# DeepSeek-R1 vs Qwen3.8 Max

> Qwen3.8 Max is the stronger model overall, scoring 56.0 to 42.0 on the Noometry Index. DeepSeek-R1 costs 3.3× less per token, which makes it the better buy when Qwen3.8 Max's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/deepseek-r1-vs-qwen3-8-max
- Last updated: 2026-10-10
- Shared benchmarks: 22

## Summary

- They share 22 benchmarks with published results for both. DeepSeek-R1 scores higher in 0 categories and Qwen3.8 Max in 9 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Qwen3.8 Max leads 53.4 to 18.7.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 66.4% for DeepSeek-R1 and 100% for Qwen3.8 Max.
- DeepSeek-R1 is cheaper at $0.50 / $2.15 per million input/output tokens, against $2 / $6 for Qwen3.8 Max.
- Qwen3.8 Max accepts more context: 1M tokens versus 164K.

## Snapshot

| | DeepSeek-R1 | Qwen3.8 Max |
|---|---|---|
| Provider | DeepSeek | Alibaba (Qwen) |
| Noometry Index | 42.0 | 56.0 |
| Rank | 110 | 21 |
| Context | 164K | 1M |
| Input $/M | $0.50 | $2 |
| Output $/M | $2.15 | $6 |
| Weights | Proprietary | Proprietary |

## Coding

- DeepSeek-R1: 45.9 (#63)
- Qwen3.8 Max: 52.3 (#30)

| Benchmark | DeepSeek-R1 | Qwen3.8 Max |
|---|---|---|
| SciCode | 35.7% | 52.9% |
| LMArena Coding | 1427 | 1499 |
| DeepSWE | — | 57.5% |
| Aider Polyglot | 71.4% | — |
| LMArena WebDev | — | 1671 |
| FrontierSWE | — | 15.8% |
| WeirdML | 41.6% | — |
| LiveBench Coding | 66.7% | — |
| ALE-Bench | 804.12 | — |
| AlgoTune | 1.7 | — |

## Agentic & Tool Use

- DeepSeek-R1: 30.4 (#68)
- Qwen3.8 Max: 42.6 (#17)

| Benchmark | DeepSeek-R1 | Qwen3.8 Max |
|---|---|---|
| τ²-bench Banking | — | 55.1% |
| DeepResearch Bench | 35.1% | — |
| BALROG | 34.9% | — |
| GDP.pdf | — | 23.2% |
| METR Time Horizons | 53.8% | — |

## Reasoning

- DeepSeek-R1: 18.7 (#253)
- Qwen3.8 Max: 53.4 (#26)

| Benchmark | DeepSeek-R1 | Qwen3.8 Max |
|---|---|---|
| CritPt | 1.1% | 20% |
| LMArena Hard Prompts | 1415 | 1493 |
| Epoch Capabilities Index | 141.3 | 156.69 |
| ARC-AGI-2 | 1.3% | — |
| SimpleBench | 40.8% | — |
| Kagi LLM Benchmark | 69.4% | — |
| NYT Connections (extended) | — | 88.3% |
| ARC-AGI-1 | 21.2% | — |
| Chess Puzzles | — | 40% |
| LiveBench Reasoning | 83.2% | — |
| Mystery Game Puzzles | — | 38% |
| DTBench | — | 92% |
| LiveBench Data Analysis | 69.8% | — |
| LMCA | — | 46.2% |
| ForecastBench | 60 | — |
| LiveBench | 71.6% | — |

## Math

- DeepSeek-R1: 42.0 (#70)
- Qwen3.8 Max: 73.1 (#15)

| Benchmark | DeepSeek-R1 | Qwen3.8 Max |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 66.4% | 100% |
| LMArena Math | 1401 | 1498 |
| FrontierMath (Tiers 1-3) | — | 74.7% |
| FrontierMath Tier 4 | — | 46.3% |
| ProofBench | — | 58% |
| Omni-MATH | 42.4% | — |
| LiveBench Math | 80.7% | — |
| MATH Level 5 | 96.6% | — |

