GLM 5.3 vs GPT-4.1
GLM 5.3 leads GPT-4.1 on the Artificial Analysis Intelligence Index, 44.8 to 12.7, and GLM 5.3 costs 1.8x less per output token ($4.40 vs $8.00 per million). Updated October 21, 2026; 53 benchmarks have a result for both.
GLM 5.3 or GPT-4.1: which to pick
Only GLM 5.3 has a coding score, 70.6 on the Vibe Coding Index; GPT-4.1 has none, so the two cannot be ranked on coding.
Pick GPT-4.1 for work measured by TaxEval (Vals AI), where it leads.
- Shared results
- 53
- GLM 5.3 only
- 47
- GPT-4.1 only
- 39
- Like-for-like categories
- 7 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
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, six public coding benchmarks
No call
Only GLM 5.3 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
GLM 5.3
GLM 5.3 leads, 44.8 against 12.7.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only GLM 5.3 has a AA Agentic Index result, 53.1.
Directional only
Reasoning
GPQA Diamond
GLM 5.3
GLM 5.3 leads, 88.1% against 65.4%.
Same board, both measured
Math
AIME
No call
Only GPT-4.1 has a AIME result, 39.6%.
Directional only
Knowledge
MMLU Pro
GLM 5.3
GLM 5.3 leads, 86.8% against 80.5%.
Same board, both measured
Web design
Design Arena: websites
GLM 5.3
GLM 5.3 leads, 1295 against 1039.
Same board, both measured
General
Vals Index
No call
Only GLM 5.3 has a Vals Index result, 59.7%.
Directional only
Long documents
Listed context window
GLM 5.3
GLM 5.3 takes 1.05M tokens against 1.05M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 5.3
GLM 5.3 $0.0036, GPT-4.1 $0.0060 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GLM 5.3
GLM 5.3 $0.36, GPT-4.1 $0.37.
Published attempt cost and pass rate
At a glance
- Coding index
- GLM 5.3 70.6 from 6 benchmarks, GPT-4.1 not benchmarked.
- Widest category
- Math, 39.9 points apart on average over 4 shared benchmarks.
- Widest single gap
- FrontierMath, tiers 1 to 3: GLM 5.3 68.8% against GPT-4.1 6.0%, 62.8 points apart on a 0 to 100 scale.
- Output price
- GLM 5.3 $4.40 against GPT-4.1 $8.00 per million output tokens, 1.8x apart.
- Context window
- GLM 5.3 takes 1.05M 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 5.3 | Bars | GPT-4.1 | Ties |
|---|---|---|---|---|
| Coding | 5 | 0 | ||
| Intelligence | 2 | 0 | ||
| Reasoning | 8 | 0 | ||
| Math | 4 | 0 | ||
| Knowledge | 7 | 1 | ||
| Web design | 6 | 0 | ||
| General | 20 | 0 | ||
| All shared boards | 52 | 1 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index50
| Benchmark | GLM 5.3 | GPT-4.1 | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 52.4% | Only GPT-4.1 measured |
| SWE-bench Verified | Not measured | 39.6% | Only GPT-4.1 measured |
| SWE-bench Multimodal | Not measured | 31.1% | Only GPT-4.1 measured |
| LiveBench coding | 82.8% | Not measured | Only GLM 5.3 measured |
| LMArena WebDev | 1621 | Not measured | Only GLM 5.3 measured |
| AA Coding Index | 74.8 | Not measured | Only GLM 5.3 measured |
| LiveCodeBench (Vals AI) | 80.5% | 54.7% | GLM 5.3 |
| SWE-bench Verified (Vals AI) | 95.4% | Not measured | Only GLM 5.3 measured |
| Terminal-Bench 4 (Vals AI) | 38.9% | Not measured | Only GLM 5.3 measured |
| Vibe Code Bench (Vals AI) | 78.1% | Not measured | Only GLM 5.3 measured |
| Code migration (Vals AI) | 44.2% | Not measured | Only GLM 5.3 measured |
| ProgramBench (Vals AI) | 1.5% | Not measured | Only GLM 5.3 measured |
