Kimi K2.6 vs Qwen3.8 Max (0902)
Updated October 21, 2026. 95 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick Qwen3.8 Max (0902) for coding: it scores 63.5 on the Vibe Coding Index against 44.2 for Kimi K2.6.
Pick Kimi K2.6 when output tokens cost 1.8x less, or when a solved task costs $0.35 against $1.32.
- Shared results
- 95
- Kimi K2.6 only
- 14
- Qwen3.8 Max (0902) only
- 18
- 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
Qwen3.8 Max (0902)
Qwen3.8 Max (0902) scores 63.5 against 44.2.
1 and 2 benchmarks behind the scores
Intelligence
AA Intelligence Index
Qwen3.8 Max (0902)
Qwen3.8 Max (0902) leads, 45.4 against 27.0.
Same board, both measured
Agentic work
AA Agentic Index
Qwen3.8 Max (0902)
Qwen3.8 Max (0902) leads, 56.0 against 20.5.
Same board, both measured
Reasoning
GPQA Diamond
Qwen3.8 Max (0902)
Qwen3.8 Max (0902) leads, 93.7% against 89.1%.
Same board, both measured
Knowledge
MMLU Pro
Qwen3.8 Max (0902)
Qwen3.8 Max (0902) leads, 88.6% against 87.6%.
Same board, both measured
Web design
Design Arena: websites
Qwen3.8 Max (0902)
Qwen3.8 Max (0902) leads, 1275 against 1269.
Same board, both measured
Long documents
Listed context window
Qwen3.8 Max (0902)
Qwen3.8 Max (0902) takes 1M tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Kimi K2.6
Kimi K2.6 $0.0024, Qwen3.8 Max (0902) $0.0050 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
Kimi K2.6
Kimi K2.6 $0.35, Qwen3.8 Max (0902) $1.32.
Published attempt cost and pass rate
At a glance
- Coding index
- Kimi K2.6 44.2 from 1 benchmark, Qwen3.8 Max (0902) 63.5 from 2 benchmarks.
- Widest category
- Agentic work, 17.7 points apart on average over 12 shared benchmarks.
- Widest single gap
- Design Arena: spreadsheets: Kimi K2.6 1276 against Qwen3.8 Max (0902) 868, 51.0 points apart on a 0 to 100 scale.
- Output price
- Kimi K2.6 $3.41 against Qwen3.8 Max (0902) $6.00 per million output tokens, 1.8x apart.
- Context window
- Kimi K2.6 takes 262K tokens, Qwen3.8 Max (0902) 1M.
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 | Kimi K2.6 | Bars | Qwen3.8 Max (0902) | Ties |
|---|---|---|---|---|
| Coding | 1 | 12 | 1 tie | |
| Intelligence | 0 | 2 | ||
| Agentic work | 1 | 11 | ||
| Reasoning | 0 | 12 | ||
| Math | 0 | 6 | ||
| Knowledge | 3 | 8 | ||
| Web design | 0 | 7 | ||
| General | 2 | 29 | ||
| All shared boards | 7 | 87 | 1 tie |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index112
| Benchmark | Kimi K2.6 | Qwen3.8 Max (0902) | Leader |
|---|---|---|---|
| LiveBench coding | Not measured | 60.3% | Only Qwen3.8 Max (0902) measured |
| LMArena WebDev | 1509 | 1674 | Qwen3.8 Max (0902) |
| AA Coding Index | 61.8 | 76.2 | Qwen3.8 Max (0902) |
| LiveCodeBench (Vals AI) | 86.8% | 87.8% | Qwen3.8 Max (0902) |
| SWE-bench Verified (Vals AI) | 76.2% | 85.6% | Qwen3.8 Max (0902) |
| Terminal-Bench 4 (Vals AI) | Not measured | 34.3% | Only Qwen3.8 Max (0902) measured |
| Vibe Code Bench (Vals AI) | 37.9% | 64.7% | Qwen3.8 Max (0902) |
| Code migration (Vals AI) | 27.8% | 24.0% | Kimi K2.6 |
| ProgramBench (Vals AI) | 0.0% | 0.0% | Tie |
| IOI (Vals AI) | Not measured | 68.9% | Only Qwen3.8 Max (0902) measured |
| Terminal-Bench 2.1 (Vals AI) | 53.6% | 67.4% | Qwen3.8 Max (0902) |
| SWE-bench Multimodal (Vals AI) | Not measured | 42.5% | Only Qwen3.8 Max (0902) measured |
| Vibe Code Bench 1-100 (Vals AI) | Not measured | 12.8% | Only Qwen3.8 Max (0902) measured |
| SkillsBench (Vals AI) | Not measured | 42.0% | Only Qwen3.8 Max (0902) measured |
