GLM 5.3 Flash vs Kimi K3
Kimi K3 leads GLM 5.3 Flash on coding, 62.3 to 57.7 on the Vibe Coding Index, and GLM 5.3 Flash costs 30.0x less per output token ($0.50 vs $15.00 per million). Updated October 22, 2026; 91 benchmarks have a result for both.
GLM 5.3 Flash or Kimi K3: which to pick
Pick Kimi K3 for coding: it scores 62.3 on the Vibe Coding Index against 57.7 for GLM 5.3 Flash.
Pick GLM 5.3 Flash for output tokens 30.0x cheaper ($0.50 vs $15.00 per million) and a lower cost per solved task ($0.02 against $0.81).
- Shared results
- 91
- GLM 5.3 Flash only
- 11
- Kimi K3 only
- 26
- 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
Kimi K3
Kimi K3 scores 62.3 against 57.7.
6 benchmarks behind each score
Intelligence
AA Intelligence Index
Kimi K3
Kimi K3 leads, 43.6 against 41.8.
Same board, both measured
Agentic work
AA Agentic Index
GLM 5.3 Flash
GLM 5.3 Flash leads, 50.9 against 50.0.
Same board, both measured
Reasoning
GPQA Diamond
Kimi K3
Kimi K3 leads, 92.9% against 86.4%.
Same board, both measured
Knowledge
MMLU Pro
Kimi K3
Kimi K3 leads, 88.0% against 86.1%.
Same board, both measured
Web design
Design Arena: websites
Kimi K3
Kimi K3 leads, 1334 against 1271.
Same board, both measured
General
Vals Index
Kimi K3
Kimi K3 leads, 53.0% against 50.9%.
Same board, both measured
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, Kimi K3 $0.011 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GLM 5.3 Flash
GLM 5.3 Flash $0.02, Kimi K3 $0.81.
Published attempt cost and pass rate
At a glance
- Coding index
- GLM 5.3 Flash 57.7 from 6 benchmarks, Kimi K3 62.3 from 6 benchmarks.
- Widest category
- Math, 18.4 points apart on average over 6 shared benchmarks.
- Widest single gap
- LiveBench browser: GLM 5.3 Flash 84.0% against Kimi K3 16.0%, 68.0 points apart on a 0 to 100 scale.
- Output price
- GLM 5.3 Flash $0.50 against Kimi K3 $15.00 per million output tokens, 30.0x 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 | Kimi K3 | Ties |
|---|---|---|---|---|
| Coding | 5 | 17 | ||
| Intelligence | 0 | 2 | ||
| Agentic work | 5 | 6 | ||
| Reasoning | 1 | 12 | ||
| Math | 1 | 5 | ||
| Knowledge | 0 | 7 | ||
| Web design | 0 | 7 | 1 tie | |
| General | 3 | 19 | ||
| All shared boards | 15 | 75 | 1 tie |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index517
| Benchmark | GLM 5.3 Flash | Kimi K3 | Leader |
|---|---|---|---|
| LiveBench coding | 80.6% | 59.3% | GLM 5.3 Flash |
| LMArena WebDev | 1607 | 1655 | Kimi K3 |
| AA Coding Index | 71.5 | 76.2 | Kimi K3 |
| LiveCodeBench (Vals AI) | 80.5% | 87.2% | Kimi K3 |
| SWE-bench Verified (Vals AI) | 92.0% | 93.4% | Kimi K3 |
| Terminal-Bench 4 (Vals AI) | 37.4% | 17.2% | GLM 5.3 Flash |
| Vibe Code Bench (Vals AI) | 30.8% | 85.0% | Kimi K3 |
| Code migration (Vals AI) | 20.5% | 16.1% | GLM 5.3 Flash |
| ProgramBench (Vals AI) | 0.0% | 2.0% | Kimi K3 |
| IOI (Vals AI) | 52.5% | 48.9% | GLM 5.3 Flash |
| Terminal-Bench 2.1 (Vals AI) | 62.9% | 80.9% | Kimi K3 |
| SWE-bench Multimodal (Vals AI) | Not measured | 44.2% | Only Kimi K3 measured |
| Vibe Code Bench 1-100 (Vals AI) | 16.0% | 18.2% | Kimi K3 |
