GLM 4.6 vs GPT-5.4
Updated October 21, 2026. 60 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 4.6 for coding: it scores 52.2 on the Vibe Coding Index against 37.7 for GPT-5.4.
Pick GPT-5.4 when the context window is larger, 1.05M against 205K tokens, or when what matters is LMArena WebDev or AA Coding Index, where it leads.
- Shared results
- 60
- GLM 4.6 only
- 6
- GPT-5.4 only
- 49
- 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 4.6
GLM 4.6 scores 52.2 against 37.7.
2 and 1 benchmarks behind the scores
Intelligence
AA Intelligence Index
GPT-5.4
GPT-5.4 leads, 39.0 against 18.5.
Same board, both measured
Reasoning
GPQA Diamond
GPT-5.4
GPT-5.4 leads, 91.7% against 74.5%.
Same board, both measured
Math
AIME
GPT-5.4
GPT-5.4 leads, 96.7% against 92.7%.
Same board, both measured
Knowledge
MMLU Pro
GPT-5.4
GPT-5.4 leads, 87.5% against 82.2%.
Same board, both measured
Web design
Design Arena: websites
GPT-5.4
GPT-5.4 leads, 1199 against 1174.
Same board, both measured
Long documents
Listed context window
GPT-5.4
GPT-5.4 takes 1.05M tokens against 205K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 4.6
GLM 4.6 $0.0016, GPT-5.4 $0.010 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.4 has the number, $1.02.
Directional only
At a glance
- Coding index
- GLM 4.6 52.2 from 2 benchmarks, GPT-5.4 37.7 from 1 benchmark.
- Widest category
- Coding, 27.4 points apart on average over 10 shared benchmarks.
- Widest single gap
- Vibe Code Bench (Vals AI): GLM 4.6 3.1% against GPT-5.4 67.4%, 64.3 points apart on a 0 to 100 scale.
- Output price
- GLM 4.6 $2.20 against GPT-5.4 $15.00 per million output tokens, 6.8x apart.
- Context window
- GLM 4.6 takes 205K tokens, GPT-5.4 1.05M.
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 4.6 | Bars | GPT-5.4 | Ties |
|---|---|---|---|---|
| Coding | 0 | 10 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 4 | 1 | ||
| Reasoning | 0 | 6 | ||
| Math | 0 | 3 | ||
| Knowledge | 0 | 9 | ||
| Web design | 1 | 6 | ||
| General | 0 | 19 | ||
| All shared boards | 5 | 55 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index010
| Benchmark | GLM 4.6 | GPT-5.4 | Leader |
|---|---|---|---|
| SWE-bench Verified | 68.2% | Not measured | Only GLM 4.6 measured |
| LMArena WebDev | 1341 | 1464 | GPT-5.4 |
| AA Coding Index | 45.8 | 71.1 | GPT-5.4 |
| LiveCodeBench (Vals AI) | 81.0% | 84.1% | GPT-5.4 |
| SWE-bench Verified (Vals AI) | Not measured | 78.2% | Only GPT-5.4 measured |
| Vibe Code Bench (Vals AI) | 3.1% | 67.4% | GPT-5.4 |
| Code migration (Vals AI) | Not measured | 35.0% | Only GPT-5.4 measured |
| ProgramBench (Vals AI) | Not measured | 0.5% | Only GPT-5.4 measured |
| SkillsBench (Vals AI) | Not measured | 51.7% | Only GPT-5.4 measured |
| LiveCodeBench (Artificial Analysis run) | 69.5% | Not measured | Only GLM 4.6 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 28.8% | 57.6% | GPT-5.4 |
| Terminal-Bench | 24.5% | 81.8% | GPT-5.4 |
| GSO-Bench | Not measured | 31.4% | Only GPT-5.4 measured |
| WeirdML | Not measured | 77.7% | Only GPT-5.4 measured |
| MirrorCode | Not measured | 15.6% | Only GPT-5.4 measured |
| ALE-Bench | 340.8 | 1607.0 | GPT-5.4 |
| LMArena Text: coding | 1449 | 1496 | GPT-5.4 |
| LMArena Text: software and IT | 1455 | 1491 | GPT-5.4 |
| LMArena WebDev: React | Not measured | 1454 | Only GPT-5.4 measured |
| LMArena WebDev: HTML | 1350 | 1478 | GPT-5.4 |
| LMArena WebDev: from an image | Not measured | 1436 | Only GPT-5.4 measured |
| Neither model has a result on the other 17 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 4.6 | GPT-5.4 | Leader |
|---|---|---|---|
| AA Intelligence Index | 18.5 | 39.0 | GPT-5.4 |
| Epoch Capabilities Index | Not measured | 157.3 | Only GPT-5.4 measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index41
| Benchmark | GLM 4.6 | GPT-5.4 | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | Not measured | 1059 | Only GPT-5.4 measured |
