GLM 5.3 Flash vs GPT-5 Nano
GLM 5.3 Flash leads GPT-5 Nano on the Artificial Analysis Intelligence Index, 41.8 to 13.0, and GPT-5 Nano costs 1.3x less per output token ($0.40 vs $0.50 per million). Updated October 21, 2026; 61 benchmarks have a result for both.
GLM 5.3 Flash or GPT-5 Nano: which to pick
Only GLM 5.3 Flash has a coding score, 57.7 on the Vibe Coding Index; GPT-5 Nano has none, so the two cannot be ranked on coding.
Pick GPT-5 Nano for output tokens 1.3x cheaper ($0.40 vs $0.50 per million) and work measured by Chess puzzles or Mystery game puzzles, where it leads.
- Shared results
- 61
- GLM 5.3 Flash only
- 41
- GPT-5 Nano only
- 26
- 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 Flash has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
GLM 5.3 Flash
GLM 5.3 Flash leads, 41.8 against 13.0.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only GLM 5.3 Flash has a AA Agentic Index result, 50.9.
Directional only
Reasoning
GPQA Diamond
GLM 5.3 Flash
GLM 5.3 Flash leads, 86.4% against 63.4%.
Same board, both measured
Math
AIME
No call
Only GPT-5 Nano has a AIME result, 81.2%.
Directional only
Knowledge
MMLU Pro
GLM 5.3 Flash
GLM 5.3 Flash leads, 86.1% against 76.1%.
Same board, both measured
Web design
Design Arena: websites
GLM 5.3 Flash
GLM 5.3 Flash leads, 1272 against 1101.
Same board, both measured
General
Vals Index
No call
Only GLM 5.3 Flash has a Vals Index result, 50.9%.
Directional only
Long documents
Listed context window
GLM 5.3 Flash
GLM 5.3 Flash takes 1.05M tokens against 400K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-5 Nano
GLM 5.3 Flash $0.0004, GPT-5 Nano $0.0003 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GLM 5.3 Flash
GLM 5.3 Flash $0.02, GPT-5 Nano $0.109.
Published attempt cost and pass rate
At a glance
- Coding index
- GLM 5.3 Flash 57.7 from 6 benchmarks, GPT-5 Nano not benchmarked.
- Widest category
- Web design, 29.3 points apart on average over 6 shared benchmarks.
- Widest single gap
- ARC-AGI: GLM 5.3 Flash 91.0% against GPT-5 Nano 20.7%, 70.3 points apart on a 0 to 100 scale.
- Output price
- GLM 5.3 Flash $0.50 against GPT-5 Nano $0.40 per million output tokens, 1.3x apart.
- Context window
- GLM 5.3 Flash takes 1.05M tokens, GPT-5 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 Flash | Bars | GPT-5 Nano | Ties |
|---|---|---|---|---|
| Coding | 3 | 0 | ||
| Intelligence | 2 | 0 | ||
| Reasoning | 12 | 2 | ||
| Math | 6 | 0 | ||
| Knowledge | 7 | 0 | ||
| Web design | 6 | 0 | ||
| General | 23 | 0 | ||
| All shared boards | 59 | 2 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index30
| Benchmark | GLM 5.3 Flash | GPT-5 Nano | Leader |
|---|---|---|---|
| SWE-bench Verified | Not measured | 34.8% | Only GPT-5 Nano measured |
| LiveBench coding | 80.6% | Not measured | Only GLM 5.3 Flash measured |
| LMArena WebDev | 1607 | Not measured | Only GLM 5.3 Flash measured |
| AA Coding Index | 71.5 | Not measured | Only GLM 5.3 Flash measured |
| LiveCodeBench (Vals AI) | 80.5% | 70.2% | GLM 5.3 Flash |
| SWE-bench Verified (Vals AI) | 92.0% | Not measured | Only GLM 5.3 Flash measured |
| Terminal-Bench 4 (Vals AI) | 37.4% | Not measured | Only GLM 5.3 Flash measured |
| Vibe Code Bench (Vals AI) | 30.8% | Not measured | Only GLM 5.3 Flash measured |
| Code migration (Vals AI) | 20.5% | Not measured | Only GLM 5.3 Flash measured |
| ProgramBench (Vals AI) | 0.0% | Not measured | Only GLM 5.3 Flash measured |
| IOI (Vals AI) | 52.5% | Not measured | Only GLM 5.3 Flash measured |
