GLM 5.2 vs Kimi K3.1 Pro
Kimi K3.1 Pro leads GLM 5.2 on coding, 82.2 to 61.1 on the Vibe Coding Index, and GLM 5.2 costs 2.3x less per output token ($4.40 vs $10.00 per million). Updated October 29, 2026; 18 benchmarks have a result for both.
GLM 5.2 or Kimi K3.1 Pro: which to pick
Pick Kimi K3.1 Pro for coding: it scores 82.2 on the Vibe Coding Index against 61.1 for GLM 5.2.
Pick GLM 5.2 for output tokens 2.3x cheaper ($4.40 vs $10.00 per million).
- Shared results
- 18
- GLM 5.2 only
- 85
- Kimi K3.1 Pro only
- 17
- Like-for-like categories
- 6 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
Kimi K3.1 Pro
Kimi K3.1 Pro scores 82.2 against 61.1.
4 benchmarks behind each score
Intelligence
AA Intelligence Index
No call
Only Kimi K3.1 Pro has a AA Intelligence Index result, 46.0.
Directional only
Agentic work
AA Agentic Index
No call
Only GLM 5.2 has a AA Agentic Index result, 38.4.
Directional only
Reasoning
GPQA Diamond
No call
Only GLM 5.2 has a GPQA Diamond result, 85.6%.
Directional only
Knowledge
MMLU Pro
No call
Only GLM 5.2 has a MMLU Pro result, 86.7%.
Directional only
Web design
Design Arena: websites
Kimi K3.1 Pro
Kimi K3.1 Pro leads, 1325 against 1283.
Same board, both measured
General
Vals Index
No call
Only Kimi K3.1 Pro has a Vals Index result, 62.5%.
Directional only
Long documents
Listed context window
No call
Both take 1.05M tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 5.2
GLM 5.2 $0.0036, Kimi K3.1 Pro $0.0070 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GLM 5.2 has the number, $0.86.
Directional only
At a glance
- Coding index
- GLM 5.2 61.1 from 4 benchmarks, Kimi K3.1 Pro 82.2 from 4 benchmarks.
- Widest category
- Math, 46.6 points apart on average over 2 shared benchmarks.
- Widest single gap
- ProofBench: GLM 5.2 35.0% against Kimi K3.1 Pro 91.0%, 56.0 points apart on a 0 to 100 scale.
- Output price
- GLM 5.2 $4.40 against Kimi K3.1 Pro $10.00 per million output tokens, 2.3x apart.
- Context window
- Both take 1.05M 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 5.2 | Bars | Kimi K3.1 Pro | Ties |
|---|---|---|---|---|
| Coding | 0 | 6 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 1 | ||
| Math | 0 | 2 | ||
| Web design | 0 | 7 | ||
| General | 0 | 1 | ||
| All shared boards | 0 | 18 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index06
| Benchmark | GLM 5.2 | Kimi K3.1 Pro | Leader |
|---|---|---|---|
| LiveBench coding | Not measured | 81.8% | Only Kimi K3.1 Pro measured |
| LMArena WebDev | 1606 | 1695 | Kimi K3.1 Pro |
| AA Coding Index | 68.8 | Not measured | Only GLM 5.2 measured |
| LiveCodeBench (Vals AI) | 69.5% | Not measured | Only GLM 5.2 measured |
| SWE-bench Verified (Vals AI) | 82.8% | Not measured | Only GLM 5.2 measured |
| Terminal-Bench 4 (Vals AI) | Not measured | 63.6% | Only Kimi K3.1 Pro measured |
| Vibe Code Bench (Vals AI) | 64.0% | 84.9% | Kimi K3.1 Pro |
| Code migration (Vals AI) | 37.9% | Not measured | Only GLM 5.2 measured |
| ProgramBench (Vals AI) | 0.5% | Not measured | Only GLM 5.2 measured |
| IOI (Vals AI) | Not measured | 81.9% | Only Kimi K3.1 Pro measured |
| Terminal-Bench 2.1 (Vals AI) | 67.8% | Not measured | Only GLM 5.2 measured |
| SkillsBench (Vals AI) | 45.1% | Not measured | Only GLM 5.2 measured |
| SciCode | 51.2% | 60.8% | Kimi K3.1 Pro |
| Terminal-Bench Hard (Artificial Analysis run) | 50.8% | Not measured | Only GLM 5.2 measured |