| IOI (Vals AI) | 68.4% | Not measured | Only GLM 5.3 measured |
| Terminal-Bench 2.1 (Vals AI) | 71.5% | Not measured | Only GLM 5.3 measured |
| Vibe Code Bench 1-100 (Vals AI) | 20.0% | Not measured | Only GLM 5.3 measured |
| SkillsBench (Vals AI) | 47.5% | Not measured | Only GLM 5.3 measured |
| CyberBench (Vals AI) | 72.1% | Not measured | Only GLM 5.3 measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 45.7% | Only GPT-4.1 measured |
| SciCode | 59.0% | Not measured | Only GLM 5.3 measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 13.6% | Only GPT-4.1 measured |
| FrontierCode | 40.1% | Not measured | Only GLM 5.3 measured |
| FrontierSWE | 30.2% | Not measured | Only GLM 5.3 measured |
| WeirdML | 75.4% | 39.0% | GLM 5.3 |
| ALE-Bench | 1317.4 | 558.1 | GLM 5.3 |
| LMArena Text: coding | 1497 | 1390 | GLM 5.3 |
| LMArena Text: software and IT | 1493 | 1391 | GLM 5.3 |
| LMArena WebDev: React | 1641 | Not measured | Only GLM 5.3 measured |
| LMArena WebDev: HTML | 1595 | Not measured | Only GLM 5.3 measured |
| SciCode | Not measured | 4.6% | Only GPT-4.1 measured |
| Neither model has a result on the other 10 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GLM 5.3 | GPT-4.1 | Leader |
|---|---|---|---|
| AA Intelligence Index | 44.8 | 12.7 | GLM 5.3 |
| Epoch Capabilities Index | 156.0 | 136.7 | GLM 5.3 |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index19 boards, none shared
| Benchmark | GLM 5.3 | GPT-4.1 | Leader |
|---|---|---|---|
| AA Agentic Index | 53.1 | Not measured | Only GLM 5.3 measured |
| Design Arena: agents building mobile apps | 1194 | Not measured | Only GLM 5.3 measured |
| Design Arena: HTML slides | 1189 | Not measured | Only GLM 5.3 measured |
| Design Arena: PowerPoint slides | 1254 | Not measured | Only GLM 5.3 measured |
| LiveBench research | 73.2% | Not measured | Only GLM 5.3 measured |
| LiveBench browser | 0.0% | Not measured | Only GLM 5.3 measured |
| LiveBench long agentic | 35.9% | Not measured | Only GLM 5.3 measured |
| SREBench (Vals AI) | 9.2% | Not measured | Only GLM 5.3 measured |
| Finance Agent (Vals AI) | 55.8% | Not measured | Only GLM 5.3 measured |
| Tax Agent Bench (Vals AI) | 39.7% | Not measured | Only GLM 5.3 measured |
| Legal Research Bench (Vals AI) | 49.0% | Not measured | Only GLM 5.3 measured |
| Harvey's Legal Agent Benchmark (Vals AI) | 14.6% | Not measured | Only GLM 5.3 measured |
| Excel Modeling Benchmark (Vals AI) | 56.3% | Not measured | Only GLM 5.3 measured |
| Terminal-Bench-Science (Vals AI) | 5.7% | Not measured | Only GLM 5.3 measured |
| τ²-Bench Telecom | Not measured | 47.1% | Only GPT-4.1 measured |
| APEX-Agents | 56.6% | Not measured | Only GLM 5.3 measured |
| Vending-Bench 2 | $8,164 | Not measured | Only GLM 5.3 measured |
| Berkeley Function Calling Leaderboard (BFCL) | Not measured | 54.0% | Only GPT-4.1 measured |
| BrowseComp-Plus | Not measured | 36.4% | Only GPT-4.1 measured |
| Neither model has a result on the other 22 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index80
| Benchmark | GLM 5.3 | GPT-4.1 | Leader |
|---|---|---|---|
| GPQA Diamond | 88.1% | 65.4% | GLM 5.3 |
| LiveBench reasoning | 85.8% | Not measured | Only GLM 5.3 measured |
| MysteryMechanism (Vals AI) | 23.0% | Not measured | Only GLM 5.3 measured |
| MMMU Pro (Vals AI) | Not measured | 72.4% | Only GPT-4.1 measured |
