GLM 5 vs GPT-5
Updated October 21, 2026. 66 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick GPT-5 for coding: it scores 72.2 on the Vibe Coding Index against 59.7 for GLM 5.
Pick GLM 5 when output tokens cost 3.1x less, or when what matters is LMArena WebDev or SWE-bench Verified (Vals AI), where it leads.
- Shared results
- 66
- GLM 5 only
- 9
- GPT-5 only
- 43
- Like-for-like categories
- 8 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, four public coding benchmarks
GPT-5
GPT-5 scores 72.2 against 59.7.
2 and 3 benchmarks behind the scores
Intelligence
AA Intelligence Index
GLM 5
GLM 5 leads, 27.9 against 23.0.
Same board, both measured
Reasoning
GPQA Diamond
GPT-5
GPT-5 leads, 85.6% against 83.3%.
Same board, both measured
Math
AIME
GPT-5
GPT-5 leads, 93.4% against 91.7%.
Same board, both measured
Knowledge
MMLU Pro
GPT-5
GPT-5 leads, 86.5% against 86.0%.
Same board, both measured
Web design
Design Arena: websites
GLM 5
GLM 5 leads, 1247 against 1185.
Same board, both measured
Long documents
Listed context window
GPT-5
GPT-5 takes 400K tokens against 205K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 5
GLM 5 $0.0026, GPT-5 $0.0063 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GPT-5
GLM 5 $0.63, GPT-5 $0.39.
Published attempt cost and pass rate
At a glance
- Coding index
- GLM 5 59.7 from 2 benchmarks, GPT-5 72.2 from 3 benchmarks.
- Widest category
- Agentic work, 11.7 points apart on average over 1 shared benchmark.
- Widest single gap
- Design Arena: 3D: GLM 5 1229 against GPT-5 1056, 21.6 points apart on a 0 to 100 scale.
- Output price
- GLM 5 $3.20 against GPT-5 $10.00 per million output tokens, 3.1x apart.
- Context window
- GLM 5 takes 205K tokens, GPT-5 400K.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
| Category | GLM 5 | Bars | GPT-5 | Ties |
|---|---|---|---|---|
| Coding | 8 | 4 | ||
| Intelligence | 1 | 1 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 5 | 5 | ||
| Math | 2 | 2 | ||
| Knowledge | 5 | 5 | ||
| Web design | 7 | 1 | ||
| General | 17 | 2 | ||
| All shared boards | 46 | 20 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index84
| Benchmark | GLM 5 | GPT-5 | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 88.0% | Only GPT-5 measured |
| SWE-bench Verified | 72.8% | 74.4% | GPT-5 |
| SWE-bench Multilingual | 69.7% | Not measured | Only GLM 5 measured |
| LMArena WebDev | 1435 | 1418 | GLM 5 |
| AA Coding Index | Not measured | 37.8 | Only GPT-5 measured |
| LiveCodeBench (Vals AI) | 81.9% | 85.9% | GPT-5 |
| SWE-bench Verified (Vals AI) | 71.4% | 69.0% | GLM 5 |
| Vibe Code Bench (Vals AI) | 23.4% | 20.1% | GLM 5 |
| LiveCodeBench (Artificial Analysis run) | Not measured | 84.6% | Only GPT-5 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 43.2% | 37.9% | GLM 5 |
| Terminal-Bench | 52.4% | 49.6% | GLM 5 |
| GSO-Bench | Not measured | 6.9% | Only GPT-5 measured |
| WeirdML | 48.2% | 60.7% | GPT-5 |
| ALE-Bench | 765.6 | 807.6 | GPT-5 |
| LMArena Text: coding | 1461 | 1436 | GLM 5 |
| LMArena Text: software and IT | 1460 | 1420 | GLM 5 |
| LMArena WebDev: React | 1424 | Not measured | Only GLM 5 measured |
| LMArena WebDev: HTML | 1450 | 1427 | GLM 5 |
| Neither model has a result on the other 20 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | GLM 5 | GPT-5 | Leader |
|---|---|---|---|
| AA Intelligence Index | 27.9 | 23.0 | GLM 5 |
| Epoch Capabilities Index | 145.8 | 150.0 | GPT-5 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 5 | GPT-5 | Leader |
|---|---|---|---|
| Design Arena: agents building full-stack apps | 1123 | Not measured | Only GLM 5 measured |
| Design Arena: agents building mobile apps | 1136 | Not measured | Only GLM 5 measured |
| Design Arena: agents building Android apps | 1152 | Not measured | Only GLM 5 measured |
| Design Arena: agents building Godot games | 1144 | Not measured | Only GLM 5 measured |
