GLM 4.6 vs GPT-4.1
Updated October 21, 2026. 56 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 46.5 for GPT-4.1.
Pick GPT-4.1 when the context window is larger, 1.05M against 205K tokens, or when what matters is ALE-Bench or LegalBench (Vals AI), where it leads.
- Shared results
- 56
- GLM 4.6 only
- 10
- GPT-4.1 only
- 31
- 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 46.5.
2 benchmarks behind each score
Intelligence
AA Intelligence Index
GLM 4.6
GLM 4.6 leads, 18.5 against 12.7.
Same board, both measured
Reasoning
GPQA Diamond
GLM 4.6
GLM 4.6 leads, 74.5% against 65.4%.
Same board, both measured
Math
AIME
GLM 4.6
GLM 4.6 leads, 92.7% against 39.6%.
Same board, both measured
Knowledge
MMLU Pro
GLM 4.6
GLM 4.6 leads, 82.2% against 80.5%.
Same board, both measured
Web design
Design Arena: websites
GLM 4.6
GLM 4.6 leads, 1174 against 1039.
Same board, both measured
Long documents
Listed context window
GPT-4.1
GPT-4.1 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-4.1 $0.0060 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-4.1 has the number, $0.37.
Directional only
At a glance
- Coding index
- GLM 4.6 52.2 from 2 benchmarks, GPT-4.1 46.5 from 2 benchmarks.
- Widest category
- Math, 30.5 points apart on average over 6 shared benchmarks.
- Widest single gap
- AIME: GLM 4.6 92.7% against GPT-4.1 39.6%, 53.1 points apart on a 0 to 100 scale.
- Output price
- GLM 4.6 $2.20 against GPT-4.1 $8.00 per million output tokens, 3.6x apart.
- Context window
- GLM 4.6 takes 205K tokens, GPT-4.1 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-4.1 | Ties |
|---|---|---|---|---|
| Coding | 6 | 1 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 6 | 0 | ||
| Math | 6 | 0 | ||
| Knowledge | 7 | 3 | ||
| Web design | 6 | 0 | ||
| General | 18 | 1 | ||
| All shared boards | 51 | 5 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index61
| Benchmark | GLM 4.6 | GPT-4.1 | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 52.4% | Only GPT-4.1 measured |
| SWE-bench Verified | 68.2% | 39.6% | GLM 4.6 |
| SWE-bench Multimodal | Not measured | 31.1% | Only GPT-4.1 measured |
| LMArena WebDev | 1341 | Not measured | Only GLM 4.6 measured |
| AA Coding Index | 45.8 | Not measured | Only GLM 4.6 measured |
| LiveCodeBench (Vals AI) | 81.0% | 54.7% | GLM 4.6 |
| Vibe Code Bench (Vals AI) | 3.1% | Not measured | Only GLM 4.6 measured |
| LiveCodeBench (Artificial Analysis run) | 69.5% | 45.7% | GLM 4.6 |
| Terminal-Bench Hard (Artificial Analysis run) | 28.8% | 13.6% | GLM 4.6 |
| Terminal-Bench | 24.5% | Not measured | Only GLM 4.6 measured |
| WeirdML | Not measured | 39.0% | Only GPT-4.1 measured |
| ALE-Bench | 340.8 | 558.1 | GPT-4.1 |
| LMArena Text: coding | 1449 | 1390 | GLM 4.6 |
| LMArena Text: software and IT | 1455 | 1391 | GLM 4.6 |
| LMArena WebDev: HTML | 1350 | Not measured | Only GLM 4.6 measured |
| Neither model has a result on the other 23 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 4.6 | GPT-4.1 | Leader |
|---|---|---|---|
| AA Intelligence Index | 18.5 | 12.7 | GLM 4.6 |
| Epoch Capabilities Index | Not measured | 136.7 | Only GPT-4.1 measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 4.6 | GPT-4.1 | Leader |
|---|---|---|---|
| Design Arena: agents building full-stack apps | 1029 | Not measured | Only GLM 4.6 measured |
| Design Arena: agents building mobile apps | 1096 | Not measured | Only GLM 4.6 measured |
