GLM 4.5 vs GLM 5
Updated October 21, 2026. 53 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick GLM 4.5 for coding: it scores 64.2 on the Vibe Coding Index against 59.7 for GLM 5, though on 1 benchmark against 2.
Pick GLM 5 when the context window is larger, 205K against 131K tokens, or when what matters is SWE-bench Verified or LiveCodeBench (Vals AI), where it leads.
- Shared results
- 53
- GLM 4.5 only
- 7
- GLM 5 only
- 22
- 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
GLM 4.5
GLM 4.5 scores 64.2 against 59.7.
1 and 2 benchmarks behind the scores
Intelligence
AA Intelligence Index
GLM 5
GLM 5 leads, 27.9 against 12.8.
Same board, both measured
Reasoning
GPQA Diamond
GLM 5
GLM 5 leads, 83.3% against 72.2%.
Same board, both measured
Math
AIME
GLM 5
GLM 5 leads, 91.7% against 86.7%.
Same board, both measured
Knowledge
MMLU Pro
GLM 5
GLM 5 leads, 86.0% against 81.2%.
Same board, both measured
Web design
Design Arena: websites
GLM 5
GLM 5 leads, 1247 against 1170.
Same board, both measured
Long documents
Listed context window
GLM 5
GLM 5 takes 205K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 4.5
GLM 4.5 $0.0017, GLM 5 $0.0026 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GLM 5 has the number, $0.63.
Directional only
At a glance
- Coding index
- GLM 4.5 64.2 from 1 benchmark, GLM 5 59.7 from 2 benchmarks.
- Widest category
- Agentic work, 55.3 points apart on average over 1 shared benchmark.
- Widest single gap
- τ²-Bench Telecom: GLM 4.5 43.0% against GLM 5 98.2%, 55.3 points apart on a 0 to 100 scale.
- Output price
- GLM 4.5 $2.20 against GLM 5 $3.20 per million output tokens, 1.5x apart.
- Context window
- GLM 4.5 takes 131K tokens, GLM 5 205K.
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 4.5 | Bars | GLM 5 | Ties |
|---|---|---|---|---|
| Coding | 0 | 7 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 6 | ||
| Math | 0 | 3 | ||
| Knowledge | 1 | 8 | ||
| Web design | 0 | 7 | ||
| General | 1 | 18 | ||
| All shared boards | 2 | 51 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index07
| Benchmark | GLM 4.5 | GLM 5 | Leader |
|---|---|---|---|
| SWE-bench Verified | 64.2% | 72.8% | GLM 5 |
| SWE-bench Multilingual | Not measured | 69.7% | Only GLM 5 measured |
| LMArena WebDev | Not measured | 1435 | Only GLM 5 measured |
| LiveCodeBench (Vals AI) | 67.5% | 81.9% | GLM 5 |
| SWE-bench Verified (Vals AI) | Not measured | 71.4% | Only GLM 5 measured |
| Vibe Code Bench (Vals AI) | Not measured | 23.4% | Only GLM 5 measured |
| LiveCodeBench (Artificial Analysis run) | 73.8% | Not measured | Only GLM 4.5 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 22.0% | 43.2% | GLM 5 |
| Terminal-Bench | Not measured | 52.4% | Only GLM 5 measured |
| WeirdML | 40.6% | 48.2% | GLM 5 |
| ALE-Bench | 344.8 | 765.6 | GLM 5 |
| LMArena Text: coding | 1434 | 1461 | GLM 5 |
| LMArena Text: software and IT | 1440 | 1460 | GLM 5 |
| LMArena WebDev: React | Not measured | 1424 | Only GLM 5 measured |
| LMArena WebDev: HTML | Not measured | 1450 | Only GLM 5 measured |
| Neither model has a result on the other 23 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 4.5 | GLM 5 | Leader |
|---|---|---|---|
