Model comparison
GLM-4.5V vs o4-mini
o4-mini is the stronger model overall, scoring 41.6 to 39.8 on the Noometry Index. GLM-4.5V costs 2.1× less per token, which makes it the better buy when o4-mini's lead doesn't matter for your workload.
Last verified . 15 shared benchmarks.
Summary
- They share 15 benchmarks with published results for both. GLM-4.5V scores higher in 1 category and o4-mini in 8 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where o4-mini leads 43.6 to 37.5.
- The biggest single-benchmark swing is Kagi LLM Benchmark: 59.8% for GLM-4.5V and 67.6% for o4-mini.
- GLM-4.5V is cheaper at $0.60 / $1.80 per million input/output tokens, against $1.10 / $4.40 for o4-mini.
- o4-mini accepts more context: 200K tokens versus 64K.
- GLM-4.5V has downloadable open weights; the other is API-only.
Side by side
| GLM-4.5V | o4-mini | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 39.8 | 41.6 |
| Released | 2025-08-11 | 2025-04-16 |
| Weights | Open | Proprietary |
| Context window | 64K | 200K |
| Max output | 16K | 100K |
| Input $ / M tokens | $0.60 | $1.10 |
| Output $ / M tokens | $1.80 | $4.40 |
| Results tracked | 15 | 60 |
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Category by category
Coding o4-mini leads
GLM-4.5V: 39.5 (#155), o4-mini: 40.9 (#127)
| Benchmark | GLM-4.5V | o4-mini |
|---|---|---|
| LMArena Coding | 1347 | 1368 |
| SWE-bench Verified (bash only) | — | 45% |
| Aider Polyglot | — | 72% |
| GSO | — | 3.6% |
| WeirdML | — | 52.6% |
| CadEval | — | 62% |
| ALE-Bench | — | 826.17 |
| AlgoTune | — | 1.72 |
Agentic & Tool Use Not comparable
GLM-4.5V: —, o4-mini: 32.6 (#61)
| Benchmark | GLM-4.5V | o4-mini |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 53.2% |
| GDPval | — | 25.3% |
| METR Time Horizons | — | 63.9% |
Reasoning GLM-4.5V leads
GLM-4.5V: 27.4 (#119), o4-mini: 24.6 (#162)
| Benchmark | GLM-4.5V | o4-mini |
|---|---|---|
| Kagi LLM Benchmark | 59.8% | 67.6% |
| LMArena Hard Prompts | 1334 | 1351 |
| ARC-AGI-2 | — | 6.1% |
| SimpleBench | — | 38.7% |
| ARC-AGI-1 | — | 58.7% |
| CritPt | — | 0.6% |
| 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-4.5V: 37.4 (#159), o4-mini: 40.8 (#89)
| Benchmark | GLM-4.5V | o4-mini |
|---|---|---|
| LMArena Math | 1354 | 1389 |
| 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% |
| FrontierMath (Feb 2025 set) | — | 24.8% |
| FrontierMath Tier 4 (v1) | — | 6.3% |
Knowledge o4-mini leads
GLM-4.5V: 37.5 (#156), o4-mini: 43.6 (#91)
| Benchmark | GLM-4.5V | o4-mini |
|---|---|---|
| LMArena Expert | 1353 | 1343 |
| GPQA Diamond | — | 79.6% |
| Humanity's Last Exam | — | 18.1% |
| SimpleQA Verified | — | 19.6% |
| MMLU-Pro | — | 82% |
| Confabulations | — | 15.8% |
| Vectara Hallucination Rate | — | 18.6% |
| GPQA (HELM) | — | 73.5% |
Multimodal o4-mini leads
GLM-4.5V: 34.3 (#92), o4-mini: 40.2 (#49)
| Benchmark | GLM-4.5V | o4-mini |
|---|---|---|
| LMArena Vision | 1154 | 1194 |
| GeoBench | — | 64% |
| VPCT | — | 57.5% |
Multilingual o4-mini leads
GLM-4.5V: 44.6 (#177), o4-mini: 47.0 (#154)
| Benchmark | GLM-4.5V | o4-mini |
|---|---|---|
| LMArena Non-English | 1303 | 1337 |
| LMArena Chinese | 1337 | 1354 |
| LMArena Russian | 1298 | 1334 |
| LMArena Spanish | 1336 | 1347 |
| LMArena French | — | 1364 |
| LMArena German | — | 1336 |
| LMArena Japanese | — | 1308 |
| LMArena Korean | — | 1312 |
Instruction Following o4-mini leads
GLM-4.5V: 69.2 (#175), o4-mini: 75.2 (#68)
| Benchmark | GLM-4.5V | o4-mini |
|---|---|---|
| LMArena Instruction Following | 1311 | 1321 |
| IFEval | — | 92.8% |
Long Context o4-mini leads
GLM-4.5V: 39.6 (#171), o4-mini: 45.5 (#33)
| Benchmark | GLM-4.5V | o4-mini |
|---|---|---|
| LMArena Longer Query | 1304 | 1315 |
| Fiction.LiveBench | — | 77.8% |
Writing & Preference o4-mini leads
GLM-4.5V: 52.5 (#170), o4-mini: 54.0 (#152)
| Benchmark | GLM-4.5V | o4-mini |
|---|---|---|
| LMArena Text | 1333 | 1353 |
| LMArena Creative Writing | 1295 | 1294 |
| LMArena Multi-Turn | 1332 | 1350 |
| Short-Story Creative Writing | — | 75% |
| WildBench | — | 85.4% |
Frequently asked questions
Is GLM-4.5V better than o4-mini?
o4-mini is the stronger model overall, scoring 41.6 to 39.8 on the Noometry Index. GLM-4.5V costs 2.1× less per token, which makes it the better buy when o4-mini's lead doesn't matter for your workload.
Which is cheaper, GLM-4.5V or o4-mini?
GLM-4.5V is cheaper. It lists at $0.60 per million input tokens and $1.80 per million output tokens; o4-mini lists at $1.10 and $4.40.
Is GLM-4.5V or o4-mini better for coding?
o4-mini scores higher on coding benchmarks: 40.9 versus 39.5 in the Noometry coding category.
Which has the bigger context window?
o4-mini does, with 200K tokens against 64K.
How many benchmarks do GLM-4.5V and o4-mini share?
15 benchmarks have published results for both models. GLM-4.5V has 15 scored results on Noometry and o4-mini has 60.