GLM 4.6 vs Kimi K2 0711
GLM 4.6 leads Kimi K2 0711 on the Artificial Analysis Intelligence Index, 18.5 to 12.7, at about the same output price ($2.20 vs $2.30 per million). Updated October 22, 2026; 28 benchmarks have a result for both.
GLM 4.6 or Kimi K2 0711: which to pick
Only GLM 4.6 has a coding score, 8.8 on the Vibe Coding Index; Kimi K2 0711 has none, so the two cannot be ranked on coding.
Pick Kimi K2 0711 for work measured by SWE-bench Verified or Terminal-Bench, where it leads.
- Shared results
- 28
- GLM 4.6 only
- 41
- Kimi K2 0711 only
- 9
- 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
No call
Only GLM 4.6 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
GLM 4.6
GLM 4.6 leads, 18.5 against 12.7.
Same board, both measured
Reasoning
GPQA Diamond
GLM 4.6
GLM 4.6 leads, 74.5% against 71.5%.
Same board, both measured
Math
AIME
GLM 4.6
GLM 4.6 leads, 92.7% against 62.7%.
Same board, both measured
Knowledge
MMLU Pro
GLM 4.6
GLM 4.6 leads, 82.2% against 79.4%.
Same board, both measured
Web design
Design Arena: websites
GLM 4.6
GLM 4.6 leads, 1174 against 1050.
Same board, both measured
Long documents
Listed context window
GLM 4.6
GLM 4.6 takes 205K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 4.6
GLM 4.6 $0.0017, Kimi K2 0711 $0.0017 per turn.
List prices
At a glance
- Coding index
- GLM 4.6 8.8 from 2 benchmarks, Kimi K2 0711 not benchmarked.
- Widest category
- Math, 22.3 points apart on average over 4 shared benchmarks.
- Widest single gap
- AIME: GLM 4.6 92.7% against Kimi K2 0711 62.7%, 30.0 points apart on a 0 to 100 scale.
- Output price
- GLM 4.6 $2.20 against Kimi K2 0711 $2.30 per million output tokens, 1.0x apart.
- Context window
- GLM 4.6 takes 205K tokens, Kimi K2 0711 131K.
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 4.6 | Bars | Kimi K2 0711 | Ties |
|---|---|---|---|---|
| Coding | 3 | 2 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 3 | 1 | ||
| Math | 3 | 1 | ||
| Knowledge | 4 | 2 | ||
| Web design | 5 | 0 | ||
| General | 2 | 0 | ||
| All shared boards | 22 | 6 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index32
| Benchmark | GLM 4.6 | Kimi K2 0711 | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 59.1% | Only Kimi K2 0711 measured |
| SWE-bench Verified | 68.2% | 71.2% | Kimi K2 0711 |
| LMArena WebDev | 1341 | Not measured | Only GLM 4.6 measured |
| AA Coding Index | 45.8 | Not measured | Only GLM 4.6 measured |
| LiveCodeBench (Vals AI) | 81.0% | 70.5% | GLM 4.6 |
| Vibe Code Bench (Vals AI) | 3.1% | Not measured | Only GLM 4.6 measured |
| LiveCodeBench (Artificial Analysis run) | 69.5% | 55.6% | GLM 4.6 |
| Terminal-Bench Hard (Artificial Analysis run) | 28.8% | 15.9% | GLM 4.6 |
| Terminal-Bench | 24.5% | 27.8% | Kimi K2 0711 |
| GSO-Bench | Not measured | 4.9% | Only Kimi K2 0711 measured |
| WeirdML | Not measured | 39.4% | Only Kimi K2 0711 measured |
| ALE-Bench | 340.8 | Not measured | Only GLM 4.6 measured |
| LMArena Text: coding | 1449 | Not measured | Only GLM 4.6 measured |
| LMArena Text: software and IT | 1455 | Not measured | Only GLM 4.6 measured |
| LMArena WebDev: HTML | 1350 | Not measured | Only GLM 4.6 measured |
| Neither model has a result on the other 24 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 4.6 | Kimi K2 0711 | Leader |
|---|---|---|---|
| AA Intelligence Index | 18.5 | 12.7 | GLM 4.6 |