| CyberBench (Vals AI) | Not measured | 28.6% | Only Qwen3.8 Max (0902) measured |
| SciCode | 51.5% | 53.2% | Qwen3.8 Max (0902) |
| Terminal-Bench Hard (Artificial Analysis run) | 43.9% | Not measured | Only Kimi K2.6 measured |
| FrontierSWE | Not measured | 17.8% | Only Qwen3.8 Max (0902) measured |
| WeirdML | 55.9% | Not measured | Only Kimi K2.6 measured |
| ALE-Bench | 1092.7 | Not measured | Only Kimi K2.6 measured |
| LMArena Text: coding | 1489 | 1502 | Qwen3.8 Max (0902) |
| LMArena Text: software and IT | 1484 | 1498 | Qwen3.8 Max (0902) |
| LMArena WebDev: React | 1505 | 1692 | Qwen3.8 Max (0902) |
| LMArena WebDev: HTML | 1485 | 1649 | Qwen3.8 Max (0902) |
| LMArena WebDev: from an image | 1521 | 1637 | Qwen3.8 Max (0902) |
| Neither model has a result on the other 13 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Kimi K2.6 | Qwen3.8 Max (0902) | Leader |
|---|---|---|---|
| AA Intelligence Index | 27.0 | 45.4 | Qwen3.8 Max (0902) |
| Epoch Capabilities Index | 150.9 | 157.4 | Qwen3.8 Max (0902) |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index111
| Benchmark | Kimi K2.6 | Qwen3.8 Max (0902) | Leader |
|---|---|---|---|
| AA Agentic Index | 20.5 | 56.0 | Qwen3.8 Max (0902) |
| Design Arena: agents building web apps | 1268 | 1301 | Qwen3.8 Max (0902) |
| Design Arena: agents building full-stack apps | 1155 | 1305 | Qwen3.8 Max (0902) |
| Design Arena: agents building mobile apps | 1156 | 1223 | Qwen3.8 Max (0902) |
| Design Arena: agents building Android apps | 1166 | 1207 | Qwen3.8 Max (0902) |
| Design Arena: agents building Godot games | 1120 | 1234 | Qwen3.8 Max (0902) |
| Design Arena: agents building games | 1141 | Not measured | Only Kimi K2.6 measured |
| Design Arena: HTML slides | 1184 | Not measured | Only Kimi K2.6 measured |
| Design Arena: PowerPoint slides | 1128 | 1257 | Qwen3.8 Max (0902) |
| Design Arena: spreadsheets | 1276 | 868 | Kimi K2.6 |
| LiveBench research | Not measured | 69.9% | Only Qwen3.8 Max (0902) measured |
| LiveBench browser | Not measured | 32.0% | Only Qwen3.8 Max (0902) measured |
| LiveBench long agentic | Not measured | 42.3% | Only Qwen3.8 Max (0902) measured |
| Finance Agent (Vals AI) | 44.9% | 50.6% | Qwen3.8 Max (0902) |
| Tax Agent Bench (Vals AI) | 12.4% | 32.1% | Qwen3.8 Max (0902) |
| Legal Research Bench (Vals AI) | 15.9% | 47.6% | Qwen3.8 Max (0902) |
| Excel Modeling Benchmark (Vals AI) | 57.9% | 60.1% | Qwen3.8 Max (0902) |
| Terminal-Bench-Science (Vals AI) | Not measured | 12.9% | Only Qwen3.8 Max (0902) measured |
| τ²-Bench Telecom | 95.9% | Not measured | Only Kimi K2.6 measured |
| APEX-Agents | Not measured | 63.3% | Only Qwen3.8 Max (0902) measured |
| OSWorld 2.0 | 4.6% | Not measured | Only Kimi K2.6 measured |
| Vending-Bench 2 | $6,205 | Not measured | Only Kimi K2.6 measured |
| LMArena Search | Not measured | 1261 | Only Qwen3.8 Max (0902) measured |
| Neither model has a result on the other 16 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index012
| Benchmark | Kimi K2.6 | Qwen3.8 Max (0902) | Leader |
|---|---|---|---|
| GPQA Diamond | 89.1% | 93.7% | Qwen3.8 Max (0902) |
| LiveBench reasoning | Not measured | 88.2% | Only Qwen3.8 Max (0902) measured |
| MysteryMechanism (Vals AI) | Not measured | 23.9% | Only Qwen3.8 Max (0902) measured |
| MMMU Pro (Vals AI) | 86.3% | 88.0% | Qwen3.8 Max (0902) |
| GPQA Diamond (Artificial Analysis run) | 91.1% | 92.8% | Qwen3.8 Max (0902) |