| Design Arena: agents building Android apps | 1090 | Not measured | Only GLM 4.6 measured |
| Design Arena: agents building Godot games | 1180 | Not measured | Only GLM 4.6 measured |
| τ²-Bench Telecom | 76.9% | 47.1% | GLM 4.6 |
| Neither model has a result on the other 34 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | GLM 4.6 | GPT-4.1 | Leader |
|---|---|---|---|
| GPQA Diamond | 74.5% | 65.4% | GLM 4.6 |
| MMMU Pro (Vals AI) | Not measured | 72.4% | Only GPT-4.1 measured |
| GPQA Diamond (Artificial Analysis run) | 78.0% | 66.6% | GLM 4.6 |
| Humanity's Last Exam (Artificial Analysis run) | 14.5% | 4.2% | GLM 4.6 |
| GPQA Diamond (Epoch AI run) | Not measured | 66.9% | Only GPT-4.1 measured |
| ARC-AGI | Not measured | 5.5% | Only GPT-4.1 measured |
| ARC-AGI-2 | Not measured | 0.4% | Only GPT-4.1 measured |
| SimpleBench | Not measured | 27.0% | Only GPT-4.1 measured |
| Chess puzzles | Not measured | 3.0% | Only GPT-4.1 measured |
| LMArena Text: hard prompts | 1440 | 1384 | GLM 4.6 |
| LMArena Text: expert | 1432 | 1364 | GLM 4.6 |
| LMArena Text: hard prompts in English | 1445 | 1393 | GLM 4.6 |
| LMArena Vision: diagrams | Not measured | 1215 | Only GPT-4.1 measured |
| LMArena Vision: homework | Not measured | 1248 | Only GPT-4.1 measured |
| Neither model has a result on the other 7 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | GLM 4.6 | GPT-4.1 | Leader |
|---|---|---|---|
| AIME | 92.7% | 39.6% | GLM 4.6 |
| MATH 500 (Vals AI) | Not measured | 87.2% | Only GPT-4.1 measured |
| MGSM (Vals AI) | 89.8% | 87.7% | GLM 4.6 |
| AA Math Index | 86.0 | 34.7 | GLM 4.6 |
| AIME 2025 (Artificial Analysis run) | 86.0% | 34.7% | GLM 4.6 |
| OTIS Mock AIME | Not measured | 38.3% | Only GPT-4.1 measured |
| FrontierMath, tiers 1 to 3 | Not measured | 6.0% | Only GPT-4.1 measured |
| MATH level 5 | Not measured | 83.0% | Only GPT-4.1 measured |
| LMArena Text: math | 1432 | 1369 | GLM 4.6 |
| LMArena Text: mathematical work | 1447 | 1360 | GLM 4.6 |
| Neither model has a result on the other 3 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index73
| Benchmark | GLM 4.6 | GPT-4.1 | Leader |
|---|---|---|---|
| MMLU Pro | 82.2% | 80.5% | GLM 4.6 |
| LegalBench (Vals AI) | 79.6% | 83.1% | GPT-4.1 |
| CaseLaw (Vals AI) | Not measured | 69.9% | Only GPT-4.1 measured |
| CorpFin (Vals AI) | 56.8% | 63.0% | GPT-4.1 |
| TaxEval (Vals AI) | 66.2% | 75.1% | GPT-4.1 |
| MortgageTax (Vals AI) | Not measured | 65.9% | Only GPT-4.1 measured |
| MedQA (Vals AI) | 92.2% | 91.2% | GLM 4.6 |
| MMLU Pro (Artificial Analysis run) | 82.9% | 80.6% | GLM 4.6 |
| SimpleQA Verified | Not measured | 31.1% | Only GPT-4.1 measured |
| GeoBench | Not measured | 72.0% | Only GPT-4.1 measured |
| LMArena Text: science | 1456 | 1382 | GLM 4.6 |
| LMArena Text: medicine and healthcare | 1455 | 1379 | GLM 4.6 |
| LMArena Text: legal and government | 1448 | 1399 | GLM 4.6 |
| LMArena Text: business and finance | 1416 | 1369 | GLM 4.6 |
| Neither model has a result on the other 3 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | GLM 4.6 | GPT-4.1 | Leader |
|---|---|---|---|
| Design Arena: websites | 1174 | 1039 | GLM 4.6 |
| Design Arena: UI components | 1147 | 1000 | GLM 4.6 |
| Design Arena: game dev | 1151 | 1080 | GLM 4.6 |
| Design Arena: data viz | 1161 | 1104 | GLM 4.6 |
| Design Arena: 3D | 1129 | 850 | GLM 4.6 |
| Design Arena: all code categories | 1166 | 1026 | GLM 4.6 |
