GLM 5.3 Flash vs MiniMax M3
GLM 5.3 Flash leads MiniMax M3 on coding, 57.7 to 39.2 on the Vibe Coding Index, and GLM 5.3 Flash costs 2.4x less per output token ($0.50 vs $1.20 per million). Updated October 22, 2026; 91 benchmarks have a result for both.
GLM 5.3 Flash or MiniMax M3: which to pick
Pick GLM 5.3 Flash for coding: it scores 57.7 on the Vibe Coding Index against 39.2 for MiniMax M3.
Pick MiniMax M3 for work measured by LiveCodeBench (Vals AI) or Vibe Code Bench (Vals AI), where it leads.
- Shared results
- 91
- GLM 5.3 Flash only
- 11
- MiniMax M3 only
- 25
- 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 Flash
GLM 5.3 Flash scores 57.7 against 39.2.
6 benchmarks behind each score
Intelligence
AA Intelligence Index
GLM 5.3 Flash
GLM 5.3 Flash leads, 41.8 against 29.2.
Same board, both measured
Agentic work
AA Agentic Index
GLM 5.3 Flash
GLM 5.3 Flash leads, 50.9 against 29.5.
Same board, both measured
Reasoning
GPQA Diamond
MiniMax M3
MiniMax M3 leads, 92.7% against 86.4%.
Same board, both measured
Knowledge
MMLU Pro
GLM 5.3 Flash
GLM 5.3 Flash leads, 86.1% against 84.2%.
Same board, both measured
Web design
Design Arena: websites
GLM 5.3 Flash
GLM 5.3 Flash leads, 1272 against 1257.
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
No call
Both take 1.05M tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 5.3 Flash
GLM 5.3 Flash $0.0004, MiniMax M3 $0.0009 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GLM 5.3 Flash
GLM 5.3 Flash $0.02, MiniMax M3 $0.56.
Published attempt cost and pass rate
At a glance
- Coding index
- GLM 5.3 Flash 57.7 from 6 benchmarks, MiniMax M3 39.2 from 6 benchmarks.
- Widest category
- Agentic work, 19.4 points apart on average over 9 shared benchmarks.
- Widest single gap
- LiveBench browser: GLM 5.3 Flash 84.0% against MiniMax M3 8.0%, 76.0 points apart on a 0 to 100 scale.
- Output price
- GLM 5.3 Flash $0.50 against MiniMax M3 $1.20 per million output tokens, 2.4x apart.
- Context window
- Both take 1.05M tokens.
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 | MiniMax M3 | Ties |
|---|---|---|---|---|
| Coding | 16 | 4 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 9 | 0 | ||
| Reasoning | 9 | 3 | 1 tie | |
| Math | 4 | 0 | ||
| Knowledge | 6 | 1 | ||
| Web design | 9 | 0 | ||
| General | 25 | 2 | ||
| All shared boards | 80 | 10 | 1 tie |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index164
| Benchmark | GLM 5.3 Flash | MiniMax M3 | Leader |
|---|---|---|---|
| LiveBench coding | 80.6% | 70.9% | GLM 5.3 Flash |
| LMArena WebDev | 1607 | 1482 | GLM 5.3 Flash |
| AA Coding Index | 71.5 | 58.6 | GLM 5.3 Flash |
| LiveCodeBench (Vals AI) | 80.5% | 82.2% | MiniMax M3 |
| SWE-bench Verified (Vals AI) | 92.0% | 75.0% | GLM 5.3 Flash |
| Terminal-Bench 4 (Vals AI) | 37.4% | 1.0% | GLM 5.3 Flash |
| Vibe Code Bench (Vals AI) | 30.8% | 47.6% | MiniMax M3 |
| Code migration (Vals AI) | 20.5% | 19.9% | GLM 5.3 Flash |
| 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% | 53.6% | GLM 5.3 Flash |
| SWE-bench Multimodal (Vals AI) | Not measured | 33.3% | Only MiniMax M3 measured |
| Vibe Code Bench 1-100 (Vals AI) | 16.0% | 9.2% | GLM 5.3 Flash |
| SkillsBench (Vals AI) | 40.2% | 51.5% | MiniMax M3 |
| CyberBench (Vals AI) | 67.9% | 65.0% | GLM 5.3 Flash |