| Design Arena: HTML slides | 1148 | Not measured | Only GLM 5 measured |
| τ²-Bench Telecom | 98.2% | 86.5% | GLM 5 |
| METR time horizons | Not measured | 69.6% | Only GPT-5 measured |
| Remote Labor Index | Not measured | 1.7% | Only GPT-5 measured |
| GDPval | Not measured | 34.8% | Only GPT-5 measured |
| BALROG | Not measured | 32.8% | Only GPT-5 measured |
| DeepResearch Bench | Not measured | 49.6% | Only GPT-5 measured |
| Vending-Bench 2 | $4,432 | Not measured | Only GLM 5 measured |
| Neither model has a result on the other 27 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index55
| Benchmark | GLM 5 | GPT-5 | Leader |
|---|---|---|---|
| GPQA Diamond | 83.3% | 85.6% | GPT-5 |
| MMMU Pro (Vals AI) | Not measured | 81.5% | Only GPT-5 measured |
| GPQA Diamond (Artificial Analysis run) | 82.0% | 85.4% | GPT-5 |
| Humanity's Last Exam (Artificial Analysis run) | 29.3% | 28.5% | GLM 5 |
| GPQA Diamond (Epoch AI run) | 87.8% | 86.2% | GLM 5 |
| ARC-AGI | 44.7% | 65.7% | GPT-5 |
| ARC-AGI-2 | 4.9% | 9.9% | GPT-5 |
| SimpleBench | 53.2% | 56.7% | GPT-5 |
| Chess puzzles | Not measured | 36.0% | Only GPT-5 measured |
| Mystery game puzzles | Not measured | 23.0% | Only GPT-5 measured |
| EBR-bench | Not measured | 12.7% | Only GPT-5 measured |
| VPCT | Not measured | 66.0% | Only GPT-5 measured |
| LMArena Text: hard prompts | 1452 | 1415 | GLM 5 |
| LMArena Text: expert | 1454 | 1419 | GLM 5 |
| LMArena Text: hard prompts in English | 1460 | 1419 | GLM 5 |
| LMArena Vision: diagrams | Not measured | 1252 | Only GPT-5 measured |
| LMArena Vision: homework | Not measured | 1278 | Only GPT-5 measured |
| Neither model has a result on the other 4 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index22
| Benchmark | GLM 5 | GPT-5 | Leader |
|---|---|---|---|
| AIME | 91.7% | 93.4% | GPT-5 |
| MATH 500 (Vals AI) | Not measured | 96.0% | Only GPT-5 measured |
| MGSM (Vals AI) | Not measured | 92.8% | Only GPT-5 measured |
| AA Math Index | Not measured | 94.3 | Only GPT-5 measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 94.3% | Only GPT-5 measured |
| OTIS Mock AIME | 80.0% | 91.4% | GPT-5 |
| FrontierMath, tiers 1 to 3 | Not measured | 55.4% | Only GPT-5 measured |
| FrontierMath, tier 4 | Not measured | 22.0% | Only GPT-5 measured |
| ProofBench | Not measured | 18.0% | Only GPT-5 measured |
| MATH level 5 | Not measured | 98.1% | Only GPT-5 measured |
| LMArena Text: math | 1440 | 1407 | GLM 5 |
| LMArena Text: mathematical work | 1451 | 1405 | GLM 5 |
| Neither model has a result on the other 1 board here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index55
| Benchmark | GLM 5 | GPT-5 | Leader |
|---|---|---|---|
| MMLU Pro | 86.0% | 86.5% | GPT-5 |
| LegalBench (Vals AI) | 84.1% | 86.0% | GPT-5 |
| CaseLaw (Vals AI) | 52.5% | 66.5% | GPT-5 |
| CorpFin (Vals AI) | 62.9% | 61.1% | GLM 5 |
| TaxEval (Vals AI) | 70.0% | 73.4% | GPT-5 |
| MortgageTax (Vals AI) | Not measured | 65.5% | Only GPT-5 measured |
| MedQA (Vals AI) | 94.3% | 96.3% | GPT-5 |
| MedCode (Vals AI) | Not measured | 49.6% | Only GPT-5 measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 87.1% | Only GPT-5 measured |
| SimpleQA Verified | Not measured | 50.1% | Only GPT-5 measured |
| GeoBench | Not measured | 81.0% | Only GPT-5 measured |
| LMArena Text: science | 1460 | 1416 | GLM 5 |
| LMArena Text: medicine and healthcare | 1455 | 1413 | GLM 5 |
| LMArena Text: legal and government | 1453 | 1431 | GLM 5 |
| LMArena Text: business and finance | 1430 | 1400 | GLM 5 |
| Neither model has a result on the other 2 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index71
| Benchmark | GLM 5 | GPT-5 | Leader |
|---|---|---|---|
| Design Arena: websites | 1247 | 1185 | GLM 5 |
| Design Arena: UI components | 1230 | 1179 | GLM 5 |
| Design Arena: game dev | 1234 | 1185 | GLM 5 |
| Design Arena: data viz | 1228 | 1221 | GLM 5 |
| Design Arena: 3D | 1229 | 1056 | GLM 5 |
