Model comparison
GLM-4.6 vs GPT-5 Mini
GLM-4.6 and GPT-5 Mini score almost the same on the Noometry Index (41.4 vs 41.8), so choose on price, context window or the category you care about most.
Last verified . 28 shared benchmarks.
Summary
- They share 28 benchmarks with published results for both. GLM-4.6 scores higher in 4 categories and GPT-5 Mini in 5 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5 Mini leads 46.7 to 39.1.
- The biggest single-benchmark swing is Kagi LLM Benchmark: 47.4% for GLM-4.6 and 70.3% for GPT-5 Mini.
- GPT-5 Mini is cheaper at $0.25 / $2 per million input/output tokens, against $0.60 / $2.20 for GLM-4.6.
- GPT-5 Mini accepts more context: 400K tokens versus 205K.
- GLM-4.6 has downloadable open weights; the other is API-only.
Side by side
| GLM-4.6 | GPT-5 Mini | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 41.4 | 41.8 |
| Released | 2025-09-30 | 2025-08-07 |
| Weights | Open | Proprietary |
| Context window | 205K | 400K |
| Max output | 131K | 128K |
| Input $ / M tokens | $0.60 | $0.25 |
| Output $ / M tokens | $2.20 | $2 |
| Results tracked | 29 | 60 |
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Category by category
Coding Too close to call
GLM-4.6: 40.1 (#148), GPT-5 Mini: 40.1 (#146)
| Benchmark | GLM-4.6 | GPT-5 Mini |
|---|---|---|
| SWE-bench Verified (bash only) | 55.4% | 59.8% |
| SciCode | 38.4% | 39.2% |
| LMArena Coding | 1449 | 1406 |
| ALE-Bench | 340.82 | 799.77 |
| SWE-bench Verified | — | 64.7% |
| LMArena WebDev | 1340 | — |
| SWE-bench Multilingual | — | 39.7% |
| WeirdML | — | 52.7% |
| AlgoTune | — | 1.38 |
Agentic & Tool Use GLM-4.6 leads
GLM-4.6: 32.3 (#66), GPT-5 Mini: 31.1 (#70)
| Benchmark | GLM-4.6 | GPT-5 Mini |
|---|---|---|
| Terminal-Bench | 24.5% | 34.8% |
| Berkeley Function Calling Leaderboard | 72.4% | 55.5% |
| Vending-Bench 2 | — | -31.18 |
Reasoning Too close to call
GLM-4.6: 23.7 (#172), GPT-5 Mini: 23.9 (#168)
| Benchmark | GLM-4.6 | GPT-5 Mini |
|---|---|---|
| Kagi LLM Benchmark | 47.4% | 70.3% |
| CritPt | 1.1% | 0% |
| LMArena Hard Prompts | 1440 | 1380 |
| ARC-AGI-2 | — | 4.4% |
| ARC-AGI-1 | — | 54.3% |
| Chess Puzzles | — | 30% |
| EnigmaEval | — | 8.2% |
| Mystery Game Puzzles | — | 10% |
| DTBench | — | 80.5% |
| LMCA | — | 34.2% |
| Epoch Capabilities Index | — | 145.52 |
| ForecastBench | — | 61 |
Math GPT-5 Mini leads
GLM-4.6: 39.1 (#111), GPT-5 Mini: 46.7 (#69)
| Benchmark | GLM-4.6 | GPT-5 Mini |
|---|---|---|
| LMArena Math | 1432 | 1378 |
| FrontierMath (Feb 2025 set) | 3.8% | 27.2% |
| FrontierMath Tier 4 (v1) | 2.1% | 6.3% |
| FrontierMath (Tiers 1-3) | — | 46.7% |
| FrontierMath Tier 4 | — | 12.2% |
| OTIS Mock AIME 2024-2025 | — | 86.7% |
| ProofBench | — | 9% |
| Omni-MATH | — | 72.2% |
| MATH Level 5 | — | 97.8% |
Knowledge GPT-5 Mini leads
GLM-4.6: 40.2 (#124), GPT-5 Mini: 45.6 (#86)
| Benchmark | GLM-4.6 | GPT-5 Mini |
|---|---|---|
| Vectara Hallucination Rate | 9.5% | 12.9% |
| LMArena Expert | 1431 | 1379 |
| GPQA Diamond | — | 75% |
| Humanity's Last Exam | — | 19.4% |
| SimpleQA Verified | — | 21.6% |
| MMLU-Pro | — | 83.5% |
| Confabulations | — | 13.3% |
| GPQA (HELM) | — | 75.6% |
Multimodal Not comparable
GLM-4.6: —, GPT-5 Mini: 35.6 (#85)
| Benchmark | GLM-4.6 | GPT-5 Mini |
|---|---|---|
| LMArena Vision | — | 1202 |
| VPCT | — | 40.2% |
Multilingual GLM-4.6 leads
GLM-4.6: 53.5 (#66), GPT-5 Mini: 48.9 (#137)
| Benchmark | GLM-4.6 | GPT-5 Mini |
|---|---|---|
| LMArena Non-English | 1426 | 1363 |
| LMArena Chinese | 1499 | 1385 |
| LMArena French | 1459 | 1386 |
| LMArena German | 1447 | 1366 |
| LMArena Japanese | 1393 | 1341 |
| LMArena Korean | 1400 | 1308 |
| LMArena Russian | 1419 | 1362 |
| LMArena Spanish | 1436 | 1355 |
Instruction Following GPT-5 Mini leads
GLM-4.6: 74.3 (#98), GPT-5 Mini: 76.2 (#46)
| Benchmark | GLM-4.6 | GPT-5 Mini |
|---|---|---|
| LMArena Instruction Following | 1410 | 1357 |
| IFEval | — | 92.7% |
Long Context GLM-4.6 leads
GLM-4.6: 43.4 (#94), GPT-5 Mini: 41.9 (#132)
| Benchmark | GLM-4.6 | GPT-5 Mini |
|---|---|---|
| LMArena Longer Query | 1422 | 1355 |
| Fiction.LiveBench | — | 69.4% |
Writing & Preference GLM-4.6 leads
GLM-4.6: 61.1 (#90), GPT-5 Mini: 55.2 (#148)
| Benchmark | GLM-4.6 | GPT-5 Mini |
|---|---|---|
| LMArena Text | 1440 | 1373 |
| LMArena Creative Writing | 1411 | 1325 |
| EQ-Bench Creative Writing | 1411 | 1313 |
| LMArena Multi-Turn | 1427 | 1363 |
| Short-Story Creative Writing | — | 83.1% |
| WildBench | — | 85.5% |
Frequently asked questions
Is GLM-4.6 better than GPT-5 Mini?
GLM-4.6 and GPT-5 Mini score almost the same on the Noometry Index (41.4 vs 41.8), so choose on price, context window or the category you care about most.
Which is cheaper, GLM-4.6 or GPT-5 Mini?
GPT-5 Mini is cheaper. It lists at $0.25 per million input tokens and $2 per million output tokens; GLM-4.6 lists at $0.60 and $2.20.
Is GLM-4.6 or GPT-5 Mini better for coding?
They score almost the same on coding (40.1 vs 40.1); test both on your own repository before choosing.
Which has the bigger context window?
GPT-5 Mini does, with 400K tokens against 205K.
How many benchmarks do GLM-4.6 and GPT-5 Mini share?
28 benchmarks have published results for both models. GLM-4.6 has 29 scored results on Noometry and GPT-5 Mini has 60.