GLM 4.6V vs Kimi K2 0905
Kimi K2 0905 leads GLM 4.6V on the Artificial Analysis Intelligence Index, 15.3 to 11.2, and GLM 4.6V costs 2.8x less per output token ($0.90 vs $2.50 per million). Updated October 21, 2026; 11 benchmarks have a result for both.
GLM 4.6V or Kimi K2 0905: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GLM 4.6V for output tokens 2.8x cheaper ($0.90 vs $2.50 per million) and work measured by LiveCodeBench (Artificial Analysis run) or Terminal-Bench Hard (Artificial Analysis run), where it leads.
- Shared results
- 11
- GLM 4.6V only
- 27
- Kimi K2 0905 only
- 6
- Like-for-like categories
- 7 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
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
Kimi K2 0905
Kimi K2 0905 leads, 15.3 against 11.2.
Same board, both measured
Web design
Design Arena: websites
No call
Only Kimi K2 0905 has a Design Arena: websites result, 1089.
Directional only
Long documents
Listed context window
Kimi K2 0905
Kimi K2 0905 takes 262K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 4.6V
GLM 4.6V $0.0008, Kimi K2 0905 $0.0019 per turn.
List prices
At a glance
- Coding index
- GLM 4.6V not benchmarked, Kimi K2 0905 not benchmarked.
- Widest category
- Agentic work, 41.8 points apart on average over 1 shared benchmark.
- Widest single gap
- τ²-Bench Telecom: GLM 4.6V 31.6% against Kimi K2 0905 73.4%, 41.8 points apart on a 0 to 100 scale.
- Output price
- GLM 4.6V $0.90 against Kimi K2 0905 $2.50 per million output tokens, 2.8x apart.
- Context window
- GLM 4.6V takes 131K tokens, Kimi K2 0905 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 4.6V | Bars | Kimi K2 0905 | 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 0905 | Leader |
|---|---|---|---|
| LiveCodeBench (Artificial Analysis run) | 41.1% | 61.0% | Kimi K2 0905 |
| Terminal-Bench Hard (Artificial Analysis run) | 14.4% | 23.5% | Kimi K2 0905 |
| WeirdML | Not measured | 36.7% | Only Kimi K2 0905 measured |
| ALE-Bench | Not measured | 267.1 | Only Kimi K2 0905 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 33 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 4.6V | Kimi K2 0905 | Leader |
|---|---|---|---|
| AA Intelligence Index | 11.2 | 15.3 | Kimi K2 0905 |
| Neither model has a result on the other 1 board here | |||
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 4.6V | Kimi K2 0905 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 31.6% | 73.4% | Kimi K2 0905 |
| Neither model has a result on the other 40 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | GLM 4.6V | Kimi K2 0905 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 71.9% | 76.7% | Kimi K2 0905 |
| Humanity's Last Exam (Artificial Analysis run) | 9.6% | 6.4% | GLM 4.6V |
| 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 |
| Kagi LLM Benchmark | Not measured | 52.2% | Only Kimi K2 0905 measured |
| Neither model has a result on the other 14 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GLM 4.6V | Kimi K2 0905 | Leader |
|---|---|---|---|
| AA Math Index | 85.3 | 57.3 | GLM 4.6V |
| AIME 2025 (Artificial Analysis run) | 85.3% | 57.3% | GLM 4.6V |
| Neither model has a result on the other 11 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 4.6V | Kimi K2 0905 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 79.9% | 81.9% | Kimi K2 0905 |
| 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 13 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GLM 4.6V | Kimi K2 0905 | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1089 | Only Kimi K2 0905 measured |
| Design Arena: all code categories | Not measured | 1098 | Only Kimi K2 0905 measured |
| Neither model has a result on the other 7 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GLM 4.6V | Kimi K2 0905 | Leader |
|---|---|---|---|
| IFBench | 30.1% | 41.7% | Kimi K2 0905 |
| Long context reasoning (AA-LCR) | 48.7% | 53.0% | Kimi K2 0905 |
| Fiction.LiveBench | Not measured | 66.7% | Only Kimi K2 0905 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 27 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.6V | Kimi K2 0905 | 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 0905 |
|---|---|---|
| Maker | Z.ai | Moonshot AI |
| Weights | Open weights | Open weights |
| Released | December 8, 2025 | September 4, 2025 |
| Context window | 131K | 262K |
| Input $/Mtok | $0.30 | $0.60 |
| Output $/Mtok | $0.90 | $2.50 |
| 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 0905 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GLM 4.6V for output tokens 2.8x cheaper ($0.90 vs $2.50 per million) and work measured by LiveCodeBench (Artificial Analysis run) or Terminal-Bench Hard (Artificial Analysis run), where it leads.
Which is cheaper, GLM 4.6V or Kimi K2 0905?
GLM 4.6V costs $0.30 per million input tokens and $0.90 per million output tokens; Kimi K2 0905 costs $0.60 and $2.50. Cost per solved task is known for neither of them.
Which has the bigger context window, GLM 4.6V or Kimi K2 0905?
Kimi K2 0905, with 262K tokens against 131K.
How many benchmarks is the GLM 4.6V vs Kimi K2 0905 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 0905's on 0. Where only one model has a result, the page says so instead of naming a winner.