| Humanity's Last Exam (Artificial Analysis run) | 37.5% | 43.1% | Qwen3.8 Max (0902) |
| GPQA Diamond (Epoch AI run) | 90.8% | 92.7% | Qwen3.8 Max (0902) |
| Chess puzzles | 20.0% | 46.0% | Qwen3.8 Max (0902) |
| Mystery game puzzles | 18.0% | 38.0% | Qwen3.8 Max (0902) |
| EBR-bench | 2.4% | Not measured | Only Kimi K2.6 measured |
| LMArena Text: hard prompts | 1470 | 1496 | Qwen3.8 Max (0902) |
| LMArena Text: expert | 1491 | 1507 | Qwen3.8 Max (0902) |
| LMArena Text: hard prompts in English | 1471 | 1503 | Qwen3.8 Max (0902) |
| LMArena Vision: diagrams | 1298 | 1323 | Qwen3.8 Max (0902) |
| LMArena Vision: homework | 1310 | 1336 | Qwen3.8 Max (0902) |
| Neither model has a result on the other 6 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | Kimi K2.6 | Qwen3.8 Max (0902) | Leader |
|---|---|---|---|
| OTIS Mock AIME | 96.1% | 100.0% | Qwen3.8 Max (0902) |
| FrontierMath, tiers 1 to 3 | 57.2% | 74.7% | Qwen3.8 Max (0902) |
| FrontierMath, tier 4 | 25.6% | 46.3% | Qwen3.8 Max (0902) |
| ProofBench | 16.0% | 58.0% | Qwen3.8 Max (0902) |
| LMArena Text: math | 1474 | 1501 | Qwen3.8 Max (0902) |
| LMArena Text: mathematical work | 1480 | 1491 | Qwen3.8 Max (0902) |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index38
| Benchmark | Kimi K2.6 | Qwen3.8 Max (0902) | Leader |
|---|---|---|---|
| MMLU Pro | 87.6% | 88.6% | Qwen3.8 Max (0902) |
| LegalBench (Vals AI) | 84.7% | 83.6% | Kimi K2.6 |
| CaseLaw (Vals AI) | 61.2% | Not measured | Only Kimi K2.6 measured |
| CorpFin (Vals AI) | 66.7% | 65.8% | Kimi K2.6 |
| TaxEval (Vals AI) | 74.7% | 75.5% | Qwen3.8 Max (0902) |
| MortgageTax (Vals AI) | 65.8% | 64.0% | Kimi K2.6 |
| MedCode (Vals AI) | 40.1% | 40.7% | Qwen3.8 Max (0902) |
| SimpleQA Verified | 34.9% | 47.3% | Qwen3.8 Max (0902) |
| LMArena Text: science | 1468 | 1512 | Qwen3.8 Max (0902) |
| LMArena Text: medicine and healthcare | 1457 | 1503 | Qwen3.8 Max (0902) |
| LMArena Text: legal and government | 1462 | 1492 | Qwen3.8 Max (0902) |
| LMArena Text: business and finance | 1445 | 1480 | Qwen3.8 Max (0902) |
| Neither model has a result on the other 5 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index07
| Benchmark | Kimi K2.6 | Qwen3.8 Max (0902) | Leader |
|---|---|---|---|
| Design Arena: websites | 1269 | 1275 | Qwen3.8 Max (0902) |
| Design Arena: UI components | 1266 | 1340 | Qwen3.8 Max (0902) |
| Design Arena: game dev | 1251 | 1313 | Qwen3.8 Max (0902) |
| Design Arena: data viz | 1249 | 1281 | Qwen3.8 Max (0902) |
| Design Arena: 3D | 1268 | 1337 | Qwen3.8 Max (0902) |
| Design Arena: all code categories | 1272 | 1293 | Qwen3.8 Max (0902) |
| Design Arena: SVG | 1181 | Not measured | Only Kimi K2.6 measured |
| Design Arena: ASCII art | 1164 | 1201 | Qwen3.8 Max (0902) |
| LiveBench design | Not measured | 78.6% | Only Qwen3.8 Max (0902) measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index229
| Benchmark | Kimi K2.6 | Qwen3.8 Max (0902) | Leader |
|---|---|---|---|
| LiveBench data analysis | Not measured | 78.4% | Only Qwen3.8 Max (0902) measured |
| Public Benefits Bench (Vals AI) | 56.6% | 67.1% | Qwen3.8 Max (0902) |
| SAGE (Vals AI) | 50.2% | 51.2% | Qwen3.8 Max (0902) |
| MedScribe (Vals AI) | 78.2% | 85.0% | Qwen3.8 Max (0902) |
| Vals Multimodal Index | 56.4% | 65.4% | Qwen3.8 Max (0902) |
| IFBench | 76.0% | Not measured | Only Kimi K2.6 measured |
| Long context reasoning (AA-LCR) | 81.0% | 80.3% | Kimi K2.6 |
| DTBench | 90.9% | 92.0% | Qwen3.8 Max (0902) |
