GLM 4.7 vs Kimi K2 Thinking
Updated October 21, 2026. 27 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick Kimi K2 Thinking for coding: it scores 63.4 on the Vibe Coding Index against 33.6 for GLM 4.7.
Pick GLM 4.7 when output tokens cost 1.3x less, or when a solved task costs $0.29 against $0.61.
- Shared results
- 27
- GLM 4.7 only
- 53
- Kimi K2 Thinking only
- 3
- Like-for-like categories
- 8 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, four public coding benchmarks
Kimi K2 Thinking
Kimi K2 Thinking scores 63.4 against 33.6.
One benchmark behind each score
Intelligence
AA Intelligence Index
GLM 4.7
GLM 4.7 leads, 22.2 against 22.0.
Same board, both measured
Reasoning
GPQA Diamond
GLM 4.7
GLM 4.7 leads, 80.0% against 78.5%.
Same board, both measured
Math
AIME
GLM 4.7
GLM 4.7 leads, 93.3% against 85.4%.
Same board, both measured
Knowledge
MMLU Pro
GLM 4.7
GLM 4.7 leads, 82.7% against 81.1%.
Same board, both measured
Web design
Design Arena: websites
GLM 4.7
GLM 4.7 leads, 1226 against 1113.
Same board, both measured
Long documents
Listed context window
Kimi K2 Thinking
Kimi K2 Thinking takes 262K tokens against 205K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 4.7
GLM 4.7 $0.0015, Kimi K2 Thinking $0.0019 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GLM 4.7
GLM 4.7 $0.29, Kimi K2 Thinking $0.61.
Published attempt cost and pass rate
At a glance
- Coding index
- GLM 4.7 33.6 from 1 benchmark, Kimi K2 Thinking 63.4 from 1 benchmark.
- Widest category
- Web design, 14.1 points apart on average over 1 shared benchmark.
- Widest single gap
- LiveCodeBench (Vals AI): GLM 4.7 82.2% against Kimi K2 Thinking 63.1%, 19.1 points apart on a 0 to 100 scale.
- Output price
- GLM 4.7 $1.98 against Kimi K2 Thinking $2.50 per million output tokens, 1.3x apart.
- Context window
- GLM 4.7 takes 205K tokens, Kimi K2 Thinking 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 4.7 | Bars | Kimi K2 Thinking | Ties |
|---|---|---|---|---|
| Coding | 4 | 2 | ||
| Intelligence | 1 | 1 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 3 | 0 | ||
| Math | 3 | 1 | ||
| Knowledge | 4 | 3 | ||
| Web design | 1 | 0 | ||
| General | 0 | 3 | ||
| All shared boards | 17 | 10 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index42
| Benchmark | GLM 4.7 | Kimi K2 Thinking | Leader |
|---|---|---|---|
| SWE-bench Verified | Not measured | 63.4% | Only Kimi K2 Thinking measured |
| LMArena WebDev | 1435 | Not measured | Only GLM 4.7 measured |
| AA Coding Index | 45.3 | Not measured | Only GLM 4.7 measured |
| LiveCodeBench (Vals AI) | 82.2% | 63.1% | GLM 4.7 |
| SWE-bench Verified (Vals AI) | 69.4% | 60.2% | GLM 4.7 |
| LiveCodeBench (Artificial Analysis run) | 89.4% | 85.3% | GLM 4.7 |
| Terminal-Bench Hard (Artificial Analysis run) | 31.8% | 31.1% | GLM 4.7 |
| Terminal-Bench | 33.4% | 35.7% | Kimi K2 Thinking |
| WeirdML | Not measured | 42.8% | Only Kimi K2 Thinking measured |
| ALE-Bench | 399.5 | 597.5 | Kimi K2 Thinking |
| LMArena Text: coding | 1454 | Not measured | Only GLM 4.7 measured |
| LMArena Text: software and IT | 1453 | Not measured | Only GLM 4.7 measured |
| LMArena WebDev: React | 1420 | Not measured | Only GLM 4.7 measured |
| LMArena WebDev: HTML | 1447 | Not measured | Only GLM 4.7 measured |
| Neither model has a result on the other 24 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | GLM 4.7 | Kimi K2 Thinking | Leader |
|---|---|---|---|
| AA Intelligence Index | 22.2 | 22.0 | GLM 4.7 |
| Epoch Capabilities Index | 143.6 | 145.8 | Kimi K2 Thinking |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 4.7 | Kimi K2 Thinking | Leader |