| Design Arena: agents building full-stack apps | 1029 | 1017 | GLM 4.6 |
| Design Arena: agents building mobile apps | 1096 | 1087 | GLM 4.6 |
| Design Arena: agents building Android apps | 1090 | 1037 | GLM 4.6 |
| Design Arena: agents building Godot games | 1180 | 1135 | GLM 4.6 |
| τ²-Bench Telecom | 76.9% | 87.1% | GPT-5.4 |
| APEX-Agents | Not measured | 52.4% | Only GPT-5.4 measured |
| METR time horizons | Not measured | 74.3% | Only GPT-5.4 measured |
| DeepResearch Bench | Not measured | 35.1% | Only GPT-5.4 measured |
| PostTrainBench | Not measured | 19.0% | Only GPT-5.4 measured |
| Vending-Bench 2 | Not measured | $6,144 | Only GPT-5.4 measured |
| Neither model has a result on the other 28 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | GLM 4.6 | GPT-5.4 | Leader |
|---|---|---|---|
| GPQA Diamond | 74.5% | 91.7% | GPT-5.4 |
| MMMU Pro (Vals AI) | Not measured | 87.5% | Only GPT-5.4 measured |
| GPQA Diamond (Artificial Analysis run) | 78.0% | 92.0% | GPT-5.4 |
| Humanity's Last Exam (Artificial Analysis run) | 14.5% | 43.7% | GPT-5.4 |
| GPQA Diamond (Epoch AI run) | Not measured | 93.3% | Only GPT-5.4 measured |
| ARC-AGI | Not measured | 93.7% | Only GPT-5.4 measured |
| ARC-AGI-2 | Not measured | 74.0% | Only GPT-5.4 measured |
| Chess puzzles | Not measured | 48.0% | Only GPT-5.4 measured |
| Mystery game puzzles | Not measured | 37.0% | Only GPT-5.4 measured |
| EBR-bench | Not measured | 25.4% | Only GPT-5.4 measured |
| LMArena Text: hard prompts | 1440 | 1485 | GPT-5.4 |
| LMArena Text: expert | 1432 | 1507 | GPT-5.4 |
| LMArena Text: hard prompts in English | 1445 | 1479 | GPT-5.4 |
| LMArena Vision: diagrams | Not measured | 1327 | Only GPT-5.4 measured |
| LMArena Vision: homework | Not measured | 1336 | Only GPT-5.4 measured |
| Neither model has a result on the other 6 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | GLM 4.6 | GPT-5.4 | Leader |
|---|---|---|---|
| AIME | 92.7% | 96.7% | GPT-5.4 |
| MGSM (Vals AI) | 89.8% | Not measured | Only GLM 4.6 measured |
| AA Math Index | 86.0 | Not measured | Only GLM 4.6 measured |
| AIME 2025 (Artificial Analysis run) | 86.0% | Not measured | Only GLM 4.6 measured |
| OTIS Mock AIME | Not measured | 97.8% | Only GPT-5.4 measured |
| FrontierMath, tiers 1 to 3 | Not measured | 78.6% | Only GPT-5.4 measured |
| FrontierMath, tier 4 | Not measured | 49.0% | Only GPT-5.4 measured |
| ProofBench | Not measured | 56.0% | Only GPT-5.4 measured |
| LMArena Text: math | 1432 | 1488 | GPT-5.4 |
| LMArena Text: mathematical work | 1447 | 1497 | GPT-5.4 |
| Neither model has a result on the other 3 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index09
| Benchmark | GLM 4.6 | GPT-5.4 | Leader |
|---|---|---|---|
| MMLU Pro | 82.2% | 87.5% | GPT-5.4 |
| LegalBench (Vals AI) | 79.6% | 86.0% | GPT-5.4 |
| CaseLaw (Vals AI) | Not measured | 63.8% | Only GPT-5.4 measured |
| CorpFin (Vals AI) | 56.8% | 65.3% | GPT-5.4 |
| TaxEval (Vals AI) | 66.2% | 74.0% | GPT-5.4 |
| MortgageTax (Vals AI) | Not measured | 68.3% | Only GPT-5.4 measured |
| MedQA (Vals AI) | 92.2% | 96.1% | GPT-5.4 |
| MedCode (Vals AI) | Not measured | 41.3% | Only GPT-5.4 measured |
| MMLU Pro (Artificial Analysis run) | 82.9% | Not measured | Only GLM 4.6 measured |
| SimpleQA Verified | Not measured | 45.1% | Only GPT-5.4 measured |
| LMArena Text: science | 1456 | 1479 | GPT-5.4 |
| LMArena Text: medicine and healthcare | 1455 | 1458 | GPT-5.4 |
| LMArena Text: legal and government | 1448 | 1486 | GPT-5.4 |
| LMArena Text: business and finance | 1416 | 1474 | GPT-5.4 |
| Neither model has a result on the other 3 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index16
| Benchmark | GLM 4.6 | GPT-5.4 | Leader |
|---|---|---|---|
| Design Arena: websites | 1174 | 1199 | GPT-5.4 |
| Design Arena: UI components | 1147 | 1215 | GPT-5.4 |
| Design Arena: game dev | 1151 | 1195 | GPT-5.4 |
| Design Arena: data viz | 1161 | 1196 | GPT-5.4 |