| Epoch Capabilities Index | Not measured | 140.0 | Only Kimi K2 0711 measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 4.6 | Kimi K2 0711 | Leader |
|---|---|---|---|
| Design Arena: agents building full-stack apps | 1029 | Not measured | Only GLM 4.6 measured |
| Design Arena: agents building mobile apps | 1096 | Not measured | Only GLM 4.6 measured |
| Design Arena: agents building Android apps | 1084 | Not measured | Only GLM 4.6 measured |
| Design Arena: agents building Godot games | 1180 | Not measured | Only GLM 4.6 measured |
| τ²-Bench Telecom | 76.9% | 61.1% | GLM 4.6 |
| Berkeley Function Calling Leaderboard (BFCL) | 72.4% | Not measured | Only GLM 4.6 measured |
| BrowseComp-Plus | 48.2% | Not measured | Only GLM 4.6 measured |
| Neither model has a result on the other 34 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index31
| Benchmark | GLM 4.6 | Kimi K2 0711 | Leader |
|---|---|---|---|
| GPQA Diamond | 74.5% | 71.5% | GLM 4.6 |
| GPQA Diamond (Artificial Analysis run) | 78.0% | 76.6% | GLM 4.6 |
| Humanity's Last Exam (Artificial Analysis run) | 14.5% | 7.4% | GLM 4.6 |
| SimpleBench | Not measured | 26.3% | Only Kimi K2 0711 measured |
| LMArena Text: hard prompts | 1440 | Not measured | Only GLM 4.6 measured |
| LMArena Text: expert | 1432 | Not measured | Only GLM 4.6 measured |
| LMArena Text: hard prompts in English | 1445 | Not measured | Only GLM 4.6 measured |
| Kagi LLM Benchmark | 45.7% | 64.4% | Kimi K2 0711 |
| Neither model has a result on the other 14 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index31
| Benchmark | GLM 4.6 | Kimi K2 0711 | Leader |
|---|---|---|---|
| AIME | 92.7% | 62.7% | GLM 4.6 |
| MATH 500 (Vals AI) | Not measured | 94.2% | Only Kimi K2 0711 measured |
| MGSM (Vals AI) | 89.8% | 91.0% | Kimi K2 0711 |
| AA Math Index | 86.0 | 57.0 | GLM 4.6 |
| AIME 2025 (Artificial Analysis run) | 86.0% | 57.0% | GLM 4.6 |
| LMArena Text: math | 1432 | Not measured | Only GLM 4.6 measured |
| LMArena Text: mathematical work | 1447 | Not measured | Only GLM 4.6 measured |
| Neither model has a result on the other 6 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index42
| Benchmark | GLM 4.6 | Kimi K2 0711 | Leader |
|---|---|---|---|
| MMLU Pro | 82.2% | 79.4% | GLM 4.6 |
| LegalBench (Vals AI) | 79.6% | 81.5% | Kimi K2 0711 |
| CorpFin (Vals AI) | 56.8% | 50.4% | GLM 4.6 |
| TaxEval (Vals AI) | 66.2% | 70.2% | Kimi K2 0711 |
| MedQA (Vals AI) | 92.2% | 84.0% | GLM 4.6 |
| MMLU Pro (Artificial Analysis run) | 82.9% | 82.4% | GLM 4.6 |
| LMArena Text: science | 1456 | Not measured | Only GLM 4.6 measured |
| LMArena Text: medicine and healthcare | 1455 | Not measured | Only GLM 4.6 measured |
| LMArena Text: legal and government | 1448 | Not measured | Only GLM 4.6 measured |
| LMArena Text: business and finance | 1416 | Not measured | Only GLM 4.6 measured |
| Neither model has a result on the other 8 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | GLM 4.6 | Kimi K2 0711 | Leader |
|---|---|---|---|
| Design Arena: websites | 1174 | 1050 | GLM 4.6 |
| Design Arena: UI components | 1145 | 1027 | GLM 4.6 |
| Design Arena: game dev | 1150 | 973 | GLM 4.6 |
| Design Arena: data viz | 1161 | 1018 | GLM 4.6 |
| Design Arena: 3D | 1128 | Not measured | Only GLM 4.6 measured |
| Design Arena: all code categories | 1165 | 1033 | GLM 4.6 |
| Design Arena: SVG | 1108 | Not measured | Only GLM 4.6 measured |