|---|---|---|---|
| Design Arena: agents building full-stack apps | 1050 | Not measured | Only GLM 4.7 measured |
| Design Arena: agents building mobile apps | 1105 | Not measured | Only GLM 4.7 measured |
| Design Arena: agents building Android apps | 1118 | Not measured | Only GLM 4.7 measured |
| Design Arena: agents building Godot games | 1058 | Not measured | Only GLM 4.7 measured |
| τ²-Bench Telecom | 95.9% | 93.0% | GLM 4.7 |
| METR time horizons | Not measured | 59.2% | Only Kimi K2 Thinking measured |
| Vending-Bench 2 | $2,377 | Not measured | Only GLM 4.7 measured |
| Neither model has a result on the other 32 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | GLM 4.7 | Kimi K2 Thinking | Leader |
|---|---|---|---|
| GPQA Diamond | 80.0% | 78.5% | GLM 4.7 |
| GPQA Diamond (Artificial Analysis run) | 85.9% | 83.8% | GLM 4.7 |
| Humanity's Last Exam (Artificial Analysis run) | 27.4% | 23.8% | GLM 4.7 |
| GPQA Diamond (Epoch AI run) | 83.3% | Not measured | Only GLM 4.7 measured |
| SimpleBench | 47.7% | Not measured | Only GLM 4.7 measured |
| Chess puzzles | 21.0% | Not measured | Only GLM 4.7 measured |
| LMArena Text: hard prompts | 1443 | Not measured | Only GLM 4.7 measured |
| LMArena Text: expert | 1424 | Not measured | Only GLM 4.7 measured |
| LMArena Text: hard prompts in English | 1452 | Not measured | Only GLM 4.7 measured |
| Neither model has a result on the other 12 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index31
| Benchmark | GLM 4.7 | Kimi K2 Thinking | Leader |
|---|---|---|---|
| AIME | 93.3% | 85.4% | GLM 4.7 |
| MGSM (Vals AI) | 88.2% | 90.2% | Kimi K2 Thinking |
| AA Math Index | 95.0 | 94.7 | GLM 4.7 |
| AIME 2025 (Artificial Analysis run) | 95.0% | 94.7% | GLM 4.7 |
| OTIS Mock AIME | 83.3% | Not measured | Only GLM 4.7 measured |
| ProofBench | 6.0% | Not measured | Only GLM 4.7 measured |
| LMArena Text: math | 1424 | Not measured | Only GLM 4.7 measured |
| LMArena Text: mathematical work | 1411 | Not measured | Only GLM 4.7 measured |
| Neither model has a result on the other 5 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index43
| Benchmark | GLM 4.7 | Kimi K2 Thinking | Leader |
|---|---|---|---|
| MMLU Pro | 82.7% | 81.1% | GLM 4.7 |
| LegalBench (Vals AI) | 83.4% | 80.2% | GLM 4.7 |
| CaseLaw (Vals AI) | 54.9% | 65.7% | Kimi K2 Thinking |
| CorpFin (Vals AI) | 46.4% | 60.6% | Kimi K2 Thinking |
| TaxEval (Vals AI) | 68.8% | 71.7% | Kimi K2 Thinking |
| MedQA (Vals AI) | 93.7% | 92.6% | GLM 4.7 |
| MedCode (Vals AI) | 32.8% | Not measured | Only GLM 4.7 measured |
| MMLU Pro (Artificial Analysis run) | 85.6% | 84.8% | GLM 4.7 |
| SimpleQA Verified | 32.2% | Not measured | Only GLM 4.7 measured |
| LMArena Text: science | 1457 | Not measured | Only GLM 4.7 measured |
| LMArena Text: medicine and healthcare | 1469 | Not measured | Only GLM 4.7 measured |
| LMArena Text: legal and government | 1444 | Not measured | Only GLM 4.7 measured |
| LMArena Text: business and finance | 1429 | Not measured | Only GLM 4.7 measured |
| Neither model has a result on the other 4 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 4.7 | Kimi K2 Thinking | Leader |
|---|---|---|---|
| Design Arena: websites | 1226 | 1113 | GLM 4.7 |
| Design Arena: UI components | 1199 | Not measured | Only GLM 4.7 measured |
| Design Arena: game dev | 1189 | Not measured | Only GLM 4.7 measured |
| Design Arena: data viz | 1195 | Not measured | Only GLM 4.7 measured |
| Design Arena: 3D | 1191 | Not measured | Only GLM 4.7 measured |