| Design Arena: 3D | 1129 | 1065 | GLM 4.6 |
| Design Arena: all code categories | 1166 | 1180 | GPT-5.4 |
| Design Arena: SVG | 1108 | 1141 | GPT-5.4 |
| Design Arena: ASCII art | Not measured | 1188 | Only GPT-5.4 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index019
| Benchmark | GLM 4.6 | GPT-5.4 | Leader |
|---|---|---|---|
| SAGE (Vals AI) | Not measured | 43.3% | Only GPT-5.4 measured |
| MedScribe (Vals AI) | Not measured | 77.5% | Only GPT-5.4 measured |
| IFBench | 43.4% | 73.9% | GPT-5.4 |
| Long context reasoning (AA-LCR) | 54.0% | 82.0% | GPT-5.4 |
| DTBench | Not measured | 94.4% | Only GPT-5.4 measured |
| LMCA | Not measured | 52.0% | Only GPT-5.4 measured |
| CL-bench | Not measured | 27.9% | Only GPT-5.4 measured |
| Furniture assembly | Not measured | 37.5% | Only GPT-5.4 measured |
| CL-bench Life | Not measured | 21.7% | Only GPT-5.4 measured |
| LMArena Text | 1440 | 1469 | GPT-5.4 |
| LMArena Text: instruction following | 1410 | 1469 | GPT-5.4 |
| LMArena Text: creative writing | 1412 | 1439 | GPT-5.4 |
| LMArena Vision | Not measured | 1303 | Only GPT-5.4 measured |
| LMArena Text: English | 1449 | 1467 | GPT-5.4 |
| LMArena Text: non-English | 1426 | 1465 | GPT-5.4 |
| LMArena Text: Chinese | 1499 | 1520 | GPT-5.4 |
| LMArena Text: German | 1447 | 1472 | GPT-5.4 |
| LMArena Text: Japanese | 1393 | 1485 | GPT-5.4 |
| LMArena Text: Korean | 1400 | 1449 | GPT-5.4 |
| LMArena Text: French | 1459 | 1493 | GPT-5.4 |
| LMArena Text: Spanish | 1436 | 1452 | GPT-5.4 |
| LMArena Text: Russian | 1418 | 1480 | GPT-5.4 |
| LMArena Text: Polish | 1447 | 1472 | GPT-5.4 |
| LMArena Text: multi-turn | 1427 | 1482 | GPT-5.4 |
| LMArena Text: longer queries | 1422 | 1473 | GPT-5.4 |
| LMArena Text: writing and language | 1415 | 1458 | GPT-5.4 |
| LMArena Text: entertainment and media | 1409 | 1438 | GPT-5.4 |
| LMArena Vision: English | Not measured | 1301 | Only GPT-5.4 measured |
| LMArena Vision: Chinese | Not measured | 1354 | Only GPT-5.4 measured |
| LMArena Vision: reading text | Not measured | 1312 | Only GPT-5.4 measured |
| LMArena Vision: humor | Not measured | 1284 | Only GPT-5.4 measured |
| LMArena Vision: recognizing things | Not measured | 1251 | Only GPT-5.4 measured |
| LMArena Vision: creative writing | Not measured | 1283 | Only GPT-5.4 measured |
| LMArena Document | Not measured | 1471 | Only GPT-5.4 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 4.6 | GPT-5.4 | 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 4.6 costs less
Repository review
50K input + 3K output tokens
GLM 4.6 costs less
Agent loop
200K input + 10K output tokens
GLM 4.6 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 4.6 | OpenAI GPT-5.4 |
|---|---|---|
| Maker | Z.ai | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | September 30, 2025 | March 5, 2026 |
| Context window | 205K | 1.05M |
| Input $/Mtok | $0.55 | $2.50 |
| Output $/Mtok | $2.20 | $15.00 |
| Coding index | 52.2 | 37.7 |
| Independent measurements behind it | 2 | 1 |
| Cost per solved task | Not computable | $1.02 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.6 better than GPT-5.4 for coding?
Pick GLM 4.6 for coding: it scores 52.2 on the Vibe Coding Index against 37.7 for GPT-5.4. Pick GPT-5.4 when the context window is larger, 1.05M against 205K tokens, or when what matters is LMArena WebDev or AA Coding Index, where it leads.
Which is cheaper, GLM 4.6 or GPT-5.4?
GLM 4.6 costs $0.55 per million input tokens and $2.20 per million output tokens; GPT-5.4 costs $2.50 and $15.00. Cost per solved task is known for GPT-5.4 only.
Which has the bigger context window, GLM 4.6 or GPT-5.4?
GPT-5.4, with 1.05M tokens against 205K.
How many benchmarks is the GLM 4.6 vs GPT-5.4 comparison based on?
60 benchmarks have a result for both models as of October 21, 2026. GLM 4.6's coding index rests on 2 independent benchmarks and GPT-5.4's on 1. Where only one model has a result, the page says so instead of naming a winner.