Best AI in German: 314 models ranked by blind votes
Prompts written in German. Ranked by LMArena’s blind votes, every score with its margin of error and, beside it, the list price of each model on our price list.
Scores from LMArena, published 20 October 2026, under CC BY 4.0. Prices from each maker’s pricing page, read on 15 October 2026.
- Models ranked
- 314
- Level with the leader
- 35
- On our price list
- 1
- Published
- 20 October 2026
Level: the votes cannot separate it from the leader.
On LMArena’s German board, Gemini 3 Pro from Google leads with 1521 points from 811 blind votes, published 20 October 2026. 34 other models are statistically level with it. None of the models it ranks has an API price on our list yet.
Best AI in German
Prompts written in German.
| # | Model | Score | Votes | Per 1M tokens, in / out |
|---|---|---|---|---|
| 1 | Gemini 3 ProGoogle | 95% interval 1499 to 1543 | 811 | · |
| 1 | Muse Spark 1.3 (Max)Meta | 95% interval 1479 to 1550 | 275 | · |
| 1 | Muse SparkMeta | 95% interval 1472 to 1551 | 237 | · |
| 1 | Claude Opus 5 (Max)Anthropic | 95% interval 1484 to 1536 | 570 | · |
| 1 | Claude Opus 4.6 (High)Anthropic | 95% interval 1489 to 1523 | 1,304 | · |
| 1 | GPT-5.6 Sol (xHigh)OpenAI | 95% interval 1482 to 1529 | 658 | · |
| 1 | Kimi K3 (Max)Moonshot AI · open weights | 95% interval 1482 to 1529 | 671 | · |
| 1 | Claude Opus 4.7 (High)Anthropic | 95% interval 1486 to 1523 | 1,021 | · |
| 1 | Gemini 3.7 Flash (High)Google | 95% interval 1474 to 1531 | 440 | · |
| 1 | Claude Fable 5 (High)Anthropic | 95% interval 1478 to 1525 | 667 | · |
| 1 | GLM-5.3 (Max)Z.ai · open weights | 95% interval 1471 to 1530 | 382 | · |
| 1 | Gemini 3 FlashGoogle | 95% interval 1474 to 1524 | 572 | · |
| 1 | Claude Opus 4.7Anthropic | 95% interval 1478 to 1516 | 1,040 | · |
| 1 | DeepSeek-V4.1-Flash-20260910 (Max)DeepSeek · open weights | 95% interval 1458 to 1531 | 239 | · |
| 1 | Gemini 3.8 Flash (High)Google | 95% interval 1470 to 1518 | 640 | · |
| 1 | Gemini 3.1 Pro PreviewGoogle | 95% interval 1479 to 1508 | 2,146 | · |
| 1 | Claude Opus 4.6Anthropic | 95% interval 1476 to 1510 | 1,354 | · |
| 1 | Claude Opus 4.8 (High)Anthropic | 95% interval 1473 to 1511 | 1,077 | · |
| 1 | GPT-6 Astra (Max)OpenAI | 95% interval 1450 to 1534 | 198 | · |
| 1 | Gemini 3.5 Flash (Medium)Google | 95% interval 1469 to 1514 | 757 | · |
| 1 | Claude Opus 5 (High)Anthropic | 95% interval 1471 to 1510 | 1,074 | · |
| 1 | GPT-5.5OpenAI | 95% interval 1471 to 1509 | 1,113 | · |
| 1 | Claude Fable 5.1 (Max)Anthropic | 95% interval 1456 to 1524 | 296 | · |
| 1 | Grok 4.20 Beta 1xAI | 95% interval 1462 to 1517 | 444 | · |
| 1 | Gemini 3.5 Flash (High)Google | 95% interval 1463 to 1508 | 776 | · |
| 1 | Gemini 3.6 Flash (High)Google | 95% interval 1459 to 1508 | 592 | · |
| 1 | Qwen3.8-MaxAlibaba | 95% interval 1455 to 1508 | 459 | · |
| 1 | Muse Spark 1.1Meta | 95% interval 1459 to 1503 | 811 | · |
| 1 | GPT-5.2 Chat (20260210)OpenAI | 95% interval 1455 to 1506 | 563 | · |
| 1 | GPT-5.6 Terra (xHigh)OpenAI | 95% interval 1456 to 1501 | 713 | · |