| AA Intelligence Index | 12.8 | 27.9 | GLM 5 |
| Epoch Capabilities Index | Not measured | 145.8 | Only GLM 5 measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 4.5 | GLM 5 | Leader |
|---|---|---|---|
| Design Arena: agents building full-stack apps | Not measured | 1123 | Only GLM 5 measured |
| Design Arena: agents building mobile apps | Not measured | 1136 | Only GLM 5 measured |
| Design Arena: agents building Android apps | Not measured | 1152 | Only GLM 5 measured |
| Design Arena: agents building Godot games | Not measured | 1144 | Only GLM 5 measured |
| Design Arena: HTML slides | Not measured | 1148 | Only GLM 5 measured |
| τ²-Bench Telecom | 43.0% | 98.2% | GLM 5 |
| Vending-Bench 2 | Not measured | $4,432 | Only GLM 5 measured |
| Neither model has a result on the other 32 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | GLM 4.5 | GLM 5 | Leader |
|---|---|---|---|
| GPQA Diamond | 72.2% | 83.3% | GLM 5 |
| GPQA Diamond (Artificial Analysis run) | 78.2% | 82.0% | GLM 5 |
| Humanity's Last Exam (Artificial Analysis run) | 13.0% | 29.3% | GLM 5 |
| GPQA Diamond (Epoch AI run) | Not measured | 87.8% | Only GLM 5 measured |
| ARC-AGI | Not measured | 44.7% | Only GLM 5 measured |
| ARC-AGI-2 | Not measured | 4.9% | Only GLM 5 measured |
| SimpleBench | Not measured | 53.2% | Only GLM 5 measured |
| LMArena Text: hard prompts | 1429 | 1452 | GLM 5 |
| LMArena Text: expert | 1433 | 1454 | GLM 5 |
| LMArena Text: hard prompts in English | 1429 | 1460 | GLM 5 |
| Neither model has a result on the other 11 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | GLM 4.5 | GLM 5 | Leader |
|---|---|---|---|
| AIME | 86.7% | 91.7% | GLM 5 |
| MATH 500 (Vals AI) | 94.0% | Not measured | Only GLM 4.5 measured |
| MGSM (Vals AI) | 90.8% | Not measured | Only GLM 4.5 measured |
| AA Math Index | 73.7 | Not measured | Only GLM 4.5 measured |
| AIME 2025 (Artificial Analysis run) | 73.7% | Not measured | Only GLM 4.5 measured |
| OTIS Mock AIME | Not measured | 80.0% | Only GLM 5 measured |
| LMArena Text: math | 1427 | 1440 | GLM 5 |
| LMArena Text: mathematical work | 1430 | 1451 | GLM 5 |
| Neither model has a result on the other 5 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index18
| Benchmark | GLM 4.5 | GLM 5 | Leader |
|---|---|---|---|
| MMLU Pro | 81.2% | 86.0% | GLM 5 |
| LegalBench (Vals AI) | 75.6% | 84.1% | GLM 5 |
| CaseLaw (Vals AI) | Not measured | 52.5% | Only GLM 5 measured |
| CorpFin (Vals AI) | 61.0% | 62.9% | GLM 5 |
| TaxEval (Vals AI) | 72.4% | 70.0% | GLM 4.5 |
| MedQA (Vals AI) | 90.0% | 94.3% | GLM 5 |
| MMLU Pro (Artificial Analysis run) | 83.5% | Not measured | Only GLM 4.5 measured |
| LMArena Text: science | 1444 | 1460 | GLM 5 |
| LMArena Text: medicine and healthcare | 1455 | 1455 | GLM 5 |
| LMArena Text: legal and government | 1438 | 1453 | GLM 5 |
| LMArena Text: business and finance | 1420 | 1430 | GLM 5 |
| Neither model has a result on the other 6 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index07
| Benchmark | GLM 4.5 | GLM 5 | Leader |
|---|---|---|---|
| Design Arena: websites | 1170 | 1247 | GLM 5 |
| Design Arena: UI components | 1144 | 1230 | GLM 5 |
