Model comparison
GLM-4.5V vs GPT-4.1 mini
GLM-4.5V is the stronger model overall, scoring 39.8 to 33.6 on the Noometry Index.
Last verified . 15 shared benchmarks.
Summary
- They share 15 benchmarks with published results for both. GLM-4.5V scores higher in 6 categories and GPT-4.1 mini in 3 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GLM-4.5V leads 27.4 to 10.8.
- The biggest single-benchmark swing is Kagi LLM Benchmark: 59.8% for GLM-4.5V and 48.6% for GPT-4.1 mini.
- GPT-4.1 mini is cheaper at $0.40 / $1.60 per million input/output tokens, against $0.60 / $1.80 for GLM-4.5V.
- GPT-4.1 mini accepts more context: 1.05M tokens versus 64K.
- GLM-4.5V has downloadable open weights; the other is API-only.
Side by side
| GLM-4.5V | GPT-4.1 mini | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 39.8 | 33.6 |
| Released | 2025-08-11 | 2025-04-14 |
| Weights | Open | Proprietary |
| Context window | 64K | 1.05M |
| Max output | 16K | 33K |
| Input $ / M tokens | $0.60 | $0.40 |
| Output $ / M tokens | $1.80 | $1.60 |
| Results tracked | 15 | 47 |
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Category by category
Coding GLM-4.5V leads
GLM-4.5V: 39.5 (#155), GPT-4.1 mini: 30.6 (#293)
| Benchmark | GLM-4.5V | GPT-4.1 mini |
|---|---|---|
| LMArena Coding | 1347 | 1367 |
| SWE-bench Verified (bash only) | — | 23.9% |
| Aider Polyglot | — | 32.4% |
| SciCode | — | 40.4% |
| WeirdML | — | 37.6% |
| BigCodeBench Instruct | — | 48.9% |
| CadEval | — | 16% |
Agentic & Tool Use Not comparable
GLM-4.5V: —, GPT-4.1 mini: 33.3 (#55)
| Benchmark | GLM-4.5V | GPT-4.1 mini |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 50.5% |
Reasoning GLM-4.5V leads
GLM-4.5V: 27.4 (#119), GPT-4.1 mini: 10.8 (#340)
| Benchmark | GLM-4.5V | GPT-4.1 mini |
|---|---|---|
| Kagi LLM Benchmark | 59.8% | 48.6% |
| LMArena Hard Prompts | 1334 | 1349 |
| ARC-AGI-2 | — | 0% |
| ARC-AGI-1 | — | 3.5% |
| CritPt | — | 0% |
| Chess Puzzles | — | 7% |
| Mystery Game Puzzles | — | 7% |
| DTBench | — | 68.8% |
| LMCA | — | 21.1% |
| Epoch Capabilities Index | — | 135.01 |
Math GLM-4.5V leads
GLM-4.5V: 37.4 (#159), GPT-4.1 mini: 24.1 (#270)
| Benchmark | GLM-4.5V | GPT-4.1 mini |
|---|---|---|
| LMArena Math | 1354 | 1343 |
| FrontierMath (Tiers 1-3) | — | 6.7% |
| OTIS Mock AIME 2024-2025 | — | 44.7% |
| Omni-MATH | — | 49.1% |
| MATH Level 5 | — | 87.3% |
| FrontierMath (Feb 2025 set) | — | 4.5% |
Knowledge GLM-4.5V leads
GLM-4.5V: 37.5 (#156), GPT-4.1 mini: 34.7 (#194)
| Benchmark | GLM-4.5V | GPT-4.1 mini |
|---|---|---|
| LMArena Expert | 1353 | 1338 |
| GPQA Diamond | — | 65.8% |
| SimpleQA Verified | — | 12.7% |
| MMLU-Pro | — | 78.3% |
| GPQA (HELM) | — | 61.4% |
Multimodal GPT-4.1 mini leads
GLM-4.5V: 34.3 (#92), GPT-4.1 mini: 35.8 (#82)
| Benchmark | GLM-4.5V | GPT-4.1 mini |
|---|---|---|
| LMArena Vision | 1154 | 1181 |
Multilingual GPT-4.1 mini leads
GLM-4.5V: 44.6 (#177), GPT-4.1 mini: 45.7 (#166)
| Benchmark | GLM-4.5V | GPT-4.1 mini |
|---|---|---|
| LMArena Non-English | 1303 | 1318 |
| LMArena Chinese | 1337 | 1329 |
| LMArena Russian | 1298 | 1324 |
| LMArena Spanish | 1336 | 1319 |
| LMArena French | — | 1358 |
| LMArena German | — | 1351 |
| LMArena Japanese | — | 1290 |
| LMArena Korean | — | 1298 |
Instruction Following GPT-4.1 mini leads
GLM-4.5V: 69.2 (#175), GPT-4.1 mini: 73.7 (#118)
| Benchmark | GLM-4.5V | GPT-4.1 mini |
|---|---|---|
| LMArena Instruction Following | 1311 | 1333 |
| IFEval | — | 90.4% |
Long Context GLM-4.5V leads
GLM-4.5V: 39.6 (#171), GPT-4.1 mini: 31.8 (#275)
| Benchmark | GLM-4.5V | GPT-4.1 mini |
|---|---|---|
| LMArena Longer Query | 1304 | 1344 |
| Fiction.LiveBench | — | 44.4% |
Writing & Preference GLM-4.5V leads
GLM-4.5V: 52.5 (#170), GPT-4.1 mini: 48.6 (#199)
| Benchmark | GLM-4.5V | GPT-4.1 mini |
|---|---|---|
| LMArena Text | 1333 | 1340 |
| LMArena Creative Writing | 1295 | 1300 |
| LMArena Multi-Turn | 1332 | 1354 |
| EQ-Bench Creative Writing | — | 1147 |
| WildBench | — | 83.8% |
Frequently asked questions
Is GLM-4.5V better than GPT-4.1 mini?
GLM-4.5V is the stronger model overall, scoring 39.8 to 33.6 on the Noometry Index.
Which is cheaper, GLM-4.5V or GPT-4.1 mini?
GPT-4.1 mini is cheaper. It lists at $0.40 per million input tokens and $1.60 per million output tokens; GLM-4.5V lists at $0.60 and $1.80.
Is GLM-4.5V or GPT-4.1 mini better for coding?
GLM-4.5V scores higher on coding benchmarks: 39.5 versus 30.6 in the Noometry coding category.
Which has the bigger context window?
GPT-4.1 mini does, with 1.05M tokens against 64K.
How many benchmarks do GLM-4.5V and GPT-4.1 mini share?
15 benchmarks have published results for both models. GLM-4.5V has 15 scored results on Noometry and GPT-4.1 mini has 47.