GLM 4.6V vs Kimi K2 0711
Updated October 21, 2026. 11 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Neither model has a coding score yet, so the two cannot be ranked on coding.
GLM 4.6V has the edge where output tokens cost 2.6x less, and where what matters is LiveCodeBench (Artificial Analysis run) or Terminal-Bench Hard (Artificial Analysis run), where it leads.
- Shared results
- 11
- GLM 4.6V only
- 27
- Kimi K2 0711 only
- 25
- Like-for-like categories
- 7 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
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
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
Kimi K2 0711
Kimi K2 0711 leads, 12.7 against 11.2.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only Kimi K2 0711 has a GPQA Diamond result, 71.5%.
Directional only
Math
AIME
No call
Only Kimi K2 0711 has a AIME result, 62.7%.
Directional only
Knowledge
MMLU Pro
No call
Only Kimi K2 0711 has a MMLU Pro result, 79.4%.
Directional only
Web design
Design Arena: websites
No call
Only Kimi K2 0711 has a Design Arena: websites result, 1050.
Directional only
Long documents
Listed context window
No call
Both take 131K tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 4.6V
GLM 4.6V $0.0008, Kimi K2 0711 $0.0017 per turn.
List prices
At a glance
- Coding index
- GLM 4.6V not benchmarked, Kimi K2 0711 not benchmarked.
- Widest category
- Agentic work, 29.5 points apart on average over 1 shared benchmark.
- Widest single gap
- τ²-Bench Telecom: GLM 4.6V 31.6% against Kimi K2 0711 61.1%, 29.5 points apart on a 0 to 100 scale.
- Output price
- GLM 4.6V $0.90 against Kimi K2 0711 $2.30 per million output tokens, 2.6x apart.
- Context window
- Both take 131K 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 4.6V | Bars | Kimi K2 0711 | Ties |
|---|---|---|---|---|
| Coding | 0 | 2 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 1 | 1 | ||
| Math | 2 | 0 | ||
| Knowledge | 0 | 1 | ||
| General | 0 | 2 | ||
| All shared boards | 3 | 8 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
| Benchmark | GLM 4.6V | Kimi K2 0711 | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 59.1% | Only Kimi K2 0711 measured |
| SWE-bench Verified | Not measured | 71.2% | Only Kimi K2 0711 measured |
| LiveCodeBench (Vals AI) | Not measured | 70.5% | Only Kimi K2 0711 measured |
| LiveCodeBench (Artificial Analysis run) | 41.1% | 55.6% | Kimi K2 0711 |
| Terminal-Bench Hard (Artificial Analysis run) | 14.4% | 15.9% | Kimi K2 0711 |
| Terminal-Bench | Not measured | 27.8% | Only Kimi K2 0711 measured |
| GSO-Bench | Not measured | 4.9% | Only Kimi K2 0711 measured |
| WeirdML | Not measured | 39.4% | Only Kimi K2 0711 measured |
| LMArena Text: coding | 1389 | Not measured | Only GLM 4.6V measured |
| LMArena Text: software and IT | 1391 | Not measured | Only GLM 4.6V measured |
| Neither model has a result on the other 28 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 4.6V | Kimi K2 0711 | Leader |
|---|---|---|---|
| AA Intelligence Index | 11.2 | 12.7 | Kimi K2 0711 |
| Epoch Capabilities Index | Not measured | 140.0 | Only Kimi K2 0711 measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 4.6V | Kimi K2 0711 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 31.6% | 61.1% | Kimi K2 0711 |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | GLM 4.6V | Kimi K2 0711 | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 71.5% | Only Kimi K2 0711 measured |
| GPQA Diamond (Artificial Analysis run) | 71.9% | 76.6% | Kimi K2 0711 |
| Humanity's Last Exam (Artificial Analysis run) | 9.6% | 7.4% | GLM 4.6V |
| SimpleBench | Not measured | 26.3% | Only Kimi K2 0711 measured |
| LMArena Text: hard prompts | 1367 | Not measured | Only GLM 4.6V measured |
| LMArena Text: expert | 1371 | Not measured | Only GLM 4.6V measured |
| LMArena Text: hard prompts in English | 1391 | Not measured | Only GLM 4.6V measured |
| LMArena Vision: diagrams | 1182 | Not measured | Only GLM 4.6V measured |
| LMArena Vision: homework | 1155 | Not measured | Only GLM 4.6V measured |
| Neither model has a result on the other 12 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GLM 4.6V | Kimi K2 0711 | Leader |
|---|---|---|---|
| AIME | Not measured | 62.7% | Only Kimi K2 0711 measured |
| MATH 500 (Vals AI) | Not measured | 94.2% | Only Kimi K2 0711 measured |
| MGSM (Vals AI) | Not measured | 91.0% | Only Kimi K2 0711 measured |
| AA Math Index | 85.3 | 57.0 | GLM 4.6V |