| Terminal-Bench 2.1 (Vals AI) | 62.9% | Not measured | Only GLM 5.3 Flash measured |
| Vibe Code Bench 1-100 (Vals AI) | 16.0% | Not measured | Only GLM 5.3 Flash measured |
| SkillsBench (Vals AI) | 40.2% | Not measured | Only GLM 5.3 Flash measured |
| CyberBench (Vals AI) | 67.9% | Not measured | Only GLM 5.3 Flash measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 78.9% | Only GPT-5 Nano measured |
| SciCode | 51.6% | Not measured | Only GLM 5.3 Flash measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 17.4% | Only GPT-5 Nano measured |
| Terminal-Bench | Not measured | 21.8% | Only GPT-5 Nano measured |
| FrontierCode | 31.8% | Not measured | Only GLM 5.3 Flash measured |
| FrontierSWE | 18.1% | Not measured | Only GLM 5.3 Flash measured |
| WeirdML | Not measured | 38.1% | Only GPT-5 Nano measured |
| ALE-Bench | 303.6 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: coding | 1507 | 1351 | GLM 5.3 Flash |
| LMArena Text: software and IT | 1500 | 1345 | GLM 5.3 Flash |
| LMArena WebDev: React | 1624 | Not measured | Only GLM 5.3 Flash measured |
| LMArena WebDev: HTML | 1577 | Not measured | Only GLM 5.3 Flash measured |
| LMArena WebDev: from an image | 1578 | Not measured | Only GLM 5.3 Flash measured |
| Neither model has a result on the other 11 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GLM 5.3 Flash | GPT-5 Nano | Leader |
|---|---|---|---|
| AA Intelligence Index | 41.8 | 13.0 | GLM 5.3 Flash |
| Epoch Capabilities Index | 153.1 | 139.2 | GLM 5.3 Flash |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index14 boards, none shared
| Benchmark | GLM 5.3 Flash | GPT-5 Nano | Leader |
|---|---|---|---|
| AA Agentic Index | 50.9 | Not measured | Only GLM 5.3 Flash measured |
| LiveBench research | 70.1% | Not measured | Only GLM 5.3 Flash measured |
| LiveBench browser | 84.0% | Not measured | Only GLM 5.3 Flash measured |
| LiveBench long agentic | 28.4% | Not measured | Only GLM 5.3 Flash measured |
| SREBench (Vals AI) | 0.8% | Not measured | Only GLM 5.3 Flash measured |
| Finance Agent (Vals AI) | 57.9% | Not measured | Only GLM 5.3 Flash measured |
| Tax Agent Bench (Vals AI) | 31.6% | Not measured | Only GLM 5.3 Flash measured |
| Legal Research Bench (Vals AI) | 45.2% | Not measured | Only GLM 5.3 Flash measured |
| Harvey's Legal Agent Benchmark (Vals AI) | 10.4% | Not measured | Only GLM 5.3 Flash measured |
| Excel Modeling Benchmark (Vals AI) | 55.9% | Not measured | Only GLM 5.3 Flash measured |
| Terminal-Bench-Science (Vals AI) | 2.9% | Not measured | Only GLM 5.3 Flash measured |
| τ²-Bench Telecom | Not measured | 36.5% | Only GPT-5 Nano measured |
| APEX-Agents | 52.8% | Not measured | Only GLM 5.3 Flash measured |
| Berkeley Function Calling Leaderboard (BFCL) | Not measured | 51.4% | Only GPT-5 Nano measured |
| Neither model has a result on the other 27 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index122
| Benchmark | GLM 5.3 Flash | GPT-5 Nano | Leader |
|---|---|---|---|
| GPQA Diamond | 86.4% | 63.4% | GLM 5.3 Flash |
| LiveBench reasoning | 77.6% | Not measured | Only GLM 5.3 Flash measured |
| MMMU Pro (Vals AI) | 86.0% | 70.9% | GLM 5.3 Flash |
| GPQA Diamond (Artificial Analysis run) | 91.2% | 67.6% | GLM 5.3 Flash |
| Humanity's Last Exam (Artificial Analysis run) | 39.9% | 9.5% | GLM 5.3 Flash |
| GPQA Diamond (Epoch AI run) | 90.2% | 69.4% | GLM 5.3 Flash |
| ARC-AGI | 91.0% | 20.7% | GLM 5.3 Flash |
| ARC-AGI-2 | 65.8% | 2.6% | GLM 5.3 Flash |
| Chess puzzles | 21.0% | 22.0% | GPT-5 Nano |
