Model comparison
GLM-4.7-Flash vs GPT-5
GPT-5 is the stronger model overall, scoring 50.9 to 38.8 on the Noometry Index. GLM-4.7-Flash costs 24× less per token, which makes it the better buy when GPT-5'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 in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in long context, where GPT-5 leads 69.5 to 40.9.
- The biggest single-benchmark swing is Chess Puzzles: 0% for GLM-4.7-Flash and 37% for GPT-5.
- GLM-4.7-Flash is cheaper at $0.06 / $0.40 per million input/output tokens, against $1.25 / $10 for GPT-5.
- GPT-5 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 | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 38.8 | 50.9 |
| Released | 2026-01-19 | 2025-08-07 |
| Weights | Open | Proprietary |
| Context window | 200K | 400K |
| Max output | 131K | 128K |
| Input $ / M tokens | $0.06 | $1.25 |
| Output $ / M tokens | $0.40 | $10 |
| Results tracked | 21 | 69 |
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Category by category
Coding GPT-5 leads
GLM-4.7-Flash: 40.6 (#135), GPT-5: 50.3 (#47)
| Benchmark | GLM-4.7-Flash | GPT-5 |
|---|---|---|
| LMArena Coding | 1383 | 1436 |
| SWE-bench Verified | — | 73.6% |
| SWE-bench Verified (bash only) | — | 65% |
| Aider Polyglot | — | 88% |
| LMArena WebDev | — | 1418 |
| SciCode | — | 42.9% |
| GSO | — | 6.9% |
| WeirdML | — | 60.7% |
| ALE-Bench | — | 1,162 |
| AlgoTune | — | 1.67 |
Agentic & Tool Use Not comparable
GLM-4.7-Flash: —, GPT-5: 33.1 (#56)
| Benchmark | GLM-4.7-Flash | 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-4.7-Flash: 20.9 (#229), GPT-5: 38.3 (#64)
| Benchmark | GLM-4.7-Flash | GPT-5 |
|---|---|---|
| Chess Puzzles | 0% | 37% |
| LMArena Hard Prompts | 1356 | 1416 |
| ARC-AGI-2 | — | 9.9% |
| SimpleBench | — | 56.7% |
| Kagi LLM Benchmark | — | 72.7% |
| ARC-AGI-1 | — | 65.7% |
| CritPt | — | 12.6% |
| 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-4.7-Flash: 36.1 (#173), GPT-5: 55.0 (#44)
| Benchmark | GLM-4.7-Flash | GPT-5 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 58.3% | 91.4% |
| LMArena Math | 1355 | 1407 |
| FrontierMath (Tiers 1-3) | — | 55.4% |
| FrontierMath Tier 4 | — | 22% |
| 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-4.7-Flash: 35.5 (#184), GPT-5: 56.6 (#43)
| Benchmark | GLM-4.7-Flash | GPT-5 |
|---|---|---|
| GPQA Diamond | 60.5% | 86.2% |
| Vectara Hallucination Rate | 9.3% | 14.7% |
| LMArena Expert | 1357 | 1419 |
| Humanity's Last Exam | — | 25.3% |
| SimpleQA Verified | — | 50.1% |
| MMLU-Pro | — | 86.3% |
| Confabulations | — | 10.3% |
| GPQA (HELM) | — | 79.2% |
Multimodal Not comparable
GLM-4.7-Flash: —, GPT-5: 46.8 (#13)
| Benchmark | GLM-4.7-Flash | GPT-5 |
|---|---|---|
| LMArena Vision | — | 1232 |
| GeoBench | — | 81% |
| VPCT | — | 66% |
Multilingual GPT-5 leads
GLM-4.7-Flash: 46.5 (#158), GPT-5: 51.4 (#110)
| Benchmark | GLM-4.7-Flash | GPT-5 |
|---|---|---|
| LMArena Non-English | 1330 | 1397 |
| LMArena Chinese | 1403 | 1422 |
| LMArena French | 1332 | 1410 |
| LMArena German | 1337 | 1416 |
| LMArena Korean | 1283 | 1360 |
| LMArena Russian | 1332 | 1406 |
| LMArena Spanish | 1350 | 1399 |
| LMArena Japanese | — | 1409 |
Instruction Following GPT-5 leads
GLM-4.7-Flash: 70.1 (#167), GPT-5: 73.8 (#113)
| Benchmark | GLM-4.7-Flash | GPT-5 |
|---|---|---|
| LMArena Instruction Following | 1327 | 1388 |
| IFEval | — | 87.5% |
Long Context GPT-5 leads
GLM-4.7-Flash: 40.9 (#148), GPT-5: 69.5 (#2)
| Benchmark | GLM-4.7-Flash | GPT-5 |
|---|---|---|
| LMArena Longer Query | 1345 | 1399 |
| Fiction.LiveBench | — | 97.2% |
Writing & Preference GPT-5 leads
GLM-4.7-Flash: 47.4 (#210), GPT-5: 63.4 (#65)
| Benchmark | GLM-4.7-Flash | GPT-5 |
|---|---|---|
| LMArena Text | 1351 | 1406 |
| LMArena Creative Writing | 1297 | 1365 |
| EQ-Bench Creative Writing | 1125 | 1627 |
| LMArena Multi-Turn | 1342 | 1426 |
| Short-Story Creative Writing | — | 86% |
| WildBench | — | 85.7% |
Frequently asked questions
Is GLM-4.7-Flash better than GPT-5?
GPT-5 is the stronger model overall, scoring 50.9 to 38.8 on the Noometry Index. GLM-4.7-Flash costs 24× less per token, which makes it the better buy when GPT-5's lead doesn't matter for your workload.
Which is cheaper, GLM-4.7-Flash or GPT-5?
GLM-4.7-Flash is cheaper. It lists at $0.06 per million input tokens and $0.40 per million output tokens; GPT-5 lists at $1.25 and $10.
Is GLM-4.7-Flash or GPT-5 better for coding?
GPT-5 scores higher on coding benchmarks: 50.3 versus 40.6 in the Noometry coding category.
Which has the bigger context window?
GPT-5 does, with 400K tokens against 200K.
How many benchmarks do GLM-4.7-Flash and GPT-5 share?
21 benchmarks have published results for both models. GLM-4.7-Flash has 21 scored results on Noometry and GPT-5 has 69.