GLM 5.1 vs Kimi K2.5
GLM 5.1 leads Kimi K2.5 on coding, 49.2 to 38.6 on the Vibe Coding Index, and Kimi K2.5 costs 1.5x less per output token ($2.85 vs $4.40 per million). Updated October 21, 2026; 73 benchmarks have a result for both.
GLM 5.1 or Kimi K2.5: which to pick
Pick GLM 5.1 for coding: it scores 49.2 on the Vibe Coding Index against 38.6 for Kimi K2.5.
Pick Kimi K2.5 for output tokens 1.5x cheaper ($2.85 vs $4.40 per million) and a lower cost per solved task ($0.24 against $0.60).
- Shared results
- 73
- GLM 5.1 only
- 16
- Kimi K2.5 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
GLM 5.1
GLM 5.1 scores 49.2 against 38.6.
3 benchmarks behind each score
Intelligence
AA Intelligence Index
GLM 5.1
GLM 5.1 leads, 26.1 against 23.5.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only GLM 5.1 has a AA Agentic Index result, 23.9.
Directional only
Reasoning
GPQA Diamond
GLM 5.1
GLM 5.1 leads, 84.5% against 84.1%.
Same board, both measured
Math
AIME
Kimi K2.5
Kimi K2.5 leads, 95.6% against 91.9%.
Same board, both measured
Knowledge
MMLU Pro
GLM 5.1
GLM 5.1 leads, 86.9% against 85.9%.
Same board, both measured
Web design
Design Arena: websites
GLM 5.1
GLM 5.1 leads, 1290 against 1248.
Same board, both measured
Long documents
Listed context window
Kimi K2.5
Kimi K2.5 takes 262K tokens against 205K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Kimi K2.5
GLM 5.1 $0.0036, Kimi K2.5 $0.0020 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
Kimi K2.5
GLM 5.1 $0.60, Kimi K2.5 $0.24.
Published attempt cost and pass rate
At a glance
- Coding index
- GLM 5.1 49.2 from 3 benchmarks, Kimi K2.5 38.6 from 3 benchmarks.
- Widest category
- Agentic work, 10.3 points apart on average over 9 shared benchmarks.
- Widest single gap
- Vending-Bench 2: GLM 5.1 $5,634 against Kimi K2.5 $1,198, 27.7 points apart on a 0 to 100 scale.
- Output price
- GLM 5.1 $4.40 against Kimi K2.5 $2.85 per million output tokens, 1.5x apart.
- Context window
- GLM 5.1 takes 205K tokens, Kimi K2.5 262K.
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 5.1 | Bars | Kimi K2.5 | Ties |
|---|---|---|---|---|
| Coding | 13 | 1 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 8 | 1 | ||
| Reasoning | 5 | 2 | ||
| Math | 1 | 2 | ||
| Knowledge | 6 | 4 | ||
| Web design | 7 | 1 | ||
| General | 18 | 2 | ||
| All shared boards | 60 | 13 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index131
| Benchmark | GLM 5.1 | Kimi K2.5 | Leader |
|---|---|---|---|
| SWE-bench Verified | Not measured | 70.8% | Only Kimi K2.5 measured |
| SWE-bench Multilingual | Not measured | 67.3% | Only Kimi K2.5 measured |
| LMArena WebDev | 1508 | 1437 | GLM 5.1 |
| AA Coding Index | 55.8 | 46.8 | GLM 5.1 |
| LiveCodeBench (Vals AI) | 81.4% | 83.9% | Kimi K2.5 |
| SWE-bench Verified (Vals AI) | 76.4% | 70.0% | GLM 5.1 |
| Vibe Code Bench (Vals AI) | 31.5% | 17.5% | GLM 5.1 |
| Code migration (Vals AI) | 25.8% | 7.0% | GLM 5.1 |
| ProgramBench (Vals AI) | 0.0% | Not measured | Only GLM 5.1 measured |