| FrontierCode | 24.5% | Not measured | Only GLM 5.2 measured |
| WeirdML | 70.1% | Not measured | Only GLM 5.2 measured |
| ALE-Bench | 1047.0 | 1932.8 | Kimi K3.1 Pro |
| LMArena Text: coding | 1487 | Not measured | Only GLM 5.2 measured |
| LMArena Text: software and IT | 1487 | Not measured | Only GLM 5.2 measured |
| LMArena WebDev: React | 1619 | 1701 | Kimi K3.1 Pro |
| LMArena WebDev: HTML | 1568 | 1706 | Kimi K3.1 Pro |
| SWE Atlas: Codebase QnA | 48.1% | Not measured | Only GLM 5.2 measured |
| SWE Atlas: Refactoring | 42.4% | Not measured | Only GLM 5.2 measured |
| SWE Atlas: Test Writing | 41.5% | Not measured | Only GLM 5.2 measured |
| Neither model has a result on the other 19 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GLM 5.2 | Kimi K3.1 Pro | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 46.0 | Only Kimi K3.1 Pro measured |
| Epoch Capabilities Index | 151.9 | Not measured | Only GLM 5.2 measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 5.2 | Kimi K3.1 Pro | Leader |
|---|---|---|---|
| AA Agentic Index | 38.4 | Not measured | Only GLM 5.2 measured |
| Design Arena: agents building web apps | 1224 | Not measured | Only GLM 5.2 measured |
| Design Arena: agents building full-stack apps | 1241 | Not measured | Only GLM 5.2 measured |
| Design Arena: agents building mobile apps | 1161 | Not measured | Only GLM 5.2 measured |
| Design Arena: agents building Android apps | 1177 | Not measured | Only GLM 5.2 measured |
| Design Arena: agents building Godot games | 1142 | Not measured | Only GLM 5.2 measured |
| Design Arena: agents building games | 1171 | Not measured | Only GLM 5.2 measured |
| Design Arena: HTML slides | 1071 | Not measured | Only GLM 5.2 measured |
| Design Arena: PowerPoint slides | 1147 | Not measured | Only GLM 5.2 measured |
| LiveBench research | Not measured | 77.9% | Only Kimi K3.1 Pro measured |
| LiveBench browser | Not measured | 75.9% | Only Kimi K3.1 Pro measured |
| LiveBench long agentic | Not measured | 39.1% | Only Kimi K3.1 Pro measured |
| SREBench (Vals AI) | 0.0% | Not measured | Only GLM 5.2 measured |
| Finance Agent (Vals AI) | 49.7% | Not measured | Only GLM 5.2 measured |
| Tax Agent Bench (Vals AI) | Not measured | 24.9% | Only Kimi K3.1 Pro measured |
| Legal Research Bench (Vals AI) | 31.3% | 37.5% | Kimi K3.1 Pro |
| Harvey's Legal Agent Benchmark (Vals AI) | Not measured | 11.7% | Only Kimi K3.1 Pro measured |
| Excel Modeling Benchmark (Vals AI) | 61.5% | Not measured | Only GLM 5.2 measured |
| BioMysteryBench (Vals AI) | Not measured | 75.6% | Only Kimi K3.1 Pro measured |
| Terminal-Bench-Science (Vals AI) | Not measured | 31.4% | Only Kimi K3.1 Pro measured |
| Vals Science Index | Not measured | 44.4% | Only Kimi K3.1 Pro measured |
| τ²-Bench Telecom | 99.1% | Not measured | Only GLM 5.2 measured |
| APEX-Agents | 45.2% | Not measured | Only GLM 5.2 measured |
| PostTrainBench | 31.7% | Not measured | Only GLM 5.2 measured |
| Vending-Bench 2 | $8,314 | Not measured | Only GLM 5.2 measured |
| MCP Atlas | 77.8% | Not measured | Only GLM 5.2 measured |
| Neither model has a result on the other 16 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 5.2 | Kimi K3.1 Pro | Leader |
|---|---|---|---|
| GPQA Diamond | 85.6% | Not measured | Only GLM 5.2 measured |
| LiveBench reasoning | Not measured | 91.1% | Only Kimi K3.1 Pro measured |