| SciCode | 51.6% | 47.1% | GLM 5.3 Flash |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 42.4% | Only MiniMax M3 measured |
| FrontierCode | 31.8% | 14.7% | GLM 5.3 Flash |
| FrontierSWE | 18.1% | Not measured | Only GLM 5.3 Flash measured |
| ALE-Bench | 303.6 | 640.0 | MiniMax M3 |
| LMArena Text: coding | 1507 | 1470 | GLM 5.3 Flash |
| LMArena Text: software and IT | 1500 | 1466 | GLM 5.3 Flash |
| LMArena WebDev: React | 1624 | 1478 | GLM 5.3 Flash |
| LMArena WebDev: HTML | 1577 | 1455 | GLM 5.3 Flash |
| LMArena WebDev: from an image | 1578 | 1470 | GLM 5.3 Flash |
| Neither model has a result on the other 14 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GLM 5.3 Flash | MiniMax M3 | Leader |
|---|---|---|---|
| AA Intelligence Index | 41.8 | 29.2 | GLM 5.3 Flash |
| Epoch Capabilities Index | 153.1 | 146.9 | GLM 5.3 Flash |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index90
| Benchmark | GLM 5.3 Flash | MiniMax M3 | Leader |
|---|---|---|---|
| AA Agentic Index | 50.9 | 29.5 | GLM 5.3 Flash |
| Design Arena: agents building web apps | Not measured | 1200 | Only MiniMax M3 measured |
| Design Arena: agents building full-stack apps | Not measured | 1197 | Only MiniMax M3 measured |
| Design Arena: agents building mobile apps | Not measured | 1169 | Only MiniMax M3 measured |
| Design Arena: agents building Android apps | Not measured | 1159 | Only MiniMax M3 measured |
| Design Arena: agents building games | Not measured | 1158 | Only MiniMax M3 measured |
| Design Arena: HTML slides | Not measured | 1130 | Only MiniMax M3 measured |
| Design Arena: PowerPoint slides | Not measured | 1181 | Only MiniMax M3 measured |
| Design Arena: spreadsheets | Not measured | 1028 | Only MiniMax M3 measured |
| LiveBench research | 70.1% | 68.0% | GLM 5.3 Flash |
| LiveBench browser | 84.0% | 8.0% | GLM 5.3 Flash |
| LiveBench long agentic | 28.4% | 12.4% | GLM 5.3 Flash |
| SREBench (Vals AI) | 0.8% | Not measured | Only GLM 5.3 Flash measured |
| Finance Agent (Vals AI) | 57.9% | 48.3% | GLM 5.3 Flash |
| Tax Agent Bench (Vals AI) | 31.6% | 20.6% | GLM 5.3 Flash |
| Legal Research Bench (Vals AI) | 45.2% | 29.8% | GLM 5.3 Flash |
| Harvey's Legal Agent Benchmark (Vals AI) | 10.4% | Not measured | Only GLM 5.3 Flash measured |
| Excel Modeling Benchmark (Vals AI) | 55.9% | 47.8% | GLM 5.3 Flash |
| Terminal-Bench-Science (Vals AI) | 2.9% | Not measured | Only GLM 5.3 Flash measured |
| τ²-Bench Telecom | Not measured | 88.9% | Only MiniMax M3 measured |
| APEX-Agents | 52.8% | 37.7% | GLM 5.3 Flash |
| OSWorld 2.0 | Not measured | 4.6% | Only MiniMax M3 measured |
| Vending-Bench 2 | Not measured | $2,158 | Only MiniMax M3 measured |
| Neither model has a result on the other 18 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index93
| Benchmark | GLM 5.3 Flash | MiniMax M3 | Leader |
|---|---|---|---|
| GPQA Diamond | 86.4% | 92.7% | MiniMax M3 |
| LiveBench reasoning | 77.6% | 74.5% | GLM 5.3 Flash |
| MMMU Pro (Vals AI) | 86.0% | 81.2% | GLM 5.3 Flash |
| GPQA Diamond (Artificial Analysis run) | 91.2% | 92.9% | MiniMax M3 |
| Humanity's Last Exam (Artificial Analysis run) | 39.9% | 39.0% | GLM 5.3 Flash |
| GPQA Diamond (Epoch AI run) | 90.2% | 90.9% | MiniMax M3 |
| ARC-AGI | 91.0% | Not measured | Only GLM 5.3 Flash measured |