| Terminal-Bench 2.1 (Vals AI) | 56.9% | 42.0% | GLM 5.1 |
| SciCode | 44.8% | Not measured | Only GLM 5.1 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 43.2% | 34.8% | GLM 5.1 |
| Terminal-Bench | Not measured | 43.2% | Only Kimi K2.5 measured |
| WeirdML | 57.1% | 45.6% | GLM 5.1 |
| ALE-Bench | 887.1 | 821.6 | GLM 5.1 |
| LMArena Text: coding | 1485 | 1483 | GLM 5.1 |
| LMArena Text: software and IT | 1484 | 1471 | GLM 5.1 |
| LMArena WebDev: React | 1505 | 1429 | GLM 5.1 |
| LMArena WebDev: HTML | 1508 | 1426 | GLM 5.1 |
| LMArena WebDev: from an image | Not measured | 1439 | Only Kimi K2.5 measured |
| Neither model has a result on the other 19 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GLM 5.1 | Kimi K2.5 | Leader |
|---|---|---|---|
| AA Intelligence Index | 26.1 | 23.5 | GLM 5.1 |
| Epoch Capabilities Index | 149.8 | 148.1 | GLM 5.1 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index81
| Benchmark | GLM 5.1 | Kimi K2.5 | Leader |
|---|---|---|---|
| AA Agentic Index | 23.9 | Not measured | Only GLM 5.1 measured |
| Design Arena: agents building web apps | 1175 | 1136 | GLM 5.1 |
| Design Arena: agents building full-stack apps | 1167 | 1114 | GLM 5.1 |
| Design Arena: agents building mobile apps | 1162 | 1147 | GLM 5.1 |
| Design Arena: agents building Android apps | 1180 | 1118 | GLM 5.1 |
| Design Arena: agents building Godot games | 1053 | 1219 | Kimi K2.5 |
| Design Arena: agents building games | 1164 | Not measured | Only GLM 5.1 measured |
| Design Arena: HTML slides | 1170 | Not measured | Only GLM 5.1 measured |
| Design Arena: PowerPoint slides | 1188 | Not measured | Only GLM 5.1 measured |
| Design Arena: spreadsheets | 1315 | Not measured | Only GLM 5.1 measured |
| Finance Agent (Vals AI) | 44.8% | 35.8% | GLM 5.1 |
| Legal Research Bench (Vals AI) | 27.9% | 15.9% | GLM 5.1 |
| Excel Modeling Benchmark (Vals AI) | Not measured | 28.5% | Only Kimi K2.5 measured |
| τ²-Bench Telecom | 97.7% | 95.9% | GLM 5.1 |
| APEX-Agents | 40.9% | Not measured | Only GLM 5.1 measured |
| OSWorld | Not measured | 63.3% | Only Kimi K2.5 measured |
| Vending-Bench 2 | $5,634 | $1,198 | GLM 5.1 |
| Neither model has a result on the other 24 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index52
| Benchmark | GLM 5.1 | Kimi K2.5 | Leader |
|---|---|---|---|
| GPQA Diamond | 84.5% | 84.1% | GLM 5.1 |
| MMMU Pro (Vals AI) | Not measured | 84.3% | Only Kimi K2.5 measured |
| GPQA Diamond (Artificial Analysis run) | 86.8% | 87.9% | Kimi K2.5 |
| Humanity's Last Exam (Artificial Analysis run) | 30.1% | 30.7% | Kimi K2.5 |
| GPQA Diamond (Epoch AI run) | 89.9% | Not measured | Only GLM 5.1 measured |
| ARC-AGI | Not measured | 65.3% | Only Kimi K2.5 measured |
| ARC-AGI-2 | Not measured | 11.8% | Only Kimi K2.5 measured |
| SimpleBench | 55.1% | 46.8% | GLM 5.1 |
| Chess puzzles | 20.0% | Not measured | Only GLM 5.1 measured |
| LMArena Text: hard prompts | 1473 | 1453 | GLM 5.1 |
| LMArena Text: expert | 1478 | 1466 | GLM 5.1 |
| LMArena Text: hard prompts in English | 1482 | 1460 | GLM 5.1 |
