Model comparison
GLM-4.7 vs GPT-4o mini
GLM-4.7 is the stronger model overall, scoring 42.0 to 25.5 on the Noometry Index. GPT-4o mini costs 3.8× less per token, which makes it the better buy when GLM-4.7's lead doesn't matter for your workload.
Last verified . 24 shared benchmarks.
Summary
- They share 24 benchmarks with published results for both. GLM-4.7 scores higher in 8 categories and GPT-4o mini in 1 category; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GLM-4.7 leads 47.0 to 17.7.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 83.3% for GLM-4.7 and 6.9% for GPT-4o mini.
- GPT-4o mini is cheaper at $0.15 / $0.60 per million input/output tokens, against $0.60 / $2.20 for GLM-4.7.
- GLM-4.7 accepts more context: 205K tokens versus 128K.
- GLM-4.7 has downloadable open weights; the other is API-only.
Side by side
| GLM-4.7 | GPT-4o mini | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 42.0 | 25.5 |
| Released | 2025-12-22 | 2024-07-18 |
| Weights | Open | Proprietary |
| Context window | 205K | 128K |
| Max output | 131K | 16K |
| Input $ / M tokens | $0.60 | $0.15 |
| Output $ / M tokens | $2.20 | $0.60 |
| Results tracked | 36 | 60 |
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Category by category
Coding GLM-4.7 leads
GLM-4.7: 44.0 (#79), GPT-4o mini: 22.0 (#335)
| Benchmark | GLM-4.7 | GPT-4o mini |
|---|---|---|
| LMArena Coding | 1454 | 1290 |
| Aider Polyglot | — | 3.6% |
| LMArena WebDev | 1435 | — |
| SciCode | 45.1% | — |
| WeirdML | — | 11.8% |
| BigCodeBench Instruct | — | 46.1% |
| LiveBench Coding | — | 43.1% |
| BigCodeBench Complete | — | 57.4% |
| ALE-Bench | 399.48 | — |
| HumanEval+ | — | 83.5% |
| MBPP+ | — | 72.2% |
Agentic & Tool Use Too close to call
GLM-4.7: 26.5 (#103), GPT-4o mini: 27.5 (#101)
| Benchmark | GLM-4.7 | GPT-4o mini |
|---|---|---|
| Terminal-Bench | 33.4% | — |
| BALROG | — | 17.4% |
| Vending-Bench 2 | 2,377 | — |
Reasoning GLM-4.7 leads
GLM-4.7: 24.3 (#164), GPT-4o mini: 8.7 (#347)
| Benchmark | GLM-4.7 | GPT-4o mini |
|---|---|---|
| SimpleBench | 47.7% | 10.7% |
| Chess Puzzles | 6% | 0% |
| LMArena Hard Prompts | 1443 | 1267 |
| Epoch Capabilities Index | 143.51 | 126.56 |
| ARC-AGI-2 | — | 0% |
| Kagi LLM Benchmark | — | 28.8% |
| CritPt | 1.7% | — |
| LiveBench Reasoning | — | 32.8% |
| Mystery Game Puzzles | — | 12% |
| DTBench | — | 54.4% |
| LiveBench Data Analysis | — | 50% |
| LMCA | — | 10.4% |
| LiveBench | — | 41.3% |
| PIQA | — | 88.7% |
Math GLM-4.7 leads
GLM-4.7: 38.6 (#135), GPT-4o mini: 10.4 (#314)
| Benchmark | GLM-4.7 | GPT-4o mini |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 83.3% | 6.9% |
| LMArena Math | 1423 | 1267 |
| FrontierMath (Tiers 1-3) | — | 0.7% |
| ProofBench | 6% | — |
| Omni-MATH | — | 28% |
| LiveBench Math | — | 36.3% |