| ARC-AGI-2 | 65.8% | Not measured | Only GLM 5.3 Flash measured |
| SimpleBench | Not measured | 45.8% | Only MiniMax M3 measured |
| Chess puzzles | 21.0% | 13.0% | GLM 5.3 Flash |
| Mystery game puzzles | 8.0% | 8.0% | Tie |
| LMArena Text: hard prompts | 1490 | 1447 | GLM 5.3 Flash |
| LMArena Text: expert | 1511 | 1461 | GLM 5.3 Flash |
| LMArena Text: hard prompts in English | 1488 | 1455 | GLM 5.3 Flash |
| LMArena Vision: diagrams | 1317 | 1266 | GLM 5.3 Flash |
| LMArena Vision: homework | 1318 | 1268 | GLM 5.3 Flash |
| Neither model has a result on the other 6 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | GLM 5.3 Flash | MiniMax M3 | Leader |
|---|---|---|---|
| OTIS Mock AIME | 93.9% | 71.1% | GLM 5.3 Flash |
| FrontierMath, tiers 1 to 3 | 55.8% | Not measured | Only GLM 5.3 Flash measured |
| FrontierMath, tier 4 | 17.1% | Not measured | Only GLM 5.3 Flash measured |
| ProofBench | 21.0% | 18.0% | GLM 5.3 Flash |
| LMArena Text: math | 1503 | 1429 | GLM 5.3 Flash |
| LMArena Text: mathematical work | 1497 | 1448 | GLM 5.3 Flash |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index61
| Benchmark | GLM 5.3 Flash | MiniMax M3 | Leader |
|---|---|---|---|
| MMLU Pro | 86.1% | 84.2% | GLM 5.3 Flash |
| LegalBench (Vals AI) | 83.9% | 85.4% | MiniMax M3 |
| CorpFin (Vals AI) | Not measured | 68.1% | Only MiniMax M3 measured |
| TaxEval (Vals AI) | 75.6% | 72.7% | GLM 5.3 Flash |
| MortgageTax (Vals AI) | Not measured | 68.4% | Only MiniMax M3 measured |
| MedCode (Vals AI) | Not measured | 46.3% | Only MiniMax M3 measured |
| LMArena Text: science | 1484 | 1452 | GLM 5.3 Flash |
| LMArena Text: medicine and healthcare | 1475 | 1441 | GLM 5.3 Flash |
| LMArena Text: legal and government | 1473 | 1437 | GLM 5.3 Flash |
| LMArena Text: business and finance | 1460 | 1434 | GLM 5.3 Flash |
| Neither model has a result on the other 8 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index90
| Benchmark | GLM 5.3 Flash | MiniMax M3 | Leader |
|---|---|---|---|
| Design Arena: websites | 1272 | 1257 | GLM 5.3 Flash |
| Design Arena: UI components | 1316 | 1242 | GLM 5.3 Flash |
| Design Arena: game dev | 1278 | 1210 | GLM 5.3 Flash |
| Design Arena: data viz | 1262 | 1236 | GLM 5.3 Flash |
| Design Arena: 3D | 1316 | 1208 | GLM 5.3 Flash |
| Design Arena: all code categories | 1282 | 1247 | GLM 5.3 Flash |
| Design Arena: SVG | 1285 | 1161 | GLM 5.3 Flash |
| Design Arena: ASCII art | 1267 | 1161 | GLM 5.3 Flash |
| LiveBench design | 77.2% | 66.2% | GLM 5.3 Flash |
GeneralLike-for-likePublic boards, shown as published, kept out of the index252
| Benchmark | GLM 5.3 Flash | MiniMax M3 | Leader |
|---|---|---|---|
| Vals Index | 50.9% | Not measured | Only GLM 5.3 Flash measured |
| LiveBench data analysis | 76.4% | 76.2% | GLM 5.3 Flash |
| Public Benefits Bench (Vals AI) | Not measured | 64.1% | Only MiniMax M3 measured |
| SAGE (Vals AI) | Not measured | 50.6% | Only MiniMax M3 measured |
| MedScribe (Vals AI) | 88.9% | 87.3% | GLM 5.3 Flash |
| Vals Multimodal Index | Not measured | 60.0% | Only MiniMax M3 measured |
| IFBench | Not measured | 82.9% | Only MiniMax M3 measured |
| Long context reasoning (AA-LCR) | 80.0% | 83.0% | MiniMax M3 |
| DTBench | Not measured | 78.9% | Only MiniMax M3 measured |