| SkillsBench (Vals AI) | 40.2% | Not measured | Only GLM 5.3 Flash measured |
| CyberBench (Vals AI) | 67.9% | 75.2% | Kimi K3 |
| SciCode | 51.6% | 59.5% | Kimi K3 |
| FrontierCode | 31.8% | 44.2% | Kimi K3 |
| FrontierSWE | 18.1% | 25.9% | Kimi K3 |
| WeirdML | Not measured | 82.6% | Only Kimi K3 measured |
| ALE-Bench | 303.6 | 1524.5 | Kimi K3 |
| LMArena Text: coding | 1507 | 1508 | Kimi K3 |
| LMArena Text: software and IT | 1500 | 1499 | GLM 5.3 Flash |
| LMArena WebDev: React | 1624 | 1670 | Kimi K3 |
| LMArena WebDev: HTML | 1577 | 1644 | Kimi K3 |
| LMArena WebDev: from an image | 1578 | 1579 | Kimi K3 |
| Neither model has a result on the other 14 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GLM 5.3 Flash | Kimi K3 | Leader |
|---|---|---|---|
| AA Intelligence Index | 41.8 | 43.6 | Kimi K3 |
| Epoch Capabilities Index | 153.1 | 157.9 | Kimi K3 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index56
| Benchmark | GLM 5.3 Flash | Kimi K3 | Leader |
|---|---|---|---|
| AA Agentic Index | 50.9 | 50.0 | GLM 5.3 Flash |
| Design Arena: agents building web apps | Not measured | 1295 | Only Kimi K3 measured |
| Design Arena: agents building full-stack apps | Not measured | 1315 | Only Kimi K3 measured |
| Design Arena: agents building mobile apps | Not measured | 1269 | Only Kimi K3 measured |
| Design Arena: agents building Android apps | Not measured | 1253 | Only Kimi K3 measured |
| Design Arena: agents building Godot games | Not measured | 1223 | Only Kimi K3 measured |
| Design Arena: agents building games | Not measured | 1252 | Only Kimi K3 measured |
| Design Arena: HTML slides | Not measured | 1206 | Only Kimi K3 measured |
| Design Arena: PowerPoint slides | Not measured | 1248 | Only Kimi K3 measured |
| Design Arena: spreadsheets | Not measured | 1096 | Only Kimi K3 measured |
| LiveBench research | 70.1% | 79.0% | Kimi K3 |
| LiveBench browser | 84.0% | 16.0% | GLM 5.3 Flash |
| LiveBench long agentic | 28.4% | 42.8% | Kimi K3 |
| SREBench (Vals AI) | 0.8% | Not measured | Only GLM 5.3 Flash measured |
| Finance Agent (Vals AI) | 57.9% | 53.1% | GLM 5.3 Flash |
| Tax Agent Bench (Vals AI) | 31.6% | 33.4% | Kimi K3 |
| Legal Research Bench (Vals AI) | 45.2% | 46.1% | Kimi K3 |
| Harvey's Legal Agent Benchmark (Vals AI) | 10.4% | 12.9% | Kimi K3 |
| Excel Modeling Benchmark (Vals AI) | 55.9% | 66.7% | Kimi K3 |
| BioMysteryBench (Vals AI) | Not measured | 72.6% | Only Kimi K3 measured |
| Terminal-Bench-Science (Vals AI) | 2.9% | 1.4% | GLM 5.3 Flash |
| Time Horizon Index (Vals AI) | Not measured | 10.5% | Only Kimi K3 measured |
| APEX-Agents | 52.8% | 47.8% | GLM 5.3 Flash |
| PostTrainBench | Not measured | 32.0% | Only Kimi K3 measured |
| Vending-Bench 2 | Not measured | $5,165 | Only Kimi K3 measured |
| Neither model has a result on the other 16 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index112
| Benchmark | GLM 5.3 Flash | Kimi K3 | Leader |
|---|---|---|---|
| GPQA Diamond | 86.4% | 92.9% | Kimi K3 |
| LiveBench reasoning | 77.6% | 90.7% | Kimi K3 |
| MMMU Pro (Vals AI) | 86.0% | 88.2% | Kimi K3 |