| AIME 2025 (Artificial Analysis run) | 85.3% | 57.0% | GLM 4.6V |
| Neither model has a result on the other 8 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 4.6V | Kimi K2 0711 | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 79.4% | Only Kimi K2 0711 measured |
| LegalBench (Vals AI) | Not measured | 81.5% | Only Kimi K2 0711 measured |
| CorpFin (Vals AI) | Not measured | 50.4% | Only Kimi K2 0711 measured |
| TaxEval (Vals AI) | Not measured | 70.2% | Only Kimi K2 0711 measured |
| MedQA (Vals AI) | Not measured | 84.0% | Only Kimi K2 0711 measured |
| MMLU Pro (Artificial Analysis run) | 79.9% | 82.4% | Kimi K2 0711 |
| LMArena Text: science | 1389 | Not measured | Only GLM 4.6V measured |
| LMArena Text: medicine and healthcare | 1389 | Not measured | Only GLM 4.6V measured |
| LMArena Text: legal and government | 1393 | Not measured | Only GLM 4.6V measured |
| LMArena Text: business and finance | 1359 | Not measured | Only GLM 4.6V measured |
| Neither model has a result on the other 7 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | GLM 4.6V | Kimi K2 0711 | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1050 | Only Kimi K2 0711 measured |
| Design Arena: UI components | Not measured | 1030 | Only Kimi K2 0711 measured |
| Design Arena: game dev | Not measured | 974 | Only Kimi K2 0711 measured |
| Design Arena: data viz | Not measured | 1018 | Only Kimi K2 0711 measured |
| Design Arena: all code categories | Not measured | 1033 | Only Kimi K2 0711 measured |
| Neither model has a result on the other 4 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GLM 4.6V | Kimi K2 0711 | Leader |
|---|---|---|---|
| IFBench | 30.1% | 41.5% | Kimi K2 0711 |
| Long context reasoning (AA-LCR) | 48.7% | 53.0% | Kimi K2 0711 |
| 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 | 1376 | Not measured | Only GLM 4.6V measured |
| LMArena Text: instruction following | 1350 | Not measured | Only GLM 4.6V measured |
| LMArena Text: creative writing | 1347 | Not measured | Only GLM 4.6V measured |
| LMArena Vision | 1164 | Not measured | Only GLM 4.6V measured |
| LMArena Text: English | 1395 | Not measured | Only GLM 4.6V measured |
| LMArena Text: non-English | 1358 | Not measured | Only GLM 4.6V measured |
| LMArena Text: Chinese | 1425 | Not measured | Only GLM 4.6V measured |
| LMArena Text: Russian | 1339 | Not measured | Only GLM 4.6V measured |
| LMArena Text: multi-turn | 1359 | Not measured | Only GLM 4.6V measured |
| LMArena Text: longer queries | 1357 | Not measured | Only GLM 4.6V measured |
| LMArena Text: writing and language | 1347 | Not measured | Only GLM 4.6V measured |
| LMArena Text: entertainment and media | 1343 | Not measured | Only GLM 4.6V measured |
| LMArena Vision: English | 1179 | Not measured | Only GLM 4.6V measured |
| LMArena Vision: Chinese | 1217 | Not measured | Only GLM 4.6V measured |
| LMArena Vision: reading text | 1171 | Not measured | Only GLM 4.6V measured |
| LMArena Vision: creative writing | 1113 | Not measured | Only GLM 4.6V measured |
| Neither model has a result on the other 25 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.6V | 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 4.6V costs less
Repository review
50K input + 3K output tokens
GLM 4.6V costs less
Agent loop
200K input + 10K output tokens
GLM 4.6V 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 4.6V | MoonshotAI Kimi K2 0711 |
|---|---|---|
| Maker | Z.ai | Moonshot AI |
| Weights | Open weights | Open weights |
| Released | December 8, 2025 | July 11, 2025 |
| Context window | 131K | 131K |
| Input $/Mtok | $0.30 | $0.57 |
| Output $/Mtok | $0.90 | $2.30 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.6V better than Kimi K2 0711 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. GLM 4.6V has the edge where output tokens cost 2.6x less, and where what matters is LiveCodeBench (Artificial Analysis run) or Terminal-Bench Hard (Artificial Analysis run), where it leads.
Which is cheaper, GLM 4.6V or Kimi K2 0711?
GLM 4.6V costs $0.30 per million input tokens and $0.90 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 4.6V or Kimi K2 0711?
Neither: both take 131K tokens.
How many benchmarks is the GLM 4.6V vs Kimi K2 0711 comparison based on?
11 benchmarks have a result for both models as of October 21, 2026. GLM 4.6V's coding index rests on 0 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.