| MysteryMechanism (Vals AI) | Not measured | 26.1% | Only Kimi K3.1 Pro measured |
| GPQA Diamond (Artificial Analysis run) | 89.5% | Not measured | Only GLM 5.2 measured |
| Humanity's Last Exam (Artificial Analysis run) | 41.1% | 52.5% | Kimi K3.1 Pro |
| GPQA Diamond (Epoch AI run) | 91.9% | Not measured | Only GLM 5.2 measured |
| ARC-AGI | 77.0% | Not measured | Only GLM 5.2 measured |
| ARC-AGI-2 | 22.8% | Not measured | Only GLM 5.2 measured |
| SimpleBench | 58.8% | Not measured | Only GLM 5.2 measured |
| Chess puzzles | 22.0% | Not measured | Only GLM 5.2 measured |
| Mystery game puzzles | 19.0% | Not measured | Only GLM 5.2 measured |
| EBR-bench | 9.5% | Not measured | Only GLM 5.2 measured |
| LMArena Text: hard prompts | 1481 | Not measured | Only GLM 5.2 measured |
| LMArena Text: expert | 1488 | Not measured | Only GLM 5.2 measured |
| LMArena Text: hard prompts in English | 1481 | Not measured | Only GLM 5.2 measured |
| Kagi LLM Benchmark | 62.6% | Not measured | Only GLM 5.2 measured |
| Neither model has a result on the other 7 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GLM 5.2 | Kimi K3.1 Pro | Leader |
|---|---|---|---|
| OTIS Mock AIME | 86.4% | Not measured | Only GLM 5.2 measured |
| FrontierMath, tiers 1 to 3 | 59.2% | Not measured | Only GLM 5.2 measured |
| FrontierMath, tier 4 | 29.3% | Not measured | Only GLM 5.2 measured |
| ProofBench | 35.0% | 91.0% | Kimi K3.1 Pro |
| LMArena Text: math | 1484 | Not measured | Only GLM 5.2 measured |
| LMArena Text: mathematical work | 1489 | Not measured | Only GLM 5.2 measured |
| MathArena | 22.4% | 59.6% | Kimi K3.1 Pro |
| Neither model has a result on the other 7 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index10 boards, none shared
| Benchmark | GLM 5.2 | Kimi K3.1 Pro | Leader |
|---|---|---|---|
| MMLU Pro | 86.7% | Not measured | Only GLM 5.2 measured |
| LegalBench (Vals AI) | 84.1% | Not measured | Only GLM 5.2 measured |
| CorpFin (Vals AI) | 66.1% | Not measured | Only GLM 5.2 measured |
| TaxEval (Vals AI) | 73.3% | Not measured | Only GLM 5.2 measured |
| MedCode (Vals AI) | 40.8% | Not measured | Only GLM 5.2 measured |
| SimpleQA Verified | 34.2% | Not measured | Only GLM 5.2 measured |
| LMArena Text: science | 1488 | Not measured | Only GLM 5.2 measured |
| LMArena Text: medicine and healthcare | 1475 | Not measured | Only GLM 5.2 measured |
| LMArena Text: legal and government | 1487 | Not measured | Only GLM 5.2 measured |
| LMArena Text: business and finance | 1451 | Not measured | Only GLM 5.2 measured |
| Neither model has a result on the other 10 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index07
| Benchmark | GLM 5.2 | Kimi K3.1 Pro | Leader |
|---|---|---|---|
| Design Arena: websites | 1283 | 1325 | Kimi K3.1 Pro |
| Design Arena: UI components | 1291 | 1400 | Kimi K3.1 Pro |
| Design Arena: game dev | 1266 | 1410 | Kimi K3.1 Pro |
| Design Arena: data viz | 1286 | 1300 | Kimi K3.1 Pro |
| Design Arena: 3D | 1283 | 1451 | Kimi K3.1 Pro |
| Design Arena: all code categories | 1285 | 1354 | Kimi K3.1 Pro |
| Design Arena: SVG | 1208 | 1328 | Kimi K3.1 Pro |
| Design Arena: ASCII art | 1188 | Not measured | Only GLM 5.2 measured |
| LiveBench design | Not measured | 77.8% | Only Kimi K3.1 Pro measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 5.2 | Kimi K3.1 Pro | Leader |
|---|---|---|---|
| Vals Index | Not measured | 62.5% | Only Kimi K3.1 Pro measured |