| LMArena Vision: diagrams | Not measured | 1282 | Only Kimi K2.5 measured |
| LMArena Vision: homework | Not measured | 1297 | Only Kimi K2.5 measured |
| Kagi LLM Benchmark | Not measured | 78.5% | Only Kimi K2.5 measured |
| Neither model has a result on the other 7 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index12
| Benchmark | GLM 5.1 | Kimi K2.5 | Leader |
|---|---|---|---|
| AIME | 91.9% | 95.6% | Kimi K2.5 |
| OTIS Mock AIME | 93.3% | Not measured | Only GLM 5.1 measured |
| FrontierMath, tiers 1 to 3 | 36.8% | Not measured | Only GLM 5.1 measured |
| ProofBench | 22.2% | Not measured | Only GLM 5.1 measured |
| LMArena Text: math | 1473 | 1470 | GLM 5.1 |
| LMArena Text: mathematical work | 1470 | 1473 | Kimi K2.5 |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index64
| Benchmark | GLM 5.1 | Kimi K2.5 | Leader |
|---|---|---|---|
| MMLU Pro | 86.9% | 85.9% | GLM 5.1 |
| LegalBench (Vals AI) | 84.4% | Not measured | Only GLM 5.1 measured |
| CaseLaw (Vals AI) | 51.5% | 58.7% | Kimi K2.5 |
| CorpFin (Vals AI) | 64.5% | 68.3% | Kimi K2.5 |
| TaxEval (Vals AI) | 71.2% | 74.2% | Kimi K2.5 |
| MortgageTax (Vals AI) | Not measured | 66.5% | Only Kimi K2.5 measured |
| MedQA (Vals AI) | Not measured | 94.4% | Only Kimi K2.5 measured |
| MedCode (Vals AI) | 41.6% | 39.3% | GLM 5.1 |
| SimpleQA Verified | 34.0% | 34.3% | Kimi K2.5 |
| LMArena Text: science | 1478 | 1459 | GLM 5.1 |
| LMArena Text: medicine and healthcare | 1474 | 1448 | GLM 5.1 |
| LMArena Text: legal and government | 1465 | 1441 | GLM 5.1 |
| LMArena Text: business and finance | 1452 | 1427 | GLM 5.1 |
| Neither model has a result on the other 5 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index71
| Benchmark | GLM 5.1 | Kimi K2.5 | Leader |
|---|---|---|---|
| Design Arena: websites | 1290 | 1248 | GLM 5.1 |
| Design Arena: UI components | 1292 | 1236 | GLM 5.1 |
| Design Arena: game dev | 1265 | 1207 | GLM 5.1 |
| Design Arena: data viz | 1366 | 1219 | GLM 5.1 |
| Design Arena: 3D | 1336 | 1205 | GLM 5.1 |
| Design Arena: all code categories | 1268 | 1236 | GLM 5.1 |
| Design Arena: SVG | 1218 | 1149 | GLM 5.1 |
| Design Arena: ASCII art | 1142 | 1166 | Kimi K2.5 |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index182
| Benchmark | GLM 5.1 | Kimi K2.5 | Leader |
|---|---|---|---|
| Public Benefits Bench (Vals AI) | 61.8% | Not measured | Only GLM 5.1 measured |
| SAGE (Vals AI) | Not measured | 49.9% | Only Kimi K2.5 measured |
| MedScribe (Vals AI) | 72.3% | 76.4% | Kimi K2.5 |
| IFBench | 76.3% | 70.2% | GLM 5.1 |
| Long context reasoning (AA-LCR) | 73.7% | 78.0% | Kimi K2.5 |
| Fiction.LiveBench | Not measured | 86.1% | Only Kimi K2.5 measured |
| CL-bench | Not measured | 19.3% | Only Kimi K2.5 measured |
| ExploitBench | 18.1% | Not measured | Only GLM 5.1 measured |
| CL-bench Life | Not measured | 13.2% | Only Kimi K2.5 measured |
| LMArena Text | 1463 | 1445 | GLM 5.1 |
| LMArena Text: instruction following | 1453 | 1431 | GLM 5.1 |
