Model comparison
GLM-4.5V vs GPT-5
GPT-5 is the stronger model overall, scoring 50.9 to 39.2 on the Noometry Index. GLM-4.5V costs 3.8× less per token, which makes it the better buy when GPT-5'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 0 categories and GPT-5 in 9 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in long context, where GPT-5 leads 69.5 to 39.6.
- The biggest single-benchmark swing is Kagi LLM Benchmark: 59.8% for GLM-4.5V and 72.7% for GPT-5.
- GLM-4.5V is cheaper at $0.60 / $1.80 per million input/output tokens, against $1.25 / $10 for GPT-5.
- GPT-5 accepts more context: 400K tokens versus 64K.
- GLM-4.5V has downloadable open weights; the other is API-only.
Side by side
| GLM-4.5V | GPT-5 | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 39.2 | 50.9 |
| Released | 2025-08-11 | 2025-08-07 |
| Weights | Open | Proprietary |
| Context window | 64K | 400K |
| Max output | 16K | 128K |
| Input $ / M tokens | $0.60 | $1.25 |
| Output $ / M tokens | $1.80 | $10 |
| Results tracked | 15 | 69 |
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Category by category
Coding GPT-5 leads
GLM-4.5V: 38.9 (#152), GPT-5: 50.7 (#39)
| Benchmark | GLM-4.5V | GPT-5 |
|---|---|---|
| LMArena Coding | 1347 | 1436 |
| SWE-bench Verified | — | 73.6% |
| SWE-bench Verified (bash only) | — | 65% |
| Aider Polyglot | — | 88% |
| LMArena WebDev | — | 1417 |
| SciCode | — | 42.9% |
| GSO | — | 6.9% |
| WeirdML | — | 60.7% |
| ALE-Bench | — | 1,162 |
| AlgoTune | — | 1.67 |
Agentic & Tool Use Not comparable
GLM-4.5V: —, GPT-5: 33.0 (#53)
| Benchmark | GLM-4.5V | GPT-5 |
|---|---|---|
| Terminal-Bench | — | 49.6% |
| GDPval | — | 34.8% |
| Remote Labor Index | — | 1.7% |
| DeepResearch Bench | — | 49.6% |
| BALROG | — | 32.8% |
| LMArena Search | — | 1133 |
| METR Time Horizons | — | 69.6% |
Reasoning GPT-5 leads
GLM-4.5V: 26.5 (#118), GPT-5: 38.3 (#55)
| Benchmark | GLM-4.5V | GPT-5 |
|---|---|---|
| Kagi LLM Benchmark | 59.8% | 72.7% |
| LMArena Hard Prompts | 1335 | 1415 |
| ARC-AGI-2 | — | 9.9% |
| SimpleBench | — | 56.7% |
| ARC-AGI-1 | — | 65.7% |
| CritPt | — | 12.6% |
| Chess Puzzles | — | 37% |
| EnigmaEval | — | 10.5% |
| EBR-Bench | — | 12.7% |
| Mystery Game Puzzles | — | 23% |
| DTBench | — | 90.7% |
| LMCA | — | 40% |
| Epoch Capabilities Index | — | 150 |
| ForecastBench | — | 61.4 |
Math GPT-5 leads
GLM-4.5V: 34.7 (#157), GPT-5: 54.6 (#36)
| Benchmark | GLM-4.5V | GPT-5 |
|---|---|---|
| LMArena Math | 1353 | 1406 |
| FrontierMath (Tiers 1-3) | — | 55.4% |
| FrontierMath Tier 4 | — | 22% |
| OTIS Mock AIME 2024-2025 | — | 91.4% |
| ProofBench | — | 18% |
| Omni-MATH | — | 64.7% |
| MATH Level 5 | — | 98.1% |
| FrontierMath (Feb 2025 set) | — | 32.4% |
| FrontierMath Tier 4 (v1) | — | 12.5% |
Knowledge GPT-5 leads
GLM-4.5V: 36.8 (#155), GPT-5: 56.5 (#37)
| Benchmark | GLM-4.5V | GPT-5 |
|---|---|---|
| LMArena Expert | 1353 | 1420 |
| GPQA Diamond | — | 86.2% |
| Humanity's Last Exam | — | 25.3% |
| SimpleQA Verified | — | 50.1% |
| MMLU-Pro | — | 86.3% |
| Confabulations | — | 10.3% |
| Vectara Hallucination Rate | — | 14.7% |
| GPQA (HELM) | — | 79.2% |
Multimodal GPT-5 leads
GLM-4.5V: 33.6 (#86), GPT-5: 46.1 (#12)
| Benchmark | GLM-4.5V | GPT-5 |
|---|---|---|
| LMArena Vision | 1154 | 1232 |
| GeoBench | — | 81% |
| VPCT | — | 66% |
Multilingual GPT-5 leads
GLM-4.5V: 44.9 (#172), GPT-5: 51.5 (#105)
| Benchmark | GLM-4.5V | GPT-5 |
|---|---|---|
| LMArena Non-English | 1304 | 1397 |
| LMArena Chinese | 1338 | 1423 |
| LMArena Russian | 1299 | 1406 |
| LMArena Spanish | 1335 | 1398 |
| LMArena French | — | 1409 |
| LMArena German | — | 1415 |
| LMArena Japanese | — | 1411 |
| LMArena Korean | — | 1361 |
Instruction Following GPT-5 leads
GLM-4.5V: 69.4 (#170), GPT-5: 74.0 (#107)
| Benchmark | GLM-4.5V | GPT-5 |
|---|---|---|
| LMArena Instruction Following | 1311 | 1388 |
| IFEval | — | 87.5% |
Long Context GPT-5 leads
GLM-4.5V: 39.6 (#166), GPT-5: 69.5 (#2)
| Benchmark | GLM-4.5V | GPT-5 |
|---|---|---|
| LMArena Longer Query | 1305 | 1399 |
| Fiction.LiveBench | — | 97.2% |
Writing & Preference GPT-5 leads
GLM-4.5V: 52.7 (#162), GPT-5: 63.5 (#63)
| Benchmark | GLM-4.5V | GPT-5 |
|---|---|---|
| LMArena Text | 1333 | 1406 |
| LMArena Creative Writing | 1295 | 1365 |
| LMArena Multi-Turn | 1333 | 1426 |
| Short-Story Creative Writing | — | 86% |
| EQ-Bench Creative Writing | — | 1627 |
| WildBench | — | 85.7% |
Frequently asked questions
Is GLM-4.5V better than GPT-5?
GPT-5 is the stronger model overall, scoring 50.9 to 39.2 on the Noometry Index. GLM-4.5V costs 3.8× less per token, which makes it the better buy when GPT-5's lead doesn't matter for your workload.
Which is cheaper, GLM-4.5V or GPT-5?
GLM-4.5V is cheaper. It lists at $0.60 per million input tokens and $1.80 per million output tokens; GPT-5 lists at $1.25 and $10.
Is GLM-4.5V or GPT-5 better for coding?
GPT-5 scores higher on coding benchmarks: 50.7 versus 38.9 in the Noometry coding category.
Which has the bigger context window?
GPT-5 does, with 400K tokens against 64K.
How many benchmarks do GLM-4.5V and GPT-5 share?
15 benchmarks have published results for both models. GLM-4.5V has 15 scored results on Noometry and GPT-5 has 69.