85.4% |
Frequently asked questions
Is GLM-5.1 better than o4-mini?
GLM-5.1 is the stronger model overall, scoring 47.8 to 41.6 on the Noometry Index.
Which is cheaper, GLM-5.1 or o4-mini?
o4-mini is cheaper. It lists at $1.10 per million input tokens and $4.40 per million output tokens; GLM-5.1 lists at $1.40 and $4.40.
Is GLM-5.1 or o4-mini better for coding?
GLM-5.1 scores higher on coding benchmarks: 48.7 versus 40.9 in the Noometry coding category.
Which has the bigger context window?
Both accept 200K tokens.
How many benchmarks do GLM-5.1 and o4-mini share?
29 benchmarks have published results for both models. GLM-5.1 has 41 scored results on Noometry and o4-mini has 60.