| GPQA Diamond (Artificial Analysis run) | 91.7% | 66.6% | GLM 5.3 |
| Humanity's Last Exam (Artificial Analysis run) | 42.3% | 4.2% | GLM 5.3 |
| GPQA Diamond (Epoch AI run) | 90.9% | 66.9% | GLM 5.3 |
| 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 | 37.0% | 3.0% | GLM 5.3 |
| Mystery game puzzles | 33.0% | Not measured | Only GLM 5.3 measured |
| LMArena Text: hard prompts | 1490 | 1384 | GLM 5.3 |
| LMArena Text: expert | 1514 | 1364 | GLM 5.3 |
| LMArena Text: hard prompts in English | 1496 | 1393 | GLM 5.3 |
| LMArena Vision: diagrams | Not measured | 1215 | Only GPT-4.1 measured |
| LMArena Vision: homework | Not measured | 1248 | Only GPT-4.1 measured |
| Kagi LLM Benchmark | Not measured | 52.3% | Only GPT-4.1 measured |
| Neither model has a result on the other 4 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | GLM 5.3 | GPT-4.1 | Leader |
|---|---|---|---|
| AIME | Not measured | 39.6% | Only GPT-4.1 measured |
| MATH 500 (Vals AI) | Not measured | 87.2% | Only GPT-4.1 measured |
| MGSM (Vals AI) | Not measured | 87.7% | Only GPT-4.1 measured |
| AA Math Index | Not measured | 34.7 | Only GPT-4.1 measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 34.7% | Only GPT-4.1 measured |
| OTIS Mock AIME | 91.1% | 38.3% | GLM 5.3 |
| FrontierMath, tiers 1 to 3 | 68.8% | 6.0% | GLM 5.3 |
| FrontierMath, tier 4 | 29.3% | Not measured | Only GLM 5.3 measured |
| ProofBench | 49.0% | Not measured | Only GLM 5.3 measured |
| MATH level 5 | Not measured | 83.0% | Only GPT-4.1 measured |
| LMArena Text: math | 1495 | 1369 | GLM 5.3 |
| LMArena Text: mathematical work | 1497 | 1360 | GLM 5.3 |
| Neither model has a result on the other 1 board here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index71
| Benchmark | GLM 5.3 | GPT-4.1 | Leader |
|---|---|---|---|
| MMLU Pro | 86.8% | 80.5% | GLM 5.3 |
| LegalBench (Vals AI) | 84.8% | 83.1% | GLM 5.3 |
| CaseLaw (Vals AI) | Not measured | 69.9% | Only GPT-4.1 measured |
| CorpFin (Vals AI) | Not measured | 63.0% | Only GPT-4.1 measured |
| TaxEval (Vals AI) | 72.4% | 75.1% | GPT-4.1 |
| MortgageTax (Vals AI) | Not measured | 65.9% | Only GPT-4.1 measured |
| MedQA (Vals AI) | Not measured | 91.2% | Only GPT-4.1 measured |
| MedCode (Vals AI) | 42.9% | Not measured | Only GLM 5.3 measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 80.6% | Only GPT-4.1 measured |
| SimpleQA Verified | 41.0% | 31.1% | GLM 5.3 |
| GeoBench | Not measured | 72.0% | Only GPT-4.1 measured |
| LMArena Text: science | 1498 | 1382 | GLM 5.3 |
| LMArena Text: medicine and healthcare | 1481 | 1379 | GLM 5.3 |
| LMArena Text: legal and government | 1473 | 1399 | GLM 5.3 |
| LMArena Text: business and finance | 1459 | 1369 | GLM 5.3 |
| FACTS Benchmark Suite | Not measured | 45.8% | Only GPT-4.1 measured |
| Neither model has a result on the other 2 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | GLM 5.3 | GPT-4.1 | Leader |
|---|---|---|---|
| Design Arena: websites | 1295 | 1039 | GLM 5.3 |
| Design Arena: UI components | 1314 | 1000 | GLM 5.3 |
| Design Arena: game dev | 1311 | 1080 | GLM 5.3 |
| Design Arena: data viz | 1259 | 1104 | GLM 5.3 |
| Design Arena: 3D | 1332 | 850 | GLM 5.3 |
| Design Arena: all code categories | 1304 | 1026 | GLM 5.3 |
| Design Arena: SVG | 1293 | Not measured | Only GLM 5.3 measured |