| Mystery game puzzles | 8.0% | 9.0% | GPT-5 Nano |
| VPCT | Not measured | 37.2% | Only GPT-5 Nano measured |
| LMArena Text: hard prompts | 1490 | 1328 | GLM 5.3 Flash |
| LMArena Text: expert | 1511 | 1321 | GLM 5.3 Flash |
| LMArena Text: hard prompts in English | 1488 | 1338 | GLM 5.3 Flash |
| LMArena Vision: diagrams | 1317 | 1141 | GLM 5.3 Flash |
| LMArena Vision: homework | 1318 | 1157 | GLM 5.3 Flash |
| Kagi LLM Benchmark | Not measured | 62.2% | Only GPT-5 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 Flash | GPT-5 Nano | Leader |
|---|---|---|---|
| AIME | Not measured | 81.2% | Only GPT-5 Nano measured |
| MATH 500 (Vals AI) | Not measured | 93.8% | Only GPT-5 Nano measured |
| MGSM (Vals AI) | Not measured | 89.3% | Only GPT-5 Nano measured |
| AA Math Index | Not measured | 83.7 | Only GPT-5 Nano measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 83.7% | Only GPT-5 Nano measured |
| OTIS Mock AIME | 93.9% | 81.1% | GLM 5.3 Flash |
| FrontierMath, tiers 1 to 3 | 55.8% | 20.0% | GLM 5.3 Flash |
| FrontierMath, tier 4 | 17.1% | 2.4% | GLM 5.3 Flash |
| ProofBench | 21.0% | 12.0% | GLM 5.3 Flash |
| MATH level 5 | Not measured | 95.2% | Only GPT-5 Nano measured |
| LMArena Text: math | 1503 | 1317 | GLM 5.3 Flash |
| LMArena Text: mathematical work | 1497 | 1326 | GLM 5.3 Flash |
| Neither model has a result on the other 1 board here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index70
| Benchmark | GLM 5.3 Flash | GPT-5 Nano | Leader |
|---|---|---|---|
| MMLU Pro | 86.1% | 76.1% | GLM 5.3 Flash |
| LegalBench (Vals AI) | 83.9% | 50.1% | GLM 5.3 Flash |
| CaseLaw (Vals AI) | Not measured | 52.6% | Only GPT-5 Nano measured |
| TaxEval (Vals AI) | 75.6% | 67.4% | GLM 5.3 Flash |
| MortgageTax (Vals AI) | Not measured | 53.6% | Only GPT-5 Nano measured |
| MedQA (Vals AI) | Not measured | 93.3% | Only GPT-5 Nano measured |
| MedCode (Vals AI) | Not measured | 30.4% | Only GPT-5 Nano measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 78.0% | Only GPT-5 Nano measured |
| SimpleQA Verified | Not measured | 11.7% | Only GPT-5 Nano measured |
| LMArena Text: science | 1484 | 1318 | GLM 5.3 Flash |
| LMArena Text: medicine and healthcare | 1475 | 1335 | GLM 5.3 Flash |
| LMArena Text: legal and government | 1473 | 1298 | GLM 5.3 Flash |
| LMArena Text: business and finance | 1460 | 1311 | GLM 5.3 Flash |
| Neither model has a result on the other 5 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | GLM 5.3 Flash | GPT-5 Nano | Leader |
|---|---|---|---|
| Design Arena: websites | 1272 | 1101 | GLM 5.3 Flash |
| Design Arena: UI components | 1316 | 1067 | GLM 5.3 Flash |
| Design Arena: game dev | 1278 | 1045 | GLM 5.3 Flash |
| Design Arena: data viz | 1261 | 1056 | GLM 5.3 Flash |
| Design Arena: 3D | 1316 | 966 | GLM 5.3 Flash |
| Design Arena: all code categories | 1282 | 1084 | GLM 5.3 Flash |
| Design Arena: SVG | 1284 | Not measured | Only GLM 5.3 Flash measured |
| Design Arena: ASCII art | 1268 | Not measured | Only GLM 5.3 Flash measured |
| LiveBench design | 77.2% | Not measured | Only GLM 5.3 Flash measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index230
| Benchmark | GLM 5.3 Flash | GPT-5 Nano | Leader |
|---|---|---|---|
| Vals Index | 50.9% | Not measured | Only GLM 5.3 Flash measured |
| LiveBench data analysis | 76.4% | Not measured | Only GLM 5.3 Flash measured |
| SAGE (Vals AI) | Not measured | 30.4% | Only GPT-5 Nano measured |
| MedScribe (Vals AI) | 88.9% | 72.9% | GLM 5.3 Flash |
| IFBench | Not measured | 67.6% | Only GPT-5 Nano measured |