| MATH Level 5 | — | 52.6% |
| FrontierMath (Feb 2025 set) | 2.4% | — |
| FrontierMath Tier 4 (v1) | 0% | — |
| GSM8K | — | 91.3% |
Knowledge GLM-4.7 leads
GLM-4.7: 47.0 (#80), GPT-4o mini: 17.7 (#284)
| Benchmark | GLM-4.7 | GPT-4o mini |
|---|---|---|
| GPQA Diamond | 83.3% | 37.7% |
| SimpleQA Verified | 32.2% | 8.3% |
| LMArena Expert | 1424 | 1235 |
| MMLU-Pro | — | 60.3% |
| Confabulations | — | 37.2% |
| Vectara Hallucination Rate | 11.7% | — |
| GPQA (HELM) | — | 36.8% |
| BoolQ | — | 88.7% |
| MMLU | — | 81.8% |
Multimodal Not comparable
GLM-4.7: —, GPT-4o mini: 25.9 (#122)
| Benchmark | GLM-4.7 | GPT-4o mini |
|---|---|---|
| LMArena Vision | — | 1066 |
| Video-MME | — | 64.8% |
| GeoBench | — | 64% |
| VPCT | — | 34% |
Multilingual GLM-4.7 leads
GLM-4.7: 52.8 (#79), GPT-4o mini: 42.0 (#199)
| Benchmark | GLM-4.7 | GPT-4o mini |
|---|---|---|
| LMArena Non-English | 1417 | 1266 |
| LMArena Chinese | 1495 | 1265 |
| LMArena French | 1432 | 1297 |
| LMArena German | 1424 | 1272 |
| LMArena Japanese | 1439 | 1216 |
| LMArena Korean | 1399 | 1195 |
| LMArena Russian | 1423 | 1275 |
| LMArena Spanish | 1434 | 1276 |
Instruction Following GLM-4.7 leads
GLM-4.7: 74.4 (#95), GPT-4o mini: 61.9 (#239)
| Benchmark | GLM-4.7 | GPT-4o mini |
|---|---|---|
| LMArena Instruction Following | 1411 | 1258 |
| LiveBench Instruction Following | — | 56.8% |
| IFEval | — | 78.2% |
Long Context GLM-4.7 leads
GLM-4.7: 42.8 (#116), GPT-4o mini: 39.1 (#186)
| Benchmark | GLM-4.7 | GPT-4o mini |
|---|---|---|
| LMArena Longer Query | 1432 | 1289 |
| CL-bench | 15.9% | — |
| CL-bench Life | 10.9% | — |
Writing & Preference GLM-4.7 leads
GLM-4.7: 60.9 (#93), GPT-4o mini: 39.5 (#248)
| Benchmark | GLM-4.7 | GPT-4o mini |
|---|---|---|
| LMArena Text | 1435 | 1286 |
| LMArena Creative Writing | 1401 | 1268 |
| EQ-Bench Creative Writing | 1413 | 873 |
| LMArena Multi-Turn | 1446 | 1285 |
| Short-Story Creative Writing | — | 67.2% |
| WildBench | — | 79.1% |
| LiveBench Language | — | 28.6% |
Frequently asked questions
Is GLM-4.7 better than GPT-4o mini?
GLM-4.7 is the stronger model overall, scoring 42.0 to 25.5 on the Noometry Index. GPT-4o mini costs 3.8× less per token, which makes it the better buy when GLM-4.7's lead doesn't matter for your workload.
Which is cheaper, GLM-4.7 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-4.7 lists at $0.60 and $2.20.
Is GLM-4.7 or GPT-4o mini better for coding?
GLM-4.7 scores higher on coding benchmarks: 44.0 versus 22.0 in the Noometry coding category.
Which has the bigger context window?
GLM-4.7 does, with 205K tokens against 128K.
How many benchmarks do GLM-4.7 and GPT-4o mini share?
24 benchmarks have published results for both models. GLM-4.7 has 36 scored results on Noometry and GPT-4o mini has 60.