| LMCA | 37.3% | 46.2% | Qwen3.8 Max (0902) |
| Furniture assembly | 21.7% | 20.0% | Kimi K2.6 |
| ExploitBench | 18.4% | Not measured | Only Kimi K2.6 measured |
| LMArena Text | 1455 | 1483 | Qwen3.8 Max (0902) |
| LMArena Text: instruction following | 1451 | 1481 | Qwen3.8 Max (0902) |
| LMArena Text: creative writing | 1434 | 1477 | Qwen3.8 Max (0902) |
| LMArena Vision | 1282 | 1313 | Qwen3.8 Max (0902) |
| LMArena Text: English | 1460 | 1491 | Qwen3.8 Max (0902) |
| LMArena Text: non-English | 1446 | 1473 | Qwen3.8 Max (0902) |
| LMArena Text: Chinese | 1520 | 1539 | Qwen3.8 Max (0902) |
| LMArena Text: German | 1451 | 1478 | Qwen3.8 Max (0902) |
| LMArena Text: Japanese | 1443 | 1473 | Qwen3.8 Max (0902) |
| LMArena Text: Korean | 1427 | 1460 | Qwen3.8 Max (0902) |
| LMArena Text: French | 1471 | 1498 | Qwen3.8 Max (0902) |
| LMArena Text: Spanish | 1464 | 1489 | Qwen3.8 Max (0902) |
| LMArena Text: Russian | 1446 | 1482 | Qwen3.8 Max (0902) |
| LMArena Text: Polish | 1438 | 1471 | Qwen3.8 Max (0902) |
| LMArena Text: multi-turn | 1453 | 1489 | Qwen3.8 Max (0902) |
| LMArena Text: longer queries | 1468 | 1488 | Qwen3.8 Max (0902) |
| LMArena Text: writing and language | 1439 | 1477 | Qwen3.8 Max (0902) |
| LMArena Text: entertainment and media | 1428 | 1464 | Qwen3.8 Max (0902) |
| LMArena Vision: English | 1287 | 1311 | Qwen3.8 Max (0902) |
| LMArena Vision: Chinese | 1346 | 1358 | Qwen3.8 Max (0902) |
| LMArena Vision: reading text | 1291 | 1322 | Qwen3.8 Max (0902) |
| LMArena Vision: humor | 1280 | 1304 | Qwen3.8 Max (0902) |
| LMArena Vision: creative writing | 1273 | 1312 | Qwen3.8 Max (0902) |
| LMArena Document | 1451 | Not measured | Only Kimi K2.6 measured |
| Neither model has a result on the other 11 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Kimi K2.6 | Qwen3.8 Max (0902) | 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
Kimi K2.6 costs less
Repository review
50K input + 3K output tokens
Kimi K2.6 costs less
Agent loop
200K input + 10K output tokens
Kimi K2.6 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 | MoonshotAI Kimi K2.6 | Qwen3.8 Max (0902) |
|---|---|---|
| Maker | Moonshot AI | Qwen |
| Weights | Open weights | No public weights listed |
| Released | April 20, 2026 | September 3, 2026 |
| Context window | 262K | 1M |
| Input $/Mtok | $0.65 | $2.00 |
| Output $/Mtok | $3.41 | $6.00 |
| Coding index | 44.2 | 63.5 |
| Independent measurements behind it | 1 | 2 |
| Cost per solved task | $0.35 | $1.32 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Kimi K2.6 better than Qwen3.8 Max (0902) for coding?
Pick Qwen3.8 Max (0902) for coding: it scores 63.5 on the Vibe Coding Index against 44.2 for Kimi K2.6. Pick Kimi K2.6 when output tokens cost 1.8x less, or when a solved task costs $0.35 against $1.32.
Which is cheaper, Kimi K2.6 or Qwen3.8 Max (0902)?
Kimi K2.6 costs $0.65 per million input tokens and $3.41 per million output tokens; Qwen3.8 Max (0902) costs $2.00 and $6.00. Per solved SWE-bench Verified task, Kimi K2.6 costs $0.35 and Qwen3.8 Max (0902) $1.32.
Which has the bigger context window, Kimi K2.6 or Qwen3.8 Max (0902)?
Qwen3.8 Max (0902), with 1M tokens against 262K.
How many benchmarks is the Kimi K2.6 vs Qwen3.8 Max (0902) comparison based on?
95 benchmarks have a result for both models as of October 21, 2026. Kimi K2.6's coding index rests on 1 independent benchmark and Qwen3.8 Max (0902)'s on 2. Where only one model has a result, the page says so instead of naming a winner.