| LMCA | Not measured | 33.7% | Only MiniMax M3 measured |
| Surface Evolver Bench | 52.5% | 55.0% | MiniMax M3 |
| LMArena Text | 1471 | 1433 | GLM 5.3 Flash |
| LMArena Text: instruction following | 1478 | 1433 | GLM 5.3 Flash |
| LMArena Text: creative writing | 1441 | 1404 | GLM 5.3 Flash |
| LMArena Vision | 1294 | 1251 | GLM 5.3 Flash |
| LMArena Text: English | 1474 | 1443 | GLM 5.3 Flash |
| LMArena Text: non-English | 1462 | 1420 | GLM 5.3 Flash |
| LMArena Text: Chinese | 1531 | 1466 | GLM 5.3 Flash |
| LMArena Text: German | 1462 | 1428 | GLM 5.3 Flash |
| LMArena Text: Japanese | 1444 | 1380 | GLM 5.3 Flash |
| LMArena Text: Korean | 1436 | 1373 | GLM 5.3 Flash |
| LMArena Text: French | 1491 | 1444 | GLM 5.3 Flash |
| LMArena Text: Spanish | 1475 | 1433 | GLM 5.3 Flash |
| LMArena Text: Russian | 1465 | 1426 | GLM 5.3 Flash |
| LMArena Text: Polish | 1450 | 1423 | GLM 5.3 Flash |
| LMArena Text: multi-turn | 1469 | 1443 | GLM 5.3 Flash |
| LMArena Text: longer queries | 1481 | 1446 | GLM 5.3 Flash |
| LMArena Text: writing and language | 1455 | 1419 | GLM 5.3 Flash |
| LMArena Text: entertainment and media | 1440 | 1403 | GLM 5.3 Flash |
| LMArena Vision: English | 1295 | 1263 | GLM 5.3 Flash |
| LMArena Vision: Chinese | 1363 | 1311 | GLM 5.3 Flash |
| LMArena Vision: reading text | 1299 | 1259 | GLM 5.3 Flash |
| LMArena Vision: humor | 1279 | 1235 | GLM 5.3 Flash |
| LMArena Vision: recognizing things | Not measured | 1197 | Only MiniMax M3 measured |
| LMArena Vision: creative writing | 1270 | 1234 | GLM 5.3 Flash |
| LMArena Document | Not measured | 1435 | Only MiniMax M3 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 Flash | MiniMax M3 | 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 Flash costs less
Repository review
50K input + 3K output tokens
GLM 5.3 Flash costs less
Agent loop
200K input + 10K output tokens
GLM 5.3 Flash 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 | MiniMax M3 |
|---|---|---|
| Maker | Z.ai | MiniMax |
| Weights | Open weights | Open weights |
| Released | August 26, 2026 | May 31, 2026 |
| Context window | 1.05M | 1.05M |
| Input $/Mtok | $0.15 | $0.30 |
| Output $/Mtok | $0.50 | $1.20 |
| Coding index | 57.7 | 39.2 |
| Independent measurements behind it | 6 | 6 |
| Cost per solved task | $0.02 | $0.56 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 5.3 Flash better than MiniMax M3 for coding?
Pick GLM 5.3 Flash for coding: it scores 57.7 on the Vibe Coding Index against 39.2 for MiniMax M3. Pick MiniMax M3 for work measured by LiveCodeBench (Vals AI) or Vibe Code Bench (Vals AI), where it leads.
Which is cheaper, GLM 5.3 Flash or MiniMax M3?
GLM 5.3 Flash costs $0.15 per million input tokens and $0.50 per million output tokens; MiniMax M3 costs $0.30 and $1.20. Per solved SWE-bench Verified task, GLM 5.3 Flash costs $0.02 and MiniMax M3 $0.56.
Which has the bigger context window, GLM 5.3 Flash or MiniMax M3?
Neither: both take 1.05M tokens.
How many benchmarks is the GLM 5.3 Flash vs MiniMax M3 comparison based on?
91 benchmarks have a result for both models as of October 22, 2026. GLM 5.3 Flash's coding index rests on 6 independent benchmarks and MiniMax M3's on 6. Where only one model has a result, the page says so instead of naming a winner.