Model comparison
GLM-4.5V vs GPT-4o mini
GLM-4.5V is the stronger model overall, scoring 39.8 to 25.5 on the Noometry Index. GPT-4o mini costs 3.4× less per token, which makes it the better buy when GLM-4.5V'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 9 categories and GPT-4o mini in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-4.5V leads 37.4 to 10.4.
- The biggest single-benchmark swing is Kagi LLM Benchmark: 59.8% for GLM-4.5V 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 / $1.80 for GLM-4.5V.
- GPT-4o mini accepts more context: 128K tokens versus 64K.
- GLM-4.5V has downloadable open weights; the other is API-only.
Side by side
| GLM-4.5V | GPT-4o mini | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 39.8 | 25.5 |
| Released | 2025-08-11 | 2024-07-18 |
| Weights | Open | Proprietary |
| Context window | 64K | 128K |
| Max output | 16K | 16K |
| Input $ / M tokens | $0.60 | $0.15 |
| Output $ / M tokens | $1.80 | $0.60 |
| Results tracked | 15 | 60 |
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Category by category
Coding GLM-4.5V leads
GLM-4.5V: 39.5 (#155), GPT-4o mini: 22.0 (#335)
| Benchmark | GLM-4.5V | GPT-4o mini |
|---|---|---|
| LMArena Coding | 1347 | 1290 |
| Aider Polyglot | — | 3.6% |
| WeirdML | — | 11.8% |
| BigCodeBench Instruct | — | 46.1% |
| LiveBench Coding | — | 43.1% |
| BigCodeBench Complete | — | 57.4% |
| HumanEval+ | — | 83.5% |
| MBPP+ | — | 72.2% |
Agentic & Tool Use Not comparable
GLM-4.5V: —, GPT-4o mini: 27.5 (#101)
| Benchmark | GLM-4.5V | GPT-4o mini |
|---|---|---|
| BALROG | — | 17.4% |
Reasoning GLM-4.5V leads
GLM-4.5V: 27.4 (#119), GPT-4o mini: 8.7 (#347)
| Benchmark | GLM-4.5V | GPT-4o mini |
|---|---|---|
| Kagi LLM Benchmark | 59.8% | 28.8% |
| LMArena Hard Prompts | 1334 | 1267 |
| ARC-AGI-2 | — | 0% |
| SimpleBench | — | 10.7% |
| 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-4.5V leads
GLM-4.5V: 37.4 (#159), GPT-4o mini: 10.4 (#314)
| Benchmark | GLM-4.5V | GPT-4o mini |
|---|---|---|
| LMArena Math | 1354 | 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% |
| GSM8K | — | 91.3% |
Knowledge GLM-4.5V leads
GLM-4.5V: 37.5 (#156), GPT-4o mini: 17.7 (#284)
| Benchmark | GLM-4.5V | GPT-4o mini |
|---|---|---|
| LMArena Expert | 1353 | 1235 |
| GPQA Diamond | — | 37.7% |
| SimpleQA Verified | — | 8.3% |
| MMLU-Pro | — | 60.3% |
| Confabulations | — | 37.2% |
| GPQA (HELM) | — | 36.8% |
| BoolQ | — | 88.7% |
| MMLU | — | 81.8% |
Multimodal GLM-4.5V leads
GLM-4.5V: 34.3 (#92), GPT-4o mini: 25.9 (#122)
| Benchmark | GLM-4.5V | GPT-4o mini |
|---|---|---|
| LMArena Vision | 1154 | 1066 |
| Video-MME | — | 64.8% |
| GeoBench | — | 64% |
| VPCT | — | 34% |
Multilingual GLM-4.5V leads
GLM-4.5V: 44.6 (#177), GPT-4o mini: 42.0 (#199)
| Benchmark | GLM-4.5V | GPT-4o mini |
|---|---|---|
| LMArena Non-English | 1303 | 1266 |
| LMArena Chinese | 1337 | 1265 |
| LMArena Russian | 1298 | 1275 |
| LMArena Spanish | 1336 | 1276 |
| LMArena French | — | 1297 |
| LMArena German | — | 1272 |
| LMArena Japanese | — | 1216 |
| LMArena Korean | — | 1195 |
Instruction Following GLM-4.5V leads
GLM-4.5V: 69.2 (#175), GPT-4o mini: 61.9 (#239)
| Benchmark | GLM-4.5V | GPT-4o mini |
|---|---|---|
| LMArena Instruction Following | 1311 | 1258 |
| LiveBench Instruction Following | — | 56.8% |
| IFEval | — | 78.2% |
Long Context Too close to call
GLM-4.5V: 39.6 (#171), GPT-4o mini: 39.1 (#186)
| Benchmark | GLM-4.5V | GPT-4o mini |
|---|---|---|
| LMArena Longer Query | 1304 | 1289 |
Writing & Preference GLM-4.5V leads
GLM-4.5V: 52.5 (#170), GPT-4o mini: 39.5 (#248)
| Benchmark | GLM-4.5V | GPT-4o mini |
|---|---|---|
| LMArena Text | 1333 | 1286 |
| LMArena Creative Writing | 1295 | 1268 |
| LMArena Multi-Turn | 1332 | 1285 |
| Short-Story Creative Writing | — | 67.2% |
| EQ-Bench Creative Writing | — | 873 |
| WildBench | — | 79.1% |
| LiveBench Language | — | 28.6% |
Frequently asked questions
Is GLM-4.5V better than GPT-4o mini?
GLM-4.5V is the stronger model overall, scoring 39.8 to 25.5 on the Noometry Index. GPT-4o mini costs 3.4× less per token, which makes it the better buy when GLM-4.5V's lead doesn't matter for your workload.
Which is cheaper, GLM-4.5V 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-4.5V lists at $0.60 and $1.80.
Is GLM-4.5V or GPT-4o mini better for coding?
GLM-4.5V scores higher on coding benchmarks: 39.5 versus 22.0 in the Noometry coding category.
Which has the bigger context window?
GPT-4o mini does, with 128K tokens against 64K.
How many benchmarks do GLM-4.5V and GPT-4o mini share?
15 benchmarks have published results for both models. GLM-4.5V has 15 scored results on Noometry and GPT-4o mini has 60.