| Design Arena: ASCII art | 1213 | Not measured | Only GLM 5.3 measured |
| LiveBench design | 77.3% | Not measured | Only GLM 5.3 measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index200
| Benchmark | GLM 5.3 | GPT-4.1 | Leader |
|---|---|---|---|
| Vals Index | 59.7% | Not measured | Only GLM 5.3 measured |
| LiveBench data analysis | 70.2% | Not measured | Only GLM 5.3 measured |
| Public Benefits Bench (Vals AI) | 68.5% | Not measured | Only GLM 5.3 measured |
| MedScribe (Vals AI) | 88.8% | Not measured | Only GLM 5.3 measured |
| IFBench | Not measured | 43.0% | Only GPT-4.1 measured |
| Long context reasoning (AA-LCR) | 79.7% | 68.3% | GLM 5.3 |
| DTBench | 87.7% | 68.3% | GLM 5.3 |
| LMCA | 55.5% | 25.6% | GLM 5.3 |
| Fiction.LiveBench | Not measured | 63.9% | Only GPT-4.1 measured |
| LMArena Text | 1474 | 1383 | GLM 5.3 |
| LMArena Text: instruction following | 1479 | 1367 | GLM 5.3 |
| LMArena Text: creative writing | 1458 | 1363 | GLM 5.3 |
| LMArena Vision | Not measured | 1210 | Only GPT-4.1 measured |
| LMArena Text: English | 1482 | 1392 | GLM 5.3 |
| LMArena Text: non-English | 1459 | 1370 | GLM 5.3 |
| LMArena Text: Chinese | 1532 | 1382 | GLM 5.3 |
| LMArena Text: German | 1488 | 1381 | GLM 5.3 |
| LMArena Text: Japanese | 1460 | 1319 | GLM 5.3 |
| LMArena Text: Korean | 1460 | 1338 | GLM 5.3 |
| LMArena Text: French | 1503 | 1382 | GLM 5.3 |
| LMArena Text: Spanish | 1462 | 1376 | GLM 5.3 |
| LMArena Text: Russian | 1469 | 1377 | GLM 5.3 |
| LMArena Text: Polish | 1466 | 1389 | GLM 5.3 |
| LMArena Text: multi-turn | 1472 | 1397 | GLM 5.3 |
| LMArena Text: longer queries | 1483 | 1385 | GLM 5.3 |
| LMArena Text: writing and language | 1469 | 1371 | GLM 5.3 |
| LMArena Text: entertainment and media | 1447 | 1365 | GLM 5.3 |
| 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 12 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 5.3 | 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 5.3 costs less
Repository review
50K input + 3K output tokens
GLM 5.3 costs less
Agent loop
200K input + 10K output tokens
GLM 5.3 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 5.3 | OpenAI GPT-4.1 |
|---|---|---|
| Maker | Z.ai | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | August 18, 2026 | April 14, 2025 |
| Context window | 1.05M | 1.05M |
| Input $/Mtok | $1.40 | $2.00 |
| Output $/Mtok | $4.40 | $8.00 |
| Coding index | 70.6 | Not benchmarked |
| Independent measurements behind it | 6 | 0 |
| Cost per solved task | $0.36 | $0.37 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 5.3 better than GPT-4.1 for coding?
Only GLM 5.3 has a coding score, 70.6 on the Vibe Coding Index; GPT-4.1 has none, so the two cannot be ranked on coding. Pick GPT-4.1 for work measured by TaxEval (Vals AI), where it leads.
Which is cheaper, GLM 5.3 or GPT-4.1?
GLM 5.3 costs $1.40 per million input tokens and $4.40 per million output tokens; GPT-4.1 costs $2.00 and $8.00. Per solved SWE-bench Verified task, GLM 5.3 costs $0.36 and GPT-4.1 $0.37.
Which has the bigger context window, GLM 5.3 or GPT-4.1?
GLM 5.3, with 1.05M tokens against 1.05M.
How many benchmarks is the GLM 5.3 vs GPT-4.1 comparison based on?
53 benchmarks have a result for both models as of October 21, 2026. GLM 5.3's coding index rests on 6 independent benchmarks and GPT-4.1's on 0. Where only one model has a result, the page says so instead of naming a winner.