GLM 5.3 vs GPT-5.4 Nano
GLM 5.3 leads GPT-5.4 Nano on coding, 70.6 to 44.8 on the Vibe Coding Index, and GPT-5.4 Nano costs 3.5x less per output token ($1.25 vs $4.40 per million). Updated October 22, 2026; 63 benchmarks have a result for both.
GLM 5.3 or GPT-5.4 Nano: which to pick
Pick GLM 5.3 for coding: it scores 70.6 on the Vibe Coding Index against 44.8 for GPT-5.4 Nano.
Pick GPT-5.4 Nano for output tokens 3.5x cheaper ($1.25 vs $4.40 per million) and a lower cost per solved task ($0.14 against $0.36).
- Shared results
- 63
- GLM 5.3 only
- 37
- GPT-5.4 Nano only
- 22
- 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
GLM 5.3
GLM 5.3 scores 70.6 against 44.8.
6 and 2 benchmarks behind the scores
Intelligence
AA Intelligence Index
GLM 5.3
GLM 5.3 leads, 44.8 against 20.7.
Same board, both measured
Agentic work
AA Agentic Index
GLM 5.3
GLM 5.3 leads, 53.1 against 16.0.
Same board, both measured
Reasoning
GPQA Diamond
GLM 5.3
GLM 5.3 leads, 88.1% against 77.5%.
Same board, both measured
Math
AIME
No call
Only GPT-5.4 Nano has a AIME result, 88.8%.
Directional only
Knowledge
MMLU Pro
GLM 5.3
GLM 5.3 leads, 86.8% against 77.2%.
Same board, both measured
Web design
Design Arena: websites
No call
Only GLM 5.3 has a Design Arena: websites result, 1295.
Directional only
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 400K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-5.4 Nano
GLM 5.3 $0.0036, GPT-5.4 Nano $0.0008 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GPT-5.4 Nano
GLM 5.3 $0.36, GPT-5.4 Nano $0.14.
Published attempt cost and pass rate
At a glance
- Coding index
- GLM 5.3 70.6 from 6 benchmarks, GPT-5.4 Nano 44.8 from 2 benchmarks.
- Widest category
- Agentic work, 28.8 points apart on average over 5 shared benchmarks.
- Widest single gap
- Vibe Code Bench (Vals AI): GLM 5.3 78.1% against GPT-5.4 Nano 26.1%, 52.0 points apart on a 0 to 100 scale.
- Output price
- GLM 5.3 $4.40 against GPT-5.4 Nano $1.25 per million output tokens, 3.5x apart.
- Context window
- GLM 5.3 takes 1.05M tokens, GPT-5.4 Nano 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 5.3 | Bars | GPT-5.4 Nano | Ties |
|---|---|---|---|---|
| Coding | 10 | 1 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 5 | 0 | ||
| Reasoning | 8 | 1 | ||
| Math | 6 | 0 | ||
| Knowledge | 9 | 0 | ||
| General | 21 | 0 | ||
| All shared boards | 61 | 2 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index101
| Benchmark | GLM 5.3 | GPT-5.4 Nano | Leader |
|---|---|---|---|
| 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 | 56.1 | GLM 5.3 |
| LiveCodeBench (Vals AI) | 80.5% | 84.0% | GPT-5.4 Nano |
| SWE-bench Verified (Vals AI) | 95.4% | 69.8% | GLM 5.3 |
| Terminal-Bench 4 (Vals AI) | 38.9% | Not measured | Only GLM 5.3 measured |
| Vibe Code Bench (Vals AI) | 78.1% | 26.1% | GLM 5.3 |
| Code migration (Vals AI) | 44.2% | 14.5% | GLM 5.3 |
| 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% | 41.6% | GLM 5.3 |
| 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 |
| SciCode | 59.0% | 47.2% | GLM 5.3 |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 42.4% | Only GPT-5.4 Nano measured |
| FrontierCode | 40.1% | Not measured | Only GLM 5.3 measured |
| FrontierSWE | 30.2% | Not measured | Only GLM 5.3 measured |
| WeirdML | 75.4% | 49.2% | GLM 5.3 |
| ALE-Bench | 1317.4 | 1004.5 | GLM 5.3 |
| LMArena Text: coding | 1497 | 1404 | GLM 5.3 |
| LMArena Text: software and IT | 1493 | 1402 | 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 |
| Neither model has a result on the other 15 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GLM 5.3 | GPT-5.4 Nano | Leader |
|---|---|---|---|
| AA Intelligence Index | 44.8 | 20.7 | GLM 5.3 |
| Epoch Capabilities Index | 156.0 | 146.0 | GLM 5.3 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | GLM 5.3 | GPT-5.4 Nano | Leader |