| 1 | GPT-4.5 Preview (2025-02-27)OpenAI | 95% interval 1445 to 1510 | 319 | · |
| 1 | Claude Opus 4.5 (High, 32K, 20251101)Anthropic | 95% interval 1454 to 1500 | 666 | · |
| 1 | GPT-5.6 Luna (xHigh)OpenAI | 95% interval 1454 to 1500 | 706 | · |
| 1 | Qwen3.5-Max-PreviewAlibaba | 95% interval 1442 to 1502 | 383 | · |
| 1 | Hy3Tencent · open weights | 95% interval 1431 to 1504 | 227 | · |
| 2 | Claude Opus 4.5 (20251101)Anthropic | 95% interval 1460 to 1497 | 1,134 | · |
| 2 | GLM-5.2 (Max)Z.ai · open weights | 95% interval 1456 to 1497 | 913 | · |
| 2 | GPT-5.4 (High)OpenAI | 95% interval 1457 to 1495 | 1,094 | · |
| 2 | Gemini 3 Flash (Minimal)Google | 95% interval 1459 to 1492 | 1,473 | · |
| 2 | Grok 4.20 Multi-Agent Beta (0309)xAI | 95% interval 1455 to 1494 | 1,058 | · |
| 2 | ERNIE 5.1Baidu | 95% interval 1450 to 1494 | 750 | · |
| 2 | GPT-5.5 (High)OpenAI | 95% interval 1453 to 1491 | 1,055 | · |
| 2 | Gemini 3.5 Flash-LiteGoogle | 95% interval 1448 to 1494 | 654 | · |
| 2 | DeepSeek-V4-ProDeepSeek · open weights | 95% interval 1450 to 1489 | 997 | · |
| 2 | GLM-5.3-FlashZ.ai · open weights | 95% interval 1444 to 1493 | 614 | · |
| 2 | GPT-5.5 InstantOpenAI | 95% interval 1438 to 1496 | 430 | · |
| 2 | Claude Sonnet 5 (High)Anthropic | 95% interval 1445 to 1489 | 814 | · |
| 3 | Claude Opus 4.8Anthropic | 95% interval 1451 to 1488 | 1,106 | · |
| 3 | GLM-5.1Z.ai · open weights | 95% interval 1448 to 1487 | 1,038 | · |
| 5 | Gemini 2.5 ProGoogle | 95% interval 1458 to 1484 | 2,652 | · |
| Further down the board: the other models on our price list | ||||
| 158 | gpt-oss-120bOpenAI · open weights | 95% interval 1305 to 1351 | 673 | · |
Where the ranking comes from
The scores, intervals and ranks are LMArena’s. The prices are ours.
- Blind votes
- LMArena puts the same prompt to two anonymous models and asks the person who wrote it to pick the better answer. The votes place every model on one scale, where a gap of 100 points means the higher model wins about 64 times in 100.
- Style control
- The scores discount answer length and formatting, so a model does not climb by writing more or by adding headings and lists.
- Shared ranks
- A model’s rank is one plus the number of models whose whole 95% interval sits above its own. Models the votes cannot separate share a rank, which is why several can be first.
- Settings
- A model can appear more than once, at different reasoning settings: the line under its name gives the entry as LMArena lists it. Entries with web search switched on are a different product and carry no price.
- Prices
- The list price per million tokens from the maker’s own pricing page, read on 15 October 2026. It is a price per token, not per answer: a higher reasoning setting spends more tokens on the same question.
Source: LMArena, leaderboard dataset (revision 9041150e, published 20 October 2026), licensed under CC BY 4.0. We change no score: we add the prices and the links to our model pages.
What each of them costs to run
The price list works three everyday jobs on every model, and our own benchmark runs them on real business tasks to see which pass.
Scores published 20 October 2026