| LiveBench data analysis | Not measured | 79.1% | Only Kimi K3.1 Pro measured |
| MedScribe (Vals AI) | 83.5% | Not measured | Only GLM 5.2 measured |
| IFBench | 73.3% | Not measured | Only GLM 5.2 measured |
| Long context reasoning (AA-LCR) | 78.3% | 86.3% | Kimi K3.1 Pro |
| DTBench | 93.6% | Not measured | Only GLM 5.2 measured |
| LMCA | 45.8% | Not measured | Only GLM 5.2 measured |
| Surface Evolver Bench | 55.6% | Not measured | Only GLM 5.2 measured |
| LMArena Text | 1470 | Not measured | Only GLM 5.2 measured |
| LMArena Text: instruction following | 1466 | Not measured | Only GLM 5.2 measured |
| LMArena Text: creative writing | 1461 | Not measured | Only GLM 5.2 measured |
| LMArena Text: English | 1475 | Not measured | Only GLM 5.2 measured |
| LMArena Text: non-English | 1458 | Not measured | Only GLM 5.2 measured |
| LMArena Text: Chinese | 1523 | Not measured | Only GLM 5.2 measured |
| LMArena Text: German | 1471 | Not measured | Only GLM 5.2 measured |
| LMArena Text: Japanese | 1450 | Not measured | Only GLM 5.2 measured |
| LMArena Text: Korean | 1438 | Not measured | Only GLM 5.2 measured |
| LMArena Text: French | 1476 | Not measured | Only GLM 5.2 measured |
| LMArena Text: Spanish | 1478 | Not measured | Only GLM 5.2 measured |
| LMArena Text: Russian | 1467 | Not measured | Only GLM 5.2 measured |
| LMArena Text: Polish | 1457 | Not measured | Only GLM 5.2 measured |
| LMArena Text: multi-turn | 1469 | Not measured | Only GLM 5.2 measured |
| LMArena Text: longer queries | 1479 | Not measured | Only GLM 5.2 measured |
| LMArena Text: writing and language | 1461 | Not measured | Only GLM 5.2 measured |
| LMArena Text: entertainment and media | 1448 | Not measured | Only GLM 5.2 measured |
| EQ-Bench 4 | 1208 | Not measured | Only GLM 5.2 measured |
| Neither model has a result on the other 22 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 5.2 | Kimi K3.1 Pro | 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 5.2 costs less
Repository review
50K input + 3K output tokens
GLM 5.2 costs less
Agent loop
200K input + 10K output tokens
GLM 5.2 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 5.2 | MoonshotAI Kimi K3.1 Pro |
|---|---|---|
| Maker | Z.ai | Moonshot AI |
| Weights | Open weights | No public weights listed |
| Released | June 16, 2026 | October 23, 2026 |
| Context window | 1.05M | 1.05M |
| Input $/Mtok | $1.40 | $2.00 |
| Output $/Mtok | $4.40 | $10.00 |
| Coding index | 61.1 | 82.2 |
| Independent measurements behind it | 4 | 4 |
| Cost per solved task | $0.86 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 5.2 better than Kimi K3.1 Pro for coding?
Pick Kimi K3.1 Pro for coding: it scores 82.2 on the Vibe Coding Index against 61.1 for GLM 5.2. Pick GLM 5.2 for output tokens 2.3x cheaper ($4.40 vs $10.00 per million).
Which is cheaper, GLM 5.2 or Kimi K3.1 Pro?
GLM 5.2 costs $1.40 per million input tokens and $4.40 per million output tokens; Kimi K3.1 Pro costs $2.00 and $10.00. Cost per solved task is known for GLM 5.2 only.
Which has the bigger context window, GLM 5.2 or Kimi K3.1 Pro?
Neither: both take 1.05M tokens.
How many benchmarks is the GLM 5.2 vs Kimi K3.1 Pro comparison based on?
18 benchmarks have a result for both models as of October 29, 2026. GLM 5.2's coding index rests on 4 independent benchmarks and Kimi K3.1 Pro's on 4. Where only one model has a result, the page says so instead of naming a winner.