| GPQA Diamond (Artificial Analysis run) | 91.2% | 93.5% | Kimi K3 |
| Humanity's Last Exam (Artificial Analysis run) | 39.9% | 46.9% | Kimi K3 |
| GPQA Diamond (Epoch AI run) | 90.2% | 93.1% | Kimi K3 |
| ARC-AGI | 91.0% | 94.5% | Kimi K3 |
| ARC-AGI-2 | 65.8% | 60.4% | GLM 5.3 Flash |
| SimpleBench | Not measured | 60.7% | Only Kimi K3 measured |
| Chess puzzles | 21.0% | 37.0% | Kimi K3 |
| Mystery game puzzles | 8.0% | 26.0% | Kimi K3 |
| LMArena Text: hard prompts | 1490 | 1495 | Kimi K3 |
| LMArena Text: expert | 1511 | 1520 | Kimi K3 |
| LMArena Text: hard prompts in English | 1488 | 1496 | Kimi K3 |
| LMArena Vision: diagrams | 1317 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Vision: homework | 1318 | Not measured | Only GLM 5.3 Flash measured |
| Neither model has a result on the other 6 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index15
| Benchmark | GLM 5.3 Flash | Kimi K3 | Leader |
|---|---|---|---|
| OTIS Mock AIME | 93.9% | 97.2% | Kimi K3 |
| FrontierMath, tiers 1 to 3 | 55.8% | 72.2% | Kimi K3 |
| FrontierMath, tier 4 | 17.1% | 39.0% | Kimi K3 |
| ProofBench | 21.0% | 87.0% | Kimi K3 |
| LMArena Text: math | 1503 | 1494 | GLM 5.3 Flash |
| LMArena Text: mathematical work | 1497 | 1505 | Kimi K3 |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index07
| Benchmark | GLM 5.3 Flash | Kimi K3 | Leader |
|---|---|---|---|
| MMLU Pro | 86.1% | 88.0% | Kimi K3 |
| LegalBench (Vals AI) | 83.9% | 86.2% | Kimi K3 |
| CorpFin (Vals AI) | Not measured | 71.6% | Only Kimi K3 measured |
| TaxEval (Vals AI) | 75.6% | 75.7% | Kimi K3 |
| MortgageTax (Vals AI) | Not measured | 66.3% | Only Kimi K3 measured |
| MedCode (Vals AI) | Not measured | 49.4% | Only Kimi K3 measured |
| SimpleQA Verified | Not measured | 50.6% | Only Kimi K3 measured |
| LMArena Text: science | 1484 | 1508 | Kimi K3 |
| LMArena Text: medicine and healthcare | 1475 | 1489 | Kimi K3 |
| LMArena Text: legal and government | 1473 | 1503 | Kimi K3 |
| LMArena Text: business and finance | 1460 | 1470 | Kimi K3 |
| Neither model has a result on the other 7 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index07
| Benchmark | GLM 5.3 Flash | Kimi K3 | Leader |
|---|---|---|---|
| Design Arena: websites | 1271 | 1334 | Kimi K3 |
| Design Arena: UI components | 1314 | 1340 | Kimi K3 |
| Design Arena: game dev | 1277 | 1360 | Kimi K3 |
| Design Arena: data viz | 1262 | 1352 | Kimi K3 |
| Design Arena: 3D | 1315 | 1387 | Kimi K3 |
| Design Arena: all code categories | 1281 | 1360 | Kimi K3 |
| Design Arena: SVG | 1284 | 1302 | Kimi K3 |
| Design Arena: ASCII art | 1267 | Not measured | Only GLM 5.3 Flash measured |
| LiveBench design | 77.2% | 77.2% | Tie |
GeneralLike-for-likePublic boards, shown as published, kept out of the index319
| Benchmark | GLM 5.3 Flash | Kimi K3 | Leader |
|---|---|---|---|
| Vals Index | 50.9% | 53.0% | Kimi K3 |
| LiveBench data analysis | 76.4% | 78.7% | Kimi K3 |
| Public Benefits Bench (Vals AI) | Not measured | 68.2% | Only Kimi K3 measured |
| SAGE (Vals AI) | Not measured | 52.8% | Only Kimi K3 measured |
| MedScribe (Vals AI) | 88.9% | 88.0% | GLM 5.3 Flash |
