Model comparison
GLM-4.7-Flash vs GPT-5.4
GPT-5.4 is the stronger model overall, scoring 59.5 to 38.4 on the Noometry Index. GLM-4.7-Flash costs 39× less per token, which makes it the better buy when GPT-5.4'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 in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-5.4 leads 61.7 to 20.2.
- The biggest single-benchmark swing is Chess Puzzles: 0% for GLM-4.7-Flash and 44% for GPT-5.4.
- GLM-4.7-Flash is cheaper at $0.06 / $0.40 per million input/output tokens, against $2.50 / $15 for GPT-5.4.
- GPT-5.4 accepts more context: 1.05M 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 | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 38.4 | 59.5 |
| Released | 2026-01-19 | 2026-03-05 |
| Weights | Open | Proprietary |
| Context window | 200K | 1.05M |
| Max output | 131K | 128K |
| Input $ / M tokens | $0.06 | $2.50 |
| Output $ / M tokens | $0.40 | $15 |
| Results tracked | 21 | 68 |
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Category by category
Coding GPT-5.4 leads
GLM-4.7-Flash: 40.0 (#133), GPT-5.4: 52.8 (#28)
| Benchmark | GLM-4.7-Flash | GPT-5.4 |
|---|---|---|
| LMArena Coding | 1383 | 1497 |
| SWE-bench Verified | — | 76.9% |
| DeepSWE | — | 51.8% |
| LMArena WebDev | — | 1465 |
| SciCode | — | 56.6% |
| GSO | — | 31.4% |
| WeirdML | — | 77.7% |
| MirrorCode | — | 15.6% |
| ALE-Bench | — | 1,607 |
| AlgoTune | — | 1.85 |
Agentic & Tool Use Not comparable
GLM-4.7-Flash: —, GPT-5.4: 46.5 (#12)
| Benchmark | GLM-4.7-Flash | GPT-5.4 |
|---|---|---|
| Terminal-Bench | — | 81.8% |
| APEX-Agents | — | 52.4% |
| τ²-bench Banking | — | 39.4% |
| DeepResearch Bench | — | 35.1% |
| PostTrainBench | — | 19% |
| GBAEval | — | 45.1% |
| LMArena Search | — | 1197 |
| METR Time Horizons | — | 74.3% |
| Vending-Bench 2 | — | 6,144 |
Reasoning GPT-5.4 leads
GLM-4.7-Flash: 20.2 (#224), GPT-5.4: 61.7 (#17)
| Benchmark | GLM-4.7-Flash | GPT-5.4 |
|---|---|---|
| Chess Puzzles | 0% | 44% |
| LMArena Hard Prompts | 1356 | 1485 |
| ARC-AGI-2 | — | 74% |
| Kagi LLM Benchmark | — | 63.8% |
| NYT Connections (extended) | — | 91.3% |
| ARC-AGI-1 | — | 93.7% |
| CritPt | — | 23.4% |
| EnigmaEval | — | 16% |
| Thematic Generalization | — | 80% |
| EBR-Bench | — | 25.4% |
| Mystery Game Puzzles | — | 37% |
| DTBench | — | 94.4% |
| LMCA | — | 52% |
| Epoch Capabilities Index | — | 156.92 |
| ForecastBench | — | 59.5 |
Math GPT-5.4 leads
GLM-4.7-Flash: 34.4 (#159), GPT-5.4: 73.3 (#14)
| Benchmark | GLM-4.7-Flash | GPT-5.4 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 58.3% | 97.8% |
| LMArena Math | 1354 | 1488 |
| FrontierMath (Tiers 1-3) | — | 78.6% |
| FrontierMath Tier 4 | — | 49% |
| MathArena Final-Answer Competitions | — | 83.1% |
| ProofBench | — | 56% |
| FrontierMath (Feb 2025 set) | — | 47.6% |
| FrontierMath Tier 4 (v1) | — | 27.1% |
Knowledge GPT-5.4 leads
GLM-4.7-Flash: 35.4 (#177), GPT-5.4: 65.3 (#13)
| Benchmark | GLM-4.7-Flash | GPT-5.4 |
|---|---|---|
| GPQA Diamond | 60.5% | 93.3% |
| Vectara Hallucination Rate | 9.3% | 7% |
| LMArena Expert | 1357 | 1507 |
| Humanity's Last Exam | — | 36.2% |
| SimpleQA Verified | — | 45.1% |
Multimodal Not comparable
GLM-4.7-Flash: —, GPT-5.4: 43.8 (#19)
| Benchmark | GLM-4.7-Flash | GPT-5.4 |
|---|---|---|
| LMArena Vision | — | 1302 |
| Blueprint-Bench 2 | — | 27.1% |
| Furniture Assembly | — | 37.5% |
| LMArena Document | — | 1471 |
Multilingual GPT-5.4 leads
GLM-4.7-Flash: 46.8 (#152), GPT-5.4: 56.4 (#22)
| Benchmark | GLM-4.7-Flash | GPT-5.4 |
|---|---|---|
| LMArena Non-English | 1331 | 1465 |
| LMArena Chinese | 1403 | 1520 |
| LMArena French | 1332 | 1493 |
| LMArena German | 1336 | 1472 |
| LMArena Korean | 1284 | 1449 |
| LMArena Russian | 1333 | 1480 |
| LMArena Spanish | 1351 | 1453 |
| LMArena Japanese | — | 1484 |
Instruction Following GPT-5.4 leads
GLM-4.7-Flash: 70.3 (#162), GPT-5.4: 77.3 (#25)
| Benchmark | GLM-4.7-Flash | GPT-5.4 |
|---|---|---|
| LMArena Instruction Following | 1327 | 1469 |
Long Context GPT-5.4 leads
GLM-4.7-Flash: 40.9 (#143), GPT-5.4: 50.3 (#8)
| Benchmark | GLM-4.7-Flash | GPT-5.4 |
|---|---|---|
| LMArena Longer Query | 1345 | 1474 |
| CL-bench | — | 27.9% |
| CL-bench Life | — | 21.7% |
Writing & Preference GPT-5.4 leads
GLM-4.7-Flash: 47.7 (#206), GPT-5.4: 72.1 (#18)
| Benchmark | GLM-4.7-Flash | GPT-5.4 |
|---|---|---|
| LMArena Text | 1351 | 1469 |
| LMArena Creative Writing | 1297 | 1439 |
| EQ-Bench Creative Writing | 1125 | 1840 |
| LMArena Multi-Turn | 1342 | 1482 |
| EQ-Bench 4 | — | 1272 |
Frequently asked questions
Is GLM-4.7-Flash better than GPT-5.4?
GPT-5.4 is the stronger model overall, scoring 59.5 to 38.4 on the Noometry Index. GLM-4.7-Flash costs 39× less per token, which makes it the better buy when GPT-5.4's lead doesn't matter for your workload.
Which is cheaper, GLM-4.7-Flash or GPT-5.4?
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 lists at $2.50 and $15.
Is GLM-4.7-Flash or GPT-5.4 better for coding?
GPT-5.4 scores higher on coding benchmarks: 52.8 versus 40.0 in the Noometry coding category.
Which has the bigger context window?
GPT-5.4 does, with 1.05M tokens against 200K.
How many benchmarks do GLM-4.7-Flash and GPT-5.4 share?
21 benchmarks have published results for both models. GLM-4.7-Flash has 21 scored results on Noometry and GPT-5.4 has 68.