| Neither model has a result on the other 2 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GLM 4.6 | Kimi K2 0711 | Leader |
|---|---|---|---|
| IFBench | 43.4% | 41.5% | GLM 4.6 |
| Long context reasoning (AA-LCR) | 54.0% | 53.0% | GLM 4.6 |
| Fiction.LiveBench | Not measured | 61.1% | Only Kimi K2 0711 measured |
| Creative writing (Lech Mazur) | Not measured | 85.6% | Only Kimi K2 0711 measured |
| CL-bench | Not measured | 11.9% | Only Kimi K2 0711 measured |
| LMArena Text | 1440 | Not measured | Only GLM 4.6 measured |
| LMArena Text: instruction following | 1410 | Not measured | Only GLM 4.6 measured |
| LMArena Text: creative writing | 1412 | Not measured | Only GLM 4.6 measured |
| LMArena Text: English | 1449 | Not measured | Only GLM 4.6 measured |
| LMArena Text: non-English | 1426 | Not measured | Only GLM 4.6 measured |
| LMArena Text: Chinese | 1499 | Not measured | Only GLM 4.6 measured |
| LMArena Text: German | 1447 | Not measured | Only GLM 4.6 measured |
| LMArena Text: Japanese | 1393 | Not measured | Only GLM 4.6 measured |
| LMArena Text: Korean | 1400 | Not measured | Only GLM 4.6 measured |
| LMArena Text: French | 1459 | Not measured | Only GLM 4.6 measured |
| LMArena Text: Spanish | 1436 | Not measured | Only GLM 4.6 measured |
| LMArena Text: Russian | 1418 | Not measured | Only GLM 4.6 measured |
| LMArena Text: Polish | 1447 | Not measured | Only GLM 4.6 measured |
| LMArena Text: multi-turn | 1427 | Not measured | Only GLM 4.6 measured |
| LMArena Text: longer queries | 1422 | Not measured | Only GLM 4.6 measured |
| LMArena Text: writing and language | 1415 | Not measured | Only GLM 4.6 measured |
| LMArena Text: entertainment and media | 1409 | Not measured | Only GLM 4.6 measured |
| Neither model has a result on the other 24 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.6 | Kimi K2 0711 | 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 4.6 costs less
Repository review
50K input + 3K output tokens
Kimi K2 0711 costs less
Agent loop
200K input + 10K output tokens
Kimi K2 0711 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 4.6 | MoonshotAI Kimi K2 0711 |
|---|---|---|
| Maker | Z.ai | Moonshot AI |
| Weights | Open weights | Open weights |
| Released | September 30, 2025 | July 11, 2025 |
| Context window | 205K | 131K |
| Input $/Mtok | $0.60 | $0.57 |
| Output $/Mtok | $2.20 | $2.30 |
| Coding index | 8.8 | Not benchmarked |
| Independent measurements behind it | 2 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.6 better than Kimi K2 0711 for coding?
Only GLM 4.6 has a coding score, 8.8 on the Vibe Coding Index; Kimi K2 0711 has none, so the two cannot be ranked on coding. Pick Kimi K2 0711 for work measured by SWE-bench Verified or Terminal-Bench, where it leads.
Which is cheaper, GLM 4.6 or Kimi K2 0711?
GLM 4.6 costs $0.60 per million input tokens and $2.20 per million output tokens; Kimi K2 0711 costs $0.57 and $2.30. Cost per solved task is known for neither of them.
Which has the bigger context window, GLM 4.6 or Kimi K2 0711?
GLM 4.6, with 205K tokens against 131K.
How many benchmarks is the GLM 4.6 vs Kimi K2 0711 comparison based on?
28 benchmarks have a result for both models as of October 22, 2026. GLM 4.6's coding index rests on 2 independent benchmarks and Kimi K2 0711's on 0. Where only one model has a result, the page says so instead of naming a winner.