|---|---|---|---|
| AA Agentic Index | 53.1 | 16.0 | GLM 5.3 |
| 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 | 1230 | 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% | 38.2% | GLM 5.3 |
| Tax Agent Bench (Vals AI) | 39.7% | 5.0% | GLM 5.3 |
| Legal Research Bench (Vals AI) | 49.0% | 6.3% | GLM 5.3 |
| Harvey's Legal Agent Benchmark (Vals AI) | 14.6% | Not measured | Only GLM 5.3 measured |
| Excel Modeling Benchmark (Vals AI) | 56.3% | 44.8% | GLM 5.3 |
| Terminal-Bench-Science (Vals AI) | 5.7% | Not measured | Only GLM 5.3 measured |
| τ²-Bench Telecom | Not measured | 76.0% | Only GPT-5.4 Nano measured |
| APEX-Agents | 56.6% | Not measured | Only GLM 5.3 measured |
| Vending-Bench 2 | $8,164 | Not measured | Only GLM 5.3 measured |
| Neither model has a result on the other 24 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index81
| Benchmark | GLM 5.3 | GPT-5.4 Nano | Leader |
|---|---|---|---|
| GPQA Diamond | 88.1% | 77.5% | 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 | 73.6% | Only GPT-5.4 Nano measured |
| GPQA Diamond (Artificial Analysis run) | 91.7% | 81.7% | GLM 5.3 |
| Humanity's Last Exam (Artificial Analysis run) | 42.3% | 28.3% | GLM 5.3 |
| GPQA Diamond (Epoch AI run) | 90.9% | 78.5% | GLM 5.3 |
| ARC-AGI | Not measured | 51.5% | Only GPT-5.4 Nano measured |
| ARC-AGI-2 | Not measured | 5.7% | Only GPT-5.4 Nano measured |
| Chess puzzles | 37.0% | 45.0% | GPT-5.4 Nano |
| Mystery game puzzles | 33.0% | 9.0% | GLM 5.3 |
| LMArena Text: hard prompts | 1490 | 1381 | GLM 5.3 |
| LMArena Text: expert | 1514 | 1396 | GLM 5.3 |
| LMArena Text: hard prompts in English | 1496 | 1387 | GLM 5.3 |
| LMArena Vision: diagrams | Not measured | 1210 | Only GPT-5.4 Nano measured |
| LMArena Vision: homework | Not measured | 1225 | Only GPT-5.4 Nano measured |
| Kagi LLM Benchmark | Not measured | 39.7% | Only GPT-5.4 Nano measured |
| Neither model has a result on the other 5 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | GLM 5.3 | GPT-5.4 Nano | Leader |
|---|---|---|---|
| AIME | Not measured | 88.8% | Only GPT-5.4 Nano measured |
| OTIS Mock AIME | 91.1% | 87.8% | GLM 5.3 |
| FrontierMath, tiers 1 to 3 | 68.8% | 44.9% | GLM 5.3 |
| FrontierMath, tier 4 | 29.3% | 12.2% | GLM 5.3 |
| ProofBench | 49.0% | 5.0% | GLM 5.3 |
| LMArena Text: math | 1495 | 1405 | GLM 5.3 |
| LMArena Text: mathematical work | 1497 | 1409 | GLM 5.3 |
| Neither model has a result on the other 6 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index90
| Benchmark | GLM 5.3 | GPT-5.4 Nano | Leader |
|---|---|---|---|
| MMLU Pro | 86.8% | 77.2% | GLM 5.3 |
| LegalBench (Vals AI) | 84.8% | 77.9% | GLM 5.3 |
| CaseLaw (Vals AI) | Not measured | 51.9% | Only GPT-5.4 Nano measured |
| CorpFin (Vals AI) | Not measured | 61.2% | Only GPT-5.4 Nano measured |
| TaxEval (Vals AI) | 72.4% | 67.4% | GLM 5.3 |
| MortgageTax (Vals AI) | Not measured | 59.1% | Only GPT-5.4 Nano measured |
| MedCode (Vals AI) | 42.9% | 41.0% | GLM 5.3 |
| SimpleQA Verified | 41.0% | 11.7% | GLM 5.3 |
| LMArena Text: science | 1498 | 1374 | GLM 5.3 |
| LMArena Text: medicine and healthcare | 1481 | 1369 | GLM 5.3 |
| LMArena Text: legal and government | 1473 | 1368 | GLM 5.3 |
| LMArena Text: business and finance | 1459 | 1366 | GLM 5.3 |
| Neither model has a result on the other 6 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
| Benchmark | GLM 5.3 | GPT-5.4 Nano | Leader |
|---|---|---|---|
| Design Arena: websites | 1295 | Not measured | Only GLM 5.3 measured |
| Design Arena: UI components | 1314 | Not measured | Only GLM 5.3 measured |
| Design Arena: game dev | 1311 | Not measured | Only GLM 5.3 measured |
| Design Arena: data viz | 1260 | Not measured | Only GLM 5.3 measured |
| Design Arena: 3D | 1332 | Not measured | Only GLM 5.3 measured |