| Design Arena: all code categories | 1246 | 1168 | GLM 5 |
| Design Arena: SVG | 1168 | 1188 | GPT-5 |
| Design Arena: ASCII art | 1146 | 1140 | GLM 5 |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index172
| Benchmark | GLM 5 | GPT-5 | Leader |
|---|---|---|---|
| SAGE (Vals AI) | Not measured | 43.7% | Only GPT-5 measured |
| MedScribe (Vals AI) | Not measured | 83.7% | Only GPT-5 measured |
| IFBench | 72.3% | 73.1% | GPT-5 |
| Long context reasoning (AA-LCR) | 75.7% | 78.2% | GPT-5 |
| DTBench | Not measured | 90.7% | Only GPT-5 measured |
| LMCA | Not measured | 40.0% | Only GPT-5 measured |
| Fiction.LiveBench | Not measured | 97.2% | Only GPT-5 measured |
| Creative writing (Lech Mazur) | Not measured | 86.0% | Only GPT-5 measured |
| CL-bench | 18.7% | Not measured | Only GLM 5 measured |
| LMArena Text | 1446 | 1406 | GLM 5 |
| LMArena Text: instruction following | 1428 | 1388 | GLM 5 |
| LMArena Text: creative writing | 1438 | 1365 | GLM 5 |
| LMArena Vision | Not measured | 1232 | Only GPT-5 measured |
| LMArena Text: English | 1458 | 1409 | GLM 5 |
| LMArena Text: non-English | 1430 | 1397 | GLM 5 |
| LMArena Text: Chinese | 1511 | 1422 | GLM 5 |
| LMArena Text: German | 1447 | 1416 | GLM 5 |
| LMArena Text: Japanese | 1416 | 1409 | GLM 5 |
| LMArena Text: Korean | 1423 | 1360 | GLM 5 |
| LMArena Text: French | 1456 | 1409 | GLM 5 |
| LMArena Text: Spanish | 1454 | 1399 | GLM 5 |
| LMArena Text: Russian | 1436 | 1407 | GLM 5 |
| LMArena Text: Polish | 1434 | 1431 | GLM 5 |
| LMArena Text: multi-turn | 1456 | 1426 | GLM 5 |
| LMArena Text: longer queries | 1446 | 1399 | GLM 5 |
| LMArena Text: writing and language | 1437 | 1384 | GLM 5 |
| LMArena Text: entertainment and media | 1426 | 1379 | GLM 5 |
| LMArena Vision: English | Not measured | 1231 | Only GPT-5 measured |
| LMArena Vision: Chinese | Not measured | 1267 | Only GPT-5 measured |
| LMArena Vision: reading text | Not measured | 1246 | Only GPT-5 measured |
| LMArena Vision: humor | Not measured | 1219 | Only GPT-5 measured |
| LMArena Vision: captioning | Not measured | 1212 | Only GPT-5 measured |
| LMArena Vision: recognizing things | Not measured | 1263 | Only GPT-5 measured |
| LMArena Vision: creative writing | Not measured | 1210 | Only GPT-5 measured |
| Neither model has a result on the other 12 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 5 | GPT-5 | Leader |
|---|---|---|---|
| Neither model has a result on 5 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
GLM 5 costs less
Repository review
50K input + 3K output tokens
GLM 5 costs less
Agent loop
200K input + 10K output tokens
GLM 5 costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | Z.ai GLM 5 | OpenAI GPT-5 |
|---|---|---|
| Maker | Z.ai | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | February 11, 2026 | August 7, 2025 |
| Context window | 205K | 400K |
| Input $/Mtok | $1.00 | $1.25 |
| Output $/Mtok | $3.20 | $10.00 |
| Coding index | 59.7 | 72.2 |
| Independent measurements behind it | 2 | 3 |
| Cost per solved task | $0.63 | $0.39 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 5 better than GPT-5 for coding?
Pick GPT-5 for coding: it scores 72.2 on the Vibe Coding Index against 59.7 for GLM 5. Pick GLM 5 when output tokens cost 3.1x less, or when what matters is LMArena WebDev or SWE-bench Verified (Vals AI), where it leads.
Which is cheaper, GLM 5 or GPT-5?
GLM 5 costs $1.00 per million input tokens and $3.20 per million output tokens; GPT-5 costs $1.25 and $10.00. Per solved SWE-bench Verified task, GLM 5 costs $0.63 and GPT-5 $0.39.
Which has the bigger context window, GLM 5 or GPT-5?
GPT-5, with 400K tokens against 205K.
How many benchmarks is the GLM 5 vs GPT-5 comparison based on?
66 benchmarks have a result for both models as of October 21, 2026. GLM 5's coding index rests on 2 independent benchmarks and GPT-5's on 3. Where only one model has a result, the page says so instead of naming a winner.