| LMArena Text: creative writing | 1453 | 1422 | GLM 5.1 |
| LMArena Vision | Not measured | 1269 | Only Kimi K2.5 measured |
| LMArena Text: English | 1476 | 1453 | GLM 5.1 |
| LMArena Text: non-English | 1448 | 1433 | GLM 5.1 |
| LMArena Text: Chinese | 1515 | 1494 | GLM 5.1 |
| LMArena Text: German | 1467 | 1441 | GLM 5.1 |
| LMArena Text: Japanese | 1435 | 1423 | GLM 5.1 |
| LMArena Text: Korean | 1424 | 1412 | GLM 5.1 |
| LMArena Text: French | 1470 | 1454 | GLM 5.1 |
| LMArena Text: Spanish | 1468 | 1450 | GLM 5.1 |
| LMArena Text: Russian | 1454 | 1435 | GLM 5.1 |
| LMArena Text: Polish | 1449 | 1445 | GLM 5.1 |
| LMArena Text: multi-turn | 1473 | 1444 | GLM 5.1 |
| LMArena Text: longer queries | 1467 | 1445 | GLM 5.1 |
| LMArena Text: writing and language | 1454 | 1425 | GLM 5.1 |
| LMArena Text: entertainment and media | 1442 | 1417 | GLM 5.1 |
| LMArena Vision: English | Not measured | 1274 | Only Kimi K2.5 measured |
| LMArena Vision: Chinese | Not measured | 1328 | Only Kimi K2.5 measured |
| LMArena Vision: reading text | Not measured | 1276 | Only Kimi K2.5 measured |
| LMArena Vision: humor | Not measured | 1284 | Only Kimi K2.5 measured |
| LMArena Vision: captioning | Not measured | 1257 | Only Kimi K2.5 measured |
| LMArena Vision: recognizing things | Not measured | 1285 | Only Kimi K2.5 measured |
| LMArena Vision: creative writing | Not measured | 1269 | Only Kimi K2.5 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 5.1 | Kimi K2.5 | 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
Kimi K2.5 costs less
Repository review
50K input + 3K output tokens
Kimi K2.5 costs less
Agent loop
200K input + 10K output tokens
Kimi K2.5 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 5.1 | MoonshotAI Kimi K2.5 |
|---|---|---|
| Maker | Z.ai | Moonshot AI |
| Weights | Open weights | Open weights |
| Released | April 7, 2026 | January 27, 2026 |
| Context window | 205K | 262K |
| Input $/Mtok | $1.40 | $0.532 |
| Output $/Mtok | $4.40 | $2.85 |
| Coding index | 49.2 | 38.6 |
| Independent measurements behind it | 3 | 3 |
| Cost per solved task | $0.60 | $0.24 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 5.1 better than Kimi K2.5 for coding?
Pick GLM 5.1 for coding: it scores 49.2 on the Vibe Coding Index against 38.6 for Kimi K2.5. Pick Kimi K2.5 for output tokens 1.5x cheaper ($2.85 vs $4.40 per million) and a lower cost per solved task ($0.24 against $0.60).
Which is cheaper, GLM 5.1 or Kimi K2.5?
GLM 5.1 costs $1.40 per million input tokens and $4.40 per million output tokens; Kimi K2.5 costs $0.532 and $2.85. Per solved SWE-bench Verified task, GLM 5.1 costs $0.60 and Kimi K2.5 $0.24.
Which has the bigger context window, GLM 5.1 or Kimi K2.5?
Kimi K2.5, with 262K tokens against 205K.
How many benchmarks is the GLM 5.1 vs Kimi K2.5 comparison based on?
73 benchmarks have a result for both models as of October 21, 2026. GLM 5.1's coding index rests on 3 independent benchmarks and Kimi K2.5's on 3. Where only one model has a result, the page says so instead of naming a winner.