| Design Arena: SVG | 1108 | Not measured | Only GLM 4.6 measured |
| Neither model has a result on the other 2 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index181
| Benchmark | GLM 4.6 | GPT-4.1 | Leader |
|---|---|---|---|
| IFBench | 43.4% | 43.0% | GLM 4.6 |
| Long context reasoning (AA-LCR) | 54.0% | 68.3% | GPT-4.1 |
| DTBench | Not measured | 68.3% | Only GPT-4.1 measured |
| LMCA | Not measured | 25.6% | Only GPT-4.1 measured |
| Fiction.LiveBench | Not measured | 63.9% | Only GPT-4.1 measured |
| LMArena Text | 1440 | 1383 | GLM 4.6 |
| LMArena Text: instruction following | 1410 | 1367 | GLM 4.6 |
| LMArena Text: creative writing | 1412 | 1363 | GLM 4.6 |
| LMArena Vision | Not measured | 1210 | Only GPT-4.1 measured |
| LMArena Text: English | 1449 | 1392 | GLM 4.6 |
| LMArena Text: non-English | 1426 | 1370 | GLM 4.6 |
| LMArena Text: Chinese | 1499 | 1382 | GLM 4.6 |
| LMArena Text: German | 1447 | 1381 | GLM 4.6 |
| LMArena Text: Japanese | 1393 | 1319 | GLM 4.6 |
| LMArena Text: Korean | 1400 | 1338 | GLM 4.6 |
| LMArena Text: French | 1459 | 1382 | GLM 4.6 |
| LMArena Text: Spanish | 1436 | 1376 | GLM 4.6 |
| LMArena Text: Russian | 1418 | 1377 | GLM 4.6 |
| LMArena Text: Polish | 1447 | 1389 | GLM 4.6 |
| LMArena Text: multi-turn | 1427 | 1397 | GLM 4.6 |
| LMArena Text: longer queries | 1422 | 1385 | GLM 4.6 |
| LMArena Text: writing and language | 1415 | 1371 | GLM 4.6 |
| LMArena Text: entertainment and media | 1409 | 1365 | GLM 4.6 |
| LMArena Vision: English | Not measured | 1209 | Only GPT-4.1 measured |
| LMArena Vision: Chinese | Not measured | 1226 | Only GPT-4.1 measured |
| LMArena Vision: reading text | Not measured | 1218 | Only GPT-4.1 measured |
| LMArena Vision: humor | Not measured | 1196 | Only GPT-4.1 measured |
| LMArena Vision: captioning | Not measured | 1207 | Only GPT-4.1 measured |
| LMArena Vision: recognizing things | Not measured | 1208 | Only GPT-4.1 measured |
| LMArena Vision: creative writing | Not measured | 1210 | Only GPT-4.1 measured |
| Neither model has a result on the other 16 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.6 | GPT-4.1 | 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-4.1 |
|---|---|---|
| Maker | Z.ai | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | September 30, 2025 | April 14, 2025 |
| Context window | 205K | 1.05M |
| Input $/Mtok | $0.55 | $2.00 |
| Output $/Mtok | $2.20 | $8.00 |
| Coding index | 52.2 | 46.5 |
| Independent measurements behind it | 2 | 2 |
| Cost per solved task | Not computable | $0.37 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.6 better than GPT-4.1 for coding?
Pick GLM 4.6 for coding: it scores 52.2 on the Vibe Coding Index against 46.5 for GPT-4.1. Pick GPT-4.1 when the context window is larger, 1.05M against 205K tokens, or when what matters is ALE-Bench or LegalBench (Vals AI), where it leads.
Which is cheaper, GLM 4.6 or GPT-4.1?
GLM 4.6 costs $0.55 per million input tokens and $2.20 per million output tokens; GPT-4.1 costs $2.00 and $8.00. Cost per solved task is known for GPT-4.1 only.
Which has the bigger context window, GLM 4.6 or GPT-4.1?
GPT-4.1, with 1.05M tokens against 205K.
How many benchmarks is the GLM 4.6 vs GPT-4.1 comparison based on?
56 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-4.1's on 2. Where only one model has a result, the page says so instead of naming a winner.