GLM 5.3 vs GPT-5 Mini
GLM 5.3 leads GPT-5 Mini on coding, 70.6 to 34.2 on the Vibe Coding Index, and GPT-5 Mini costs 2.2x less per output token ($2.00 vs $4.40 per million). Updated October 21, 2026; 63 benchmarks have a result for both.
GLM 5.3 or GPT-5 Mini: which to pick
Pick GLM 5.3 for coding: it scores 70.6 on the Vibe Coding Index against 34.2 for GPT-5 Mini.
Pick GPT-5 Mini for output tokens 2.2x cheaper ($2.00 vs $4.40 per million) and a lower cost per solved task ($0.08 against $0.36).
- Shared results
- 63
- GLM 5.3 only
- 37
- GPT-5 Mini only
- 38
- Like-for-like categories
- 8 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 34.2.
6 and 2 benchmarks behind the scores
Intelligence
AA Intelligence Index
GLM 5.3
GLM 5.3 leads, 44.8 against 20.6.
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 80.3%.
Same board, both measured
Math
AIME
No call
Only GPT-5 Mini has a AIME result, 91.5%.
Directional only
Knowledge
MMLU Pro
GLM 5.3
GLM 5.3 leads, 86.8% against 82.2%.
Same board, both measured
Web design
Design Arena: websites
GLM 5.3
GLM 5.3 leads, 1295 against 1125.
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 400K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-5 Mini
GLM 5.3 $0.0036, GPT-5 Mini $0.0013 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GPT-5 Mini
GLM 5.3 $0.36, GPT-5 Mini $0.08.
Published attempt cost and pass rate
At a glance
- Coding index
- GLM 5.3 70.6 from 6 benchmarks, GPT-5 Mini 34.2 from 2 benchmarks.
- Widest category
- Agentic work, 51.0 points apart on average over 1 shared benchmark.
- Widest single gap
- Vibe Code Bench (Vals AI): GLM 5.3 78.1% against GPT-5 Mini 14.2%, 64.0 points apart on a 0 to 100 scale.
- Output price
- GLM 5.3 $4.40 against GPT-5 Mini $2.00 per million output tokens, 2.2x apart.
- Context window
- GLM 5.3 takes 1.05M tokens, GPT-5 Mini 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 Mini | Ties |
|---|---|---|---|---|
| Coding | 6 | 1 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 9 | 0 | ||
| Math | 6 | 0 | ||
| Knowledge | 7 | 2 | ||
| Web design | 8 | 0 | ||
| General | 21 | 0 | ||
| All shared boards | 60 | 3 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index61
| Benchmark | GLM 5.3 | GPT-5 Mini | Leader |
|---|---|---|---|
| SWE-bench Verified | Not measured | 59.8% | Only GPT-5 Mini measured |
| SWE-bench Multilingual | Not measured | 39.7% | Only GPT-5 Mini 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% | 86.6% | GPT-5 Mini |
| SWE-bench Verified (Vals AI) | 95.4% | 60.8% | GLM 5.3 |
| Terminal-Bench 4 (Vals AI) | 38.9% | Not measured | Only GLM 5.3 measured |
| Vibe Code Bench (Vals AI) | 78.1% | 14.2% | GLM 5.3 |
| 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 | 83.8% | Only GPT-5 Mini measured |
| SciCode | 59.0% | 39.0% | GLM 5.3 |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 33.3% | Only GPT-5 Mini measured |
| Terminal-Bench | Not measured | 34.8% | Only GPT-5 Mini measured |
| FrontierCode | 40.1% | Not measured | Only GLM 5.3 measured |
| FrontierSWE | 30.2% | Not measured | Only GLM 5.3 measured |
| WeirdML | 75.4% | 52.7% | GLM 5.3 |
| ALE-Bench | 1317.4 | Not measured | Only GLM 5.3 measured |
| LMArena Text: coding | 1497 | 1406 | GLM 5.3 |
| LMArena Text: software and IT | 1493 | 1394 | 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 11 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GLM 5.3 | GPT-5 Mini | Leader |
|---|---|---|---|
| AA Intelligence Index | 44.8 | 20.6 | GLM 5.3 |
| Epoch Capabilities Index | 156.0 | 145.3 | GLM 5.3 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 5.3 | GPT-5 Mini | 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 | 71.1% | Only GPT-5 Mini measured |
| APEX-Agents | 56.6% | Not measured | Only GLM 5.3 measured |