| Vals Multimodal Index | Not measured | 73.4% | Only Kimi K3 measured |
| Long context reasoning (AA-LCR) | 80.0% | 88.7% | Kimi K3 |
| DTBench | Not measured | 91.2% | Only Kimi K3 measured |
| LMCA | Not measured | 52.7% | Only Kimi K3 measured |
| Surface Evolver Bench | 52.5% | 95.0% | Kimi K3 |
| Furniture assembly | Not measured | 34.2% | Only Kimi K3 measured |
| LMArena Text | 1471 | 1476 | Kimi K3 |
| LMArena Text: instruction following | 1478 | 1485 | Kimi K3 |
| LMArena Text: creative writing | 1441 | 1457 | Kimi K3 |
| LMArena Vision | 1294 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: English | 1474 | 1481 | Kimi K3 |
| LMArena Text: non-English | 1462 | 1466 | Kimi K3 |
| LMArena Text: Chinese | 1531 | 1529 | GLM 5.3 Flash |
| LMArena Text: German | 1462 | 1490 | Kimi K3 |
| LMArena Text: Japanese | 1444 | 1494 | Kimi K3 |
| LMArena Text: Korean | 1436 | 1460 | Kimi K3 |
| LMArena Text: French | 1491 | 1492 | Kimi K3 |
| LMArena Text: Spanish | 1475 | 1472 | GLM 5.3 Flash |
| LMArena Text: Russian | 1465 | 1485 | Kimi K3 |
| LMArena Text: Polish | 1450 | 1458 | Kimi K3 |
| LMArena Text: multi-turn | 1469 | 1489 | Kimi K3 |
| LMArena Text: longer queries | 1481 | 1494 | Kimi K3 |
| LMArena Text: writing and language | 1455 | 1471 | Kimi K3 |
| LMArena Text: entertainment and media | 1440 | 1453 | Kimi K3 |
| LMArena Vision: English | 1295 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Vision: Chinese | 1363 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Vision: reading text | 1299 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Vision: humor | 1279 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Vision: creative writing | 1270 | Not measured | Only GLM 5.3 Flash 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 Flash | Kimi K3 | 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 | MoonshotAI Kimi K3 |
|---|---|---|
| Maker | Z.ai | Moonshot AI |
| Weights | Open weights | Open weights |
| Released | August 26, 2026 | July 16, 2026 |
| Context window | 1.05M | 1.05M |
| Input $/Mtok | $0.15 | $3.00 |
| Output $/Mtok | $0.50 | $15.00 |
| Coding index | 57.7 | 62.3 |
| Independent measurements behind it | 6 | 6 |
| Cost per solved task | $0.02 | $0.81 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 5.3 Flash better than Kimi K3 for coding?
Pick Kimi K3 for coding: it scores 62.3 on the Vibe Coding Index against 57.7 for GLM 5.3 Flash. Pick GLM 5.3 Flash for output tokens 30.0x cheaper ($0.50 vs $15.00 per million) and a lower cost per solved task ($0.02 against $0.81).
Which is cheaper, GLM 5.3 Flash or Kimi K3?
GLM 5.3 Flash costs $0.15 per million input tokens and $0.50 per million output tokens; Kimi K3 costs $3.00 and $15.00. Per solved SWE-bench Verified task, GLM 5.3 Flash costs $0.02 and Kimi K3 $0.81.
Which has the bigger context window, GLM 5.3 Flash or Kimi K3?
Neither: both take 1.05M tokens.
How many benchmarks is the GLM 5.3 Flash vs Kimi K3 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 Kimi K3's on 6. Where only one model has a result, the page says so instead of naming a winner.