## Knowledge

- DeepSeek-R1: 44.3 (#81)
- Qwen3.8 Max: 61.7 (#25)

| Benchmark | DeepSeek-R1 | Qwen3.8 Max |
|---|---|---|
| GPQA Diamond | 76.3% | 92.7% |
| LMArena Expert | 1394 | 1504 |
| SimpleQA Verified | — | 47.3% |
| MMLU-Pro | 79.3% | — |
| Confabulations | 12.7% | — |
| Vectara Hallucination Rate | 11.3% | — |
| GPQA (HELM) | 66.6% | — |

## Multimodal

- DeepSeek-R1: —
- Qwen3.8 Max: 36.9 (#70)

| Benchmark | DeepSeek-R1 | Qwen3.8 Max |
|---|---|---|
| LMArena Vision | — | 1314 |
| Furniture Assembly | — | 20% |

## Multilingual

- DeepSeek-R1: 52.6 (#80)
- Qwen3.8 Max: 56.6 (#18)

| Benchmark | DeepSeek-R1 | Qwen3.8 Max |
|---|---|---|
| LMArena Non-English | 1412 | 1468 |
| LMArena Chinese | 1443 | 1537 |
| LMArena French | 1415 | 1502 |
| LMArena German | 1405 | 1489 |
| LMArena Japanese | 1394 | 1466 |
| LMArena Korean | 1359 | 1467 |
| LMArena Russian | 1423 | 1478 |
| LMArena Spanish | 1410 | 1495 |

## Instruction Following

- DeepSeek-R1: 72.2 (#138)
- Qwen3.8 Max: 77.7 (#19)

| Benchmark | DeepSeek-R1 | Qwen3.8 Max |
|---|---|---|
| LMArena Instruction Following | 1382 | 1478 |
| LiveBench Instruction Following | 80.5% | — |
| IFEval | 78.4% | — |

## Long Context

- DeepSeek-R1: 45.4 (#36)
- Qwen3.8 Max: 45.5 (#30)

| Benchmark | DeepSeek-R1 | Qwen3.8 Max |
|---|---|---|
| LMArena Longer Query | 1391 | 1486 |
| Fiction.LiveBench | 75% | — |

## Writing & Preference

- DeepSeek-R1: 61.5 (#84)
- Qwen3.8 Max: 67.0 (#31)

| Benchmark | DeepSeek-R1 | Qwen3.8 Max |
|---|---|---|
| LMArena Text | 1428 | 1480 |
| LMArena Creative Writing | 1406 | 1478 |
| LMArena Multi-Turn | 1404 | 1487 |
| Short-Story Creative Writing | 83% | — |
| EQ-Bench Creative Writing | 1500 | — |
| WildBench | 82.8% | — |
| LiveBench Language | 48.5% | — |

## FAQ

### Is DeepSeek-R1 better than Qwen3.8 Max?

Qwen3.8 Max is the stronger model overall, scoring 56.0 to 42.0 on the Noometry Index. DeepSeek-R1 costs 3.3× less per token, which makes it the better buy when Qwen3.8 Max's lead doesn't matter for your workload.

### Which is cheaper, DeepSeek-R1 or Qwen3.8 Max?

DeepSeek-R1 is cheaper. It lists at $0.50 per million input tokens and $2.15 per million output tokens; Qwen3.8 Max lists at $2 and $6.

### Is DeepSeek-R1 or Qwen3.8 Max better for coding?

Qwen3.8 Max scores higher on coding benchmarks: 52.3 versus 45.9 in the Noometry coding category.

### Which has the bigger context window?

Qwen3.8 Max does, with 1M tokens against 164K.

### How many benchmarks do DeepSeek-R1 and Qwen3.8 Max share?

22 benchmarks have published results for both models. DeepSeek-R1 has 52 scored results on Noometry and Qwen3.8 Max has 38.