| Long context reasoning (AA-LCR) | 80.0% | 45.0% | GLM 5.3 Flash |
| DTBench | Not measured | 62.7% | Only GPT-5 Nano measured |
| LMCA | Not measured | 7.9% | Only GPT-5 Nano measured |
| Fiction.LiveBench | Not measured | 44.4% | Only GPT-5 Nano measured |
| Surface Evolver Bench | 52.5% | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text | 1471 | 1320 | GLM 5.3 Flash |
| LMArena Text: instruction following | 1478 | 1306 | GLM 5.3 Flash |
| LMArena Text: creative writing | 1441 | 1249 | GLM 5.3 Flash |
| LMArena Vision | 1294 | 1159 | GLM 5.3 Flash |
| LMArena Text: English | 1474 | 1321 | GLM 5.3 Flash |
| LMArena Text: non-English | 1462 | 1313 | GLM 5.3 Flash |
| LMArena Text: Chinese | 1531 | 1356 | GLM 5.3 Flash |
| LMArena Text: German | 1462 | 1327 | GLM 5.3 Flash |
| LMArena Text: Japanese | 1444 | 1227 | GLM 5.3 Flash |
| LMArena Text: Korean | 1436 | 1269 | GLM 5.3 Flash |
| LMArena Text: French | 1491 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: Spanish | 1475 | 1360 | GLM 5.3 Flash |
| LMArena Text: Russian | 1465 | 1296 | GLM 5.3 Flash |
| LMArena Text: Polish | 1450 | 1314 | GLM 5.3 Flash |
| LMArena Text: multi-turn | 1469 | 1311 | GLM 5.3 Flash |
| LMArena Text: longer queries | 1481 | 1312 | GLM 5.3 Flash |
| LMArena Text: writing and language | 1455 | 1283 | GLM 5.3 Flash |
| LMArena Text: entertainment and media | 1440 | 1268 | GLM 5.3 Flash |
| LMArena Vision: English | 1295 | 1170 | GLM 5.3 Flash |
| LMArena Vision: Chinese | 1363 | 1176 | GLM 5.3 Flash |
| LMArena Vision: reading text | 1299 | 1152 | GLM 5.3 Flash |
| LMArena Vision: humor | 1279 | 1110 | GLM 5.3 Flash |
| LMArena Vision: creative writing | 1270 | Not measured | Only GLM 5.3 Flash measured |
| Neither model has a result on the other 13 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 5.3 Flash | GPT-5 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 Nano costs less
Repository review
50K input + 3K output tokens
GPT-5 Nano costs less
Agent loop
200K input + 10K output tokens
GPT-5 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 Flash | OpenAI GPT-5 Nano |
|---|---|---|
| Maker | Z.ai | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | August 26, 2026 | August 7, 2025 |
| Context window | 1.05M | 400K |
| Input $/Mtok | $0.15 | $0.05 |
| Output $/Mtok | $0.50 | $0.40 |
| Coding index | 57.7 | Not benchmarked |
| Independent measurements behind it | 6 | 0 |
| Cost per solved task | $0.02 | $0.109 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 5.3 Flash better than GPT-5 Nano for coding?
Only GLM 5.3 Flash has a coding score, 57.7 on the Vibe Coding Index; GPT-5 Nano has none, so the two cannot be ranked on coding. Pick GPT-5 Nano for output tokens 1.3x cheaper ($0.40 vs $0.50 per million) and work measured by Chess puzzles or Mystery game puzzles, where it leads.
Which is cheaper, GLM 5.3 Flash or GPT-5 Nano?
GLM 5.3 Flash costs $0.15 per million input tokens and $0.50 per million output tokens; GPT-5 Nano costs $0.05 and $0.40. Per solved SWE-bench Verified task, GLM 5.3 Flash costs $0.02 and GPT-5 Nano $0.109.
Which has the bigger context window, GLM 5.3 Flash or GPT-5 Nano?
GLM 5.3 Flash, with 1.05M tokens against 400K.
How many benchmarks is the GLM 5.3 Flash vs GPT-5 Nano comparison based on?
61 benchmarks have a result for both models as of October 21, 2026. GLM 5.3 Flash's coding index rests on 6 independent benchmarks and GPT-5 Nano's on 0. Where only one model has a result, the page says so instead of naming a winner.