| Vending-Bench 2 | $8,164 | -$31 | GLM 5.3 |
| Berkeley Function Calling Leaderboard (BFCL) | Not measured | 55.5% | Only GPT-5 Mini measured |
| Neither model has a result on the other 23 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index90
| Benchmark | GLM 5.3 | GPT-5 Mini | Leader |
|---|---|---|---|
| GPQA Diamond | 88.1% | 80.3% | 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 | 78.9% | Only GPT-5 Mini measured |
| GPQA Diamond (Artificial Analysis run) | 91.7% | 82.8% | GLM 5.3 |
| Humanity's Last Exam (Artificial Analysis run) | 42.3% | 21.5% | GLM 5.3 |
| GPQA Diamond (Epoch AI run) | 90.9% | 75.0% | GLM 5.3 |
| ARC-AGI | Not measured | 54.3% | Only GPT-5 Mini measured |
| ARC-AGI-2 | Not measured | 4.4% | Only GPT-5 Mini measured |
| Chess puzzles | 37.0% | 24.0% | GLM 5.3 |
| Mystery game puzzles | 33.0% | 10.0% | GLM 5.3 |
| VPCT | Not measured | 40.2% | Only GPT-5 Mini measured |
| LMArena Text: hard prompts | 1490 | 1380 | GLM 5.3 |
| LMArena Text: expert | 1514 | 1379 | GLM 5.3 |
| LMArena Text: hard prompts in English | 1496 | 1383 | GLM 5.3 |
| LMArena Vision: diagrams | Not measured | 1202 | Only GPT-5 Mini measured |
| LMArena Vision: homework | Not measured | 1210 | Only GPT-5 Mini measured |
| Kagi LLM Benchmark | Not measured | 70.3% | Only GPT-5 Mini measured |
| Neither model has a result on the other 4 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | GLM 5.3 | GPT-5 Mini | Leader |
|---|---|---|---|
| AIME | Not measured | 91.5% | Only GPT-5 Mini measured |
| MATH 500 (Vals AI) | Not measured | 94.8% | Only GPT-5 Mini measured |
| MGSM (Vals AI) | Not measured | 92.6% | Only GPT-5 Mini measured |
| AA Math Index | Not measured | 90.7 | Only GPT-5 Mini measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 90.7% | Only GPT-5 Mini measured |
| OTIS Mock AIME | 91.1% | 86.7% | GLM 5.3 |
| FrontierMath, tiers 1 to 3 | 68.8% | 46.7% | GLM 5.3 |
| FrontierMath, tier 4 | 29.3% | 12.2% | GLM 5.3 |
| ProofBench | 49.0% | 9.0% | GLM 5.3 |
| MATH level 5 | Not measured | 97.8% | Only GPT-5 Mini measured |
| LMArena Text: math | 1495 | 1378 | GLM 5.3 |
| LMArena Text: mathematical work | 1497 | 1380 | 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 index72
| Benchmark | GLM 5.3 | GPT-5 Mini | Leader |
|---|---|---|---|
| MMLU Pro | 86.8% | 82.2% | GLM 5.3 |
| LegalBench (Vals AI) | 84.8% | 81.8% | GLM 5.3 |
| CaseLaw (Vals AI) | Not measured | 68.5% | Only GPT-5 Mini measured |
| CorpFin (Vals AI) | Not measured | 60.2% | Only GPT-5 Mini measured |
| TaxEval (Vals AI) | 72.4% | 75.2% | GPT-5 Mini |
| MortgageTax (Vals AI) | Not measured | 66.9% | Only GPT-5 Mini measured |
| MedQA (Vals AI) | Not measured | 96.1% | Only GPT-5 Mini measured |
| MedCode (Vals AI) | 42.9% | 43.0% | GPT-5 Mini |
| MMLU Pro (Artificial Analysis run) | Not measured | 83.7% | Only GPT-5 Mini measured |
| SimpleQA Verified | 41.0% | 21.6% | GLM 5.3 |
| LMArena Text: science | 1498 | 1371 | GLM 5.3 |
| LMArena Text: medicine and healthcare | 1481 | 1380 | GLM 5.3 |
| LMArena Text: legal and government | 1473 | 1375 | GLM 5.3 |
| LMArena Text: business and finance | 1459 | 1358 | GLM 5.3 |
| FACTS Benchmark Suite | Not measured | 38.6% | Only GPT-5 Mini measured |
| Neither model has a result on the other 3 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index80
| Benchmark | GLM 5.3 | GPT-5 Mini | Leader |
|---|---|---|---|
| Design Arena: websites | 1295 | 1125 | GLM 5.3 |
| Design Arena: UI components | 1314 | 1106 | GLM 5.3 |
| Design Arena: game dev | 1311 | 1130 | GLM 5.3 |
| Design Arena: data viz | 1259 | 1130 | GLM 5.3 |
| Design Arena: 3D | 1332 | 1037 | GLM 5.3 |
| Design Arena: all code categories | 1304 | 1115 | GLM 5.3 |