| Design Arena: all code categories | 1304 | Not measured | Only GLM 5.3 measured |
| Design Arena: SVG | 1294 | Not measured | Only GLM 5.3 measured |
| Design Arena: ASCII art | 1208 | 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 index210
| Benchmark | GLM 5.3 | GPT-5.4 Nano | 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 |
| SAGE (Vals AI) | Not measured | 38.1% | Only GPT-5.4 Nano measured |
| MedScribe (Vals AI) | 88.8% | 77.1% | GLM 5.3 |
| Vals Multimodal Index | Not measured | 47.6% | Only GPT-5.4 Nano measured |
| IFBench | Not measured | 75.9% | Only GPT-5.4 Nano measured |
| Long context reasoning (AA-LCR) | 79.7% | 76.7% | GLM 5.3 |
| DTBench | 87.7% | 80.3% | GLM 5.3 |
| LMCA | 55.5% | 36.9% | GLM 5.3 |
| LMArena Text | 1474 | 1372 | GLM 5.3 |
| LMArena Text: instruction following | 1479 | 1362 | GLM 5.3 |
| LMArena Text: creative writing | 1458 | 1315 | GLM 5.3 |
| LMArena Vision | Not measured | 1196 | Only GPT-5.4 Nano measured |
| LMArena Text: English | 1482 | 1381 | GLM 5.3 |
| LMArena Text: non-English | 1459 | 1359 | GLM 5.3 |
| LMArena Text: Chinese | 1532 | 1391 | GLM 5.3 |
| LMArena Text: German | 1488 | 1368 | GLM 5.3 |
| LMArena Text: Japanese | 1460 | 1343 | GLM 5.3 |
| LMArena Text: Korean | 1460 | 1321 | GLM 5.3 |
| LMArena Text: French | 1503 | 1395 | GLM 5.3 |
| LMArena Text: Spanish | 1462 | 1371 | GLM 5.3 |
| LMArena Text: Russian | 1469 | 1363 | GLM 5.3 |
| LMArena Text: Polish | 1466 | 1366 | GLM 5.3 |
| LMArena Text: multi-turn | 1472 | 1382 | GLM 5.3 |
| LMArena Text: longer queries | 1483 | 1365 | GLM 5.3 |
| LMArena Text: writing and language | 1469 | 1338 | GLM 5.3 |
| LMArena Text: entertainment and media | 1447 | 1325 | GLM 5.3 |
| LMArena Vision: English | Not measured | 1202 | Only GPT-5.4 Nano measured |
| LMArena Vision: Chinese | Not measured | 1232 | Only GPT-5.4 Nano measured |
| LMArena Vision: reading text | Not measured | 1204 | Only GPT-5.4 Nano measured |
| LMArena Vision: humor | Not measured | 1161 | Only GPT-5.4 Nano measured |
| LMArena Vision: recognizing things | Not measured | 1141 | Only GPT-5.4 Nano measured |
| LMArena Vision: creative writing | Not measured | 1159 | Only GPT-5.4 Nano 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-5.4 Nano | 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
GPT-5.4 Nano costs less
Repository review
50K input + 3K output tokens
GPT-5.4 Nano costs less
Agent loop
200K input + 10K output tokens
GPT-5.4 Nano 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-5.4 Nano |
|---|---|---|
| Maker | Z.ai | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | August 18, 2026 | March 17, 2026 |
| Context window | 1.05M | 400K |
| Input $/Mtok | $1.40 | $0.20 |
| Output $/Mtok | $4.40 | $1.25 |
| Coding index | 70.6 | 44.8 |
| Independent measurements behind it | 6 | 2 |
| Cost per solved task | $0.36 | $0.14 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 5.3 better than GPT-5.4 Nano for coding?
Pick GLM 5.3 for coding: it scores 70.6 on the Vibe Coding Index against 44.8 for GPT-5.4 Nano. Pick GPT-5.4 Nano for output tokens 3.5x cheaper ($1.25 vs $4.40 per million) and a lower cost per solved task ($0.14 against $0.36).
Which is cheaper, GLM 5.3 or GPT-5.4 Nano?
GLM 5.3 costs $1.40 per million input tokens and $4.40 per million output tokens; GPT-5.4 Nano costs $0.20 and $1.25. Per solved SWE-bench Verified task, GLM 5.3 costs $0.36 and GPT-5.4 Nano $0.14.
Which has the bigger context window, GLM 5.3 or GPT-5.4 Nano?
GLM 5.3, with 1.05M tokens against 400K.
How many benchmarks is the GLM 5.3 vs GPT-5.4 Nano comparison based on?
63 benchmarks have a result for both models as of October 22, 2026. GLM 5.3's coding index rests on 6 independent benchmarks and GPT-5.4 Nano's on 2. Where only one model has a result, the page says so instead of naming a winner.