| Design Arena: game dev | 1145 | 1234 | GLM 5 |
| Design Arena: data viz | 1159 | 1228 | GLM 5 |
| Design Arena: 3D | 1170 | 1229 | GLM 5 |
| Design Arena: all code categories | 1165 | 1246 | GLM 5 |
| Design Arena: SVG | 1096 | 1168 | GLM 5 |
| Design Arena: ASCII art | Not measured | 1146 | Only GLM 5 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index118
| Benchmark | GLM 4.5 | GLM 5 | Leader |
|---|---|---|---|
| IFBench | 44.1% | 72.3% | GLM 5 |
| Long context reasoning (AA-LCR) | 52.7% | 75.7% | GLM 5 |
| Creative writing (Lech Mazur) | 73.4% | Not measured | Only GLM 4.5 measured |
| CL-bench | Not measured | 18.7% | Only GLM 5 measured |
| LMArena Text | 1430 | 1446 | GLM 5 |
| LMArena Text: instruction following | 1404 | 1428 | GLM 5 |
| LMArena Text: creative writing | 1395 | 1438 | GLM 5 |
| LMArena Text: English | 1437 | 1458 | GLM 5 |
| LMArena Text: non-English | 1417 | 1430 | GLM 5 |
| LMArena Text: Chinese | 1465 | 1511 | GLM 5 |
| LMArena Text: German | 1407 | 1447 | GLM 5 |
| LMArena Text: Japanese | 1415 | 1416 | GLM 5 |
| LMArena Text: Korean | 1380 | 1423 | GLM 5 |
| LMArena Text: French | 1418 | 1456 | GLM 5 |
| LMArena Text: Spanish | 1454 | 1454 | GLM 4.5 |
| LMArena Text: Russian | 1413 | 1436 | GLM 5 |
| LMArena Text: Polish | 1422 | 1434 | GLM 5 |
| LMArena Text: multi-turn | 1415 | 1456 | GLM 5 |
| LMArena Text: longer queries | 1412 | 1446 | GLM 5 |
| LMArena Text: writing and language | 1403 | 1437 | GLM 5 |
| LMArena Text: entertainment and media | 1404 | 1426 | GLM 5 |
| Neither model has a result on the other 25 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.5 | GLM 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 4.5 costs less
Repository review
50K input + 3K output tokens
GLM 4.5 costs less
Agent loop
200K input + 10K output tokens
GLM 4.5 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 4.5 | Z.ai GLM 5 |
|---|---|---|
| Maker | Z.ai | Z.ai |
| Weights | Open weights | Open weights |
| Released | July 25, 2025 | February 11, 2026 |
| Context window | 131K | 205K |
| Input $/Mtok | $0.60 | $1.00 |
| Output $/Mtok | $2.20 | $3.20 |
| Coding index | 64.2 | 59.7 |
| Independent measurements behind it | 1 | 2 |
| Cost per solved task | Not computable | $0.63 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.5 better than GLM 5 for coding?
Pick GLM 4.5 for coding: it scores 64.2 on the Vibe Coding Index against 59.7 for GLM 5, though on 1 benchmark against 2. Pick GLM 5 when the context window is larger, 205K against 131K tokens, or when what matters is SWE-bench Verified or LiveCodeBench (Vals AI), where it leads.
Which is cheaper, GLM 4.5 or GLM 5?
GLM 4.5 costs $0.60 per million input tokens and $2.20 per million output tokens; GLM 5 costs $1.00 and $3.20. Cost per solved task is known for GLM 5 only.
Which has the bigger context window, GLM 4.5 or GLM 5?
GLM 5, with 205K tokens against 131K.
How many benchmarks is the GLM 4.5 vs GLM 5 comparison based on?
53 benchmarks have a result for both models as of October 21, 2026. GLM 4.5's coding index rests on 1 independent benchmark and GLM 5's on 2. Where only one model has a result, the page says so instead of naming a winner.