Model comparison
GLM-4.7-Flash vs GPT-5.4 mini
GPT-5.4 mini is the stronger model overall, scoring 45.0 to 38.8 on the Noometry Index. GLM-4.7-Flash costs 12× less per token, which makes it the better buy when GPT-5.4 mini's lead doesn't matter for your workload.
Last verified . 21 shared benchmarks.
Summary
- They share 21 benchmarks with published results for both. GLM-4.7-Flash scores higher in 0 categories and GPT-5.4 mini in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where GPT-5.4 mini leads 64.0 to 47.4.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 58.3% for GLM-4.7-Flash and 88.9% for GPT-5.4 mini.
- GLM-4.7-Flash is cheaper at $0.06 / $0.40 per million input/output tokens, against $0.75 / $4.50 for GPT-5.4 mini.
- GPT-5.4 mini accepts more context: 400K tokens versus 200K.
- GLM-4.7-Flash has downloadable open weights; the other is API-only.
Side by side
| GLM-4.7-Flash | GPT-5.4 mini | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 38.8 | 45.0 |
| Released | 2026-01-19 | 2026-03-17 |
| Weights | Open | Proprietary |
| Context window | 200K | 400K |
| Max output | 131K | 128K |
| Input $ / M tokens | $0.06 | $0.75 |
| Output $ / M tokens | $0.40 | $4.50 |
| Results tracked | 21 | 46 |
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Category by category
Coding GPT-5.4 mini leads
GLM-4.7-Flash: 40.6 (#135), GPT-5.4 mini: 45.2 (#72)
| Benchmark | GLM-4.7-Flash | GPT-5.4 mini |
|---|---|---|
| LMArena Coding | 1383 | 1438 |
| FrontierCode | — | 27% |
| LMArena WebDev | — | 1397 |
| SciCode | — | 49.9% |
| WeirdML | — | 60.3% |
| ALE-Bench | — | 1,189 |
Agentic & Tool Use Not comparable
GLM-4.7-Flash: —, GPT-5.4 mini: 29.9 (#81)
| Benchmark | GLM-4.7-Flash | GPT-5.4 mini |
|---|---|---|
| DeepResearch Bench | — | 36.3% |
Reasoning GPT-5.4 mini leads
GLM-4.7-Flash: 20.9 (#229), GPT-5.4 mini: 30.4 (#85)
| Benchmark | GLM-4.7-Flash | GPT-5.4 mini |
|---|---|---|
| Chess Puzzles | 0% | 24% |
| LMArena Hard Prompts | 1356 | 1424 |
| ARC-AGI-2 | — | 18.9% |
| Kagi LLM Benchmark | — | 37.9% |
| NYT Connections (extended) | — | 61.8% |
| ARC-AGI-1 | — | 63.7% |
| CritPt | — | 10% |
| Thematic Generalization | — | 61.7% |
| Mystery Game Puzzles | — | 11% |
| DTBench | — | 80% |
| LMCA | — | 40.8% |
| Epoch Capabilities Index | — | 148.84 |
| ForecastBench | — | 57 |
Math GPT-5.4 mini leads
GLM-4.7-Flash: 36.1 (#173), GPT-5.4 mini: 45.5 (#75)
| Benchmark | GLM-4.7-Flash | GPT-5.4 mini |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 58.3% | 88.9% |
| LMArena Math | 1355 | 1419 |
| FrontierMath (Tiers 1-3) | — | 51.2% |
| FrontierMath Tier 4 | — | 9.8% |
| ProofBench | — | 21% |
| FrontierMath (Feb 2025 set) | — | 28.3% |
| FrontierMath Tier 4 (v1) | — | 2.1% |
Knowledge GPT-5.4 mini leads
GLM-4.7-Flash: 35.5 (#184), GPT-5.4 mini: 51.5 (#67)
| Benchmark | GLM-4.7-Flash | GPT-5.4 mini |
|---|---|---|
| GPQA Diamond | 60.5% | 86.9% |
| Vectara Hallucination Rate | 9.3% | 5.5% |
| LMArena Expert | 1357 | 1435 |
| SimpleQA Verified | — | 29.4% |
Multimodal Not comparable
GLM-4.7-Flash: —, GPT-5.4 mini: 39.7 (#56)
| Benchmark | GLM-4.7-Flash | GPT-5.4 mini |
|---|---|---|
| LMArena Vision | — | 1245 |
Multilingual GPT-5.4 mini leads
GLM-4.7-Flash: 46.5 (#158), GPT-5.4 mini: 51.9 (#96)
| Benchmark | GLM-4.7-Flash | GPT-5.4 mini |
|---|---|---|
| LMArena Non-English | 1330 | 1405 |
| LMArena Chinese | 1403 | 1446 |
| LMArena French | 1332 | 1440 |
| LMArena German | 1337 | 1409 |
| LMArena Korean | 1283 | 1368 |
| LMArena Russian | 1332 | 1417 |
| LMArena Spanish | 1350 | 1405 |
| LMArena Japanese | — | 1374 |
Instruction Following GPT-5.4 mini leads
GLM-4.7-Flash: 70.1 (#167), GPT-5.4 mini: 74.1 (#102)
| Benchmark | GLM-4.7-Flash | GPT-5.4 mini |
|---|---|---|
| LMArena Instruction Following | 1327 | 1405 |
Long Context GPT-5.4 mini leads
GLM-4.7-Flash: 40.9 (#148), GPT-5.4 mini: 43.0 (#112)
| Benchmark | GLM-4.7-Flash | GPT-5.4 mini |
|---|---|---|
| LMArena Longer Query | 1345 | 1407 |
Writing & Preference GPT-5.4 mini leads
GLM-4.7-Flash: 47.4 (#210), GPT-5.4 mini: 64.0 (#58)
| Benchmark | GLM-4.7-Flash | GPT-5.4 mini |
|---|---|---|
| LMArena Text | 1351 | 1412 |
| LMArena Creative Writing | 1297 | 1370 |
| EQ-Bench Creative Writing | 1125 | 1665 |
| LMArena Multi-Turn | 1342 | 1429 |
Frequently asked questions
Is GLM-4.7-Flash better than GPT-5.4 mini?
GPT-5.4 mini is the stronger model overall, scoring 45.0 to 38.8 on the Noometry Index. GLM-4.7-Flash costs 12× less per token, which makes it the better buy when GPT-5.4 mini's lead doesn't matter for your workload.
Which is cheaper, GLM-4.7-Flash or GPT-5.4 mini?
GLM-4.7-Flash is cheaper. It lists at $0.06 per million input tokens and $0.40 per million output tokens; GPT-5.4 mini lists at $0.75 and $4.50.
Is GLM-4.7-Flash or GPT-5.4 mini better for coding?
GPT-5.4 mini scores higher on coding benchmarks: 45.2 versus 40.6 in the Noometry coding category.
Which has the bigger context window?
GPT-5.4 mini does, with 400K tokens against 200K.
How many benchmarks do GLM-4.7-Flash and GPT-5.4 mini share?
21 benchmarks have published results for both models. GLM-4.7-Flash has 21 scored results on Noometry and GPT-5.4 mini has 46.