| Design Arena: SVG | 1293 | 1091 | GLM 5.3 |
| Design Arena: ASCII art | 1213 | 1125 | GLM 5.3 |
| 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 Mini | 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 | 43.0% | Only GPT-5 Mini measured |
| MedScribe (Vals AI) | 88.8% | 80.6% | GLM 5.3 |
| IFBench | Not measured | 75.4% | Only GPT-5 Mini measured |
| Long context reasoning (AA-LCR) | 79.7% | 72.3% | GLM 5.3 |
| DTBench | 87.7% | 80.5% | GLM 5.3 |
| LMCA | 55.5% | 34.2% | GLM 5.3 |
| Fiction.LiveBench | Not measured | 69.4% | Only GPT-5 Mini measured |
| Creative writing (Lech Mazur) | Not measured | 83.1% | Only GPT-5 Mini measured |
| LMArena Text | 1474 | 1373 | GLM 5.3 |
| LMArena Text: instruction following | 1479 | 1357 | GLM 5.3 |
| LMArena Text: creative writing | 1458 | 1325 | GLM 5.3 |
| LMArena Vision | Not measured | 1202 | Only GPT-5 Mini measured |
| LMArena Text: English | 1482 | 1379 | GLM 5.3 |
| LMArena Text: non-English | 1459 | 1363 | GLM 5.3 |
| LMArena Text: Chinese | 1532 | 1386 | GLM 5.3 |
| LMArena Text: German | 1488 | 1366 | GLM 5.3 |
| LMArena Text: Japanese | 1460 | 1341 | GLM 5.3 |
| LMArena Text: Korean | 1460 | 1308 | GLM 5.3 |
| LMArena Text: French | 1503 | 1386 | GLM 5.3 |
| LMArena Text: Spanish | 1462 | 1354 | GLM 5.3 |
| LMArena Text: Russian | 1469 | 1362 | GLM 5.3 |
| LMArena Text: Polish | 1466 | 1378 | GLM 5.3 |
| LMArena Text: multi-turn | 1472 | 1363 | GLM 5.3 |
| LMArena Text: longer queries | 1483 | 1355 | GLM 5.3 |
| LMArena Text: writing and language | 1469 | 1344 | GLM 5.3 |
| LMArena Text: entertainment and media | 1447 | 1340 | GLM 5.3 |
| LMArena Vision: English | Not measured | 1216 | Only GPT-5 Mini measured |
| LMArena Vision: Chinese | Not measured | 1214 | Only GPT-5 Mini measured |
| LMArena Vision: reading text | Not measured | 1199 | Only GPT-5 Mini measured |
| LMArena Vision: humor | Not measured | 1205 | Only GPT-5 Mini measured |
| LMArena Vision: captioning | Not measured | 1217 | Only GPT-5 Mini measured |
| LMArena Vision: recognizing things | Not measured | 1226 | Only GPT-5 Mini measured |
| LMArena Vision: creative writing | Not measured | 1181 | Only GPT-5 Mini measured |
| Neither model has a result on the other 10 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 5.3 | GPT-5 Mini | 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 Mini costs less
Repository review
50K input + 3K output tokens
GPT-5 Mini costs less
Agent loop
200K input + 10K output tokens
GPT-5 Mini 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 Mini |
|---|---|---|
| Maker | Z.ai | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | August 18, 2026 | August 7, 2025 |
| Context window | 1.05M | 400K |
| Input $/Mtok | $1.40 | $0.25 |
| Output $/Mtok | $4.40 | $2.00 |
| Coding index | 70.6 | 34.2 |
| Independent measurements behind it | 6 | 2 |
| Cost per solved task | $0.36 | $0.08 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 5.3 better than GPT-5 Mini for coding?
Pick GLM 5.3 for coding: it scores 70.6 on the Vibe Coding Index against 34.2 for GPT-5 Mini. Pick GPT-5 Mini for output tokens 2.2x cheaper ($2.00 vs $4.40 per million) and a lower cost per solved task ($0.08 against $0.36).
Which is cheaper, GLM 5.3 or GPT-5 Mini?
GLM 5.3 costs $1.40 per million input tokens and $4.40 per million output tokens; GPT-5 Mini costs $0.25 and $2.00. Per solved SWE-bench Verified task, GLM 5.3 costs $0.36 and GPT-5 Mini $0.08.
Which has the bigger context window, GLM 5.3 or GPT-5 Mini?
GLM 5.3, with 1.05M tokens against 400K.
How many benchmarks is the GLM 5.3 vs GPT-5 Mini comparison based on?
63 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-5 Mini's on 2. Where only one model has a result, the page says so instead of naming a winner.