| Design Arena: all code categories | 1217 | Not measured | Only GLM 4.7 measured |
| Design Arena: SVG | 1142 | Not measured | Only GLM 4.7 measured |
| Design Arena: ASCII art | 1165 | Not measured | Only GLM 4.7 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | GLM 4.7 | Kimi K2 Thinking | Leader |
|---|---|---|---|
| MedScribe (Vals AI) | 68.6% | Not measured | Only GLM 4.7 measured |
| IFBench | 67.9% | 68.1% | Kimi K2 Thinking |
| Long context reasoning (AA-LCR) | 71.0% | 72.0% | Kimi K2 Thinking |
| CL-bench | 15.9% | 17.6% | Kimi K2 Thinking |
| CL-bench Life | 10.9% | Not measured | Only GLM 4.7 measured |
| LMArena Text | 1435 | Not measured | Only GLM 4.7 measured |
| LMArena Text: instruction following | 1412 | Not measured | Only GLM 4.7 measured |
| LMArena Text: creative writing | 1401 | Not measured | Only GLM 4.7 measured |
| LMArena Text: English | 1453 | Not measured | Only GLM 4.7 measured |
| LMArena Text: non-English | 1417 | Not measured | Only GLM 4.7 measured |
| LMArena Text: Chinese | 1495 | Not measured | Only GLM 4.7 measured |
| LMArena Text: German | 1426 | Not measured | Only GLM 4.7 measured |
| LMArena Text: Japanese | 1439 | Not measured | Only GLM 4.7 measured |
| LMArena Text: Korean | 1399 | Not measured | Only GLM 4.7 measured |
| LMArena Text: French | 1432 | Not measured | Only GLM 4.7 measured |
| LMArena Text: Spanish | 1434 | Not measured | Only GLM 4.7 measured |
| LMArena Text: Russian | 1423 | Not measured | Only GLM 4.7 measured |
| LMArena Text: Polish | 1435 | Not measured | Only GLM 4.7 measured |
| LMArena Text: multi-turn | 1446 | Not measured | Only GLM 4.7 measured |
| LMArena Text: longer queries | 1432 | Not measured | Only GLM 4.7 measured |
| LMArena Text: writing and language | 1411 | Not measured | Only GLM 4.7 measured |
| LMArena Text: entertainment and media | 1400 | Not measured | Only GLM 4.7 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 4.7 | Kimi K2 Thinking | 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 4.7 costs less
Repository review
50K input + 3K output tokens
GLM 4.7 costs less
Agent loop
200K input + 10K output tokens
GLM 4.7 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 4.7 | MoonshotAI Kimi K2 Thinking |
|---|---|---|
| Maker | Z.ai | Moonshot AI |
| Weights | Open weights | Open weights |
| Released | December 22, 2025 | November 6, 2025 |
| Context window | 205K | 262K |
| Input $/Mtok | $0.54 | $0.60 |
| Output $/Mtok | $1.98 | $2.50 |
| Coding index | 33.6 | 63.4 |
| Independent measurements behind it | 1 | 1 |
| Cost per solved task | $0.29 | $0.61 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.7 better than Kimi K2 Thinking for coding?
Pick Kimi K2 Thinking for coding: it scores 63.4 on the Vibe Coding Index against 33.6 for GLM 4.7. Pick GLM 4.7 when output tokens cost 1.3x less, or when a solved task costs $0.29 against $0.61.
Which is cheaper, GLM 4.7 or Kimi K2 Thinking?
GLM 4.7 costs $0.54 per million input tokens and $1.98 per million output tokens; Kimi K2 Thinking costs $0.60 and $2.50. Per solved SWE-bench Verified task, GLM 4.7 costs $0.29 and Kimi K2 Thinking $0.61.
Which has the bigger context window, GLM 4.7 or Kimi K2 Thinking?
Kimi K2 Thinking, with 262K tokens against 205K.
How many benchmarks is the GLM 4.7 vs Kimi K2 Thinking comparison based on?
27 benchmarks have a result for both models as of October 21, 2026. GLM 4.7's coding index rests on 1 independent benchmark and Kimi K2 Thinking's on 1. Where only one model has a result, the page says so instead of naming a winner.