# DeepSeek-R1 vs Kimi K2.7 Code

> DeepSeek-R1 and Kimi K2.7 Code score almost the same on the Noometry Index (42.0 vs 43.0), so choose on price, context window or the category you care about most.

- Canonical page: https://noometry.com/compare/deepseek-r1-vs-kimi-k2-7-code
- Last updated: 2026-10-10
- Shared benchmarks: 8

## Summary

- They share 8 benchmarks with published results for both. DeepSeek-R1 scores higher in 2 categories and Kimi K2.7 Code in 3 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Kimi K2.7 Code leads 38.2 to 18.7.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 66.4% for DeepSeek-R1 and 95.6% for Kimi K2.7 Code.
- DeepSeek-R1 is cheaper at $0.50 / $2.15 per million input/output tokens, against $0.95 / $4 for Kimi K2.7 Code.
- Kimi K2.7 Code accepts more context: 262K tokens versus 164K.
- Kimi K2.7 Code has downloadable open weights; the other is API-only.

## Snapshot

| | DeepSeek-R1 | Kimi K2.7 Code |
|---|---|---|
| Provider | DeepSeek | Moonshot AI |
| Noometry Index | 42.0 | 43.0 |
| Rank | 110 | 90 |
| Context | 164K | 262K |
| Input $/M | $0.50 | $0.95 |
| Output $/M | $2.15 | $4 |
| Weights | Proprietary | Open |

## Coding

- DeepSeek-R1: 45.9 (#63)
- Kimi K2.7 Code: 42.4 (#92)

| Benchmark | DeepSeek-R1 | Kimi K2.7 Code |
|---|---|---|
| SciCode | 35.7% | 47.5% |
| WeirdML | 41.6% | 54.1% |
| ALE-Bench | 804.12 | 886.23 |
| DeepSWE | — | 30.5% |
| FrontierCode | — | 30.1% |
| Aider Polyglot | 71.4% | — |
| LMArena WebDev | — | 1473 |
| LiveBench Coding | 66.7% | — |
| LMArena Coding | 1427 | — |
| AlgoTune | 1.7 | — |

## Agentic & Tool Use

- DeepSeek-R1: 30.4 (#68)
- Kimi K2.7 Code: 23.9 (#115)

| Benchmark | DeepSeek-R1 | Kimi K2.7 Code |
|---|---|---|
| APEX-Agents | — | 37.6% |
| DeepResearch Bench | 35.1% | — |
| BALROG | 34.9% | — |
| GBAEval | — | 0.9% |
| METR Time Horizons | 53.8% | — |
| Vending-Bench 2 | — | 5,083 |

## Reasoning

- DeepSeek-R1: 18.7 (#253)
- Kimi K2.7 Code: 38.2 (#56)

| Benchmark | DeepSeek-R1 | Kimi K2.7 Code |
|---|---|---|
| SimpleBench | 40.8% | 57.9% |
| CritPt | 1.1% | 10% |
| Epoch Capabilities Index | 141.3 | 150.01 |
| ARC-AGI-2 | 1.3% | — |
| Kagi LLM Benchmark | 69.4% | — |
| ARC-AGI-1 | 21.2% | — |
| Chess Puzzles | — | 21% |
| LiveBench Reasoning | 83.2% | — |
| LMArena Hard Prompts | 1415 | — |
| LiveBench Data Analysis | 69.8% | — |
| Surface Evolver Bench | — | 48.8% |
| ForecastBench | 60 | — |
| LiveBench | 71.6% | — |

## Math

- DeepSeek-R1: 42.0 (#70)
- Kimi K2.7 Code: 52.6 (#39)

| Benchmark | DeepSeek-R1 | Kimi K2.7 Code |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 66.4% | 95.6% |
| FrontierMath (Tiers 1-3) | — | 54% |
| FrontierMath Tier 4 | — | 12.2% |
| Omni-MATH | 42.4% | — |
| LiveBench Math | 80.7% | — |
| LMArena Math | 1401 | — |
| MATH Level 5 | 96.6% | — |

## Knowledge

- DeepSeek-R1: 44.3 (#81)
- Kimi K2.7 Code: 53.6 (#51)

| Benchmark | DeepSeek-R1 | Kimi K2.7 Code |
|---|---|---|
| GPQA Diamond | 76.3% | 87.9% |
| SimpleQA Verified | — | 36.5% |
| MMLU-Pro | 79.3% | — |
| Confabulations | 12.7% | — |
| Vectara Hallucination Rate | 11.3% | — |
| GPQA (HELM) | 66.6% | — |
| LMArena Expert | 1394 | — |

## Multilingual

- DeepSeek-R1: 52.6 (#80)
- Kimi K2.7 Code: —

| Benchmark | DeepSeek-R1 | Kimi K2.7 Code |
|---|---|---|
| LMArena Non-English | 1412 | — |
| LMArena Chinese | 1443 | — |
| LMArena French | 1415 | — |
| LMArena German | 1405 | — |
| LMArena Japanese | 1394 | — |
| LMArena Korean | 1359 | — |
| LMArena Russian | 1423 | — |
| LMArena Spanish | 1410 | — |

## Instruction Following

- DeepSeek-R1: 72.2 (#138)
- Kimi K2.7 Code: —

| Benchmark | DeepSeek-R1 | Kimi K2.7 Code |
|---|---|---|
| LiveBench Instruction Following | 80.5% | — |
| IFEval | 78.4% | — |
| LMArena Instruction Following | 1382 | — |

## Long Context

- DeepSeek-R1: 45.4 (#36)
- Kimi K2.7 Code: —

| Benchmark | DeepSeek-R1 | Kimi K2.7 Code |
|---|---|---|
| Fiction.LiveBench | 75% | — |
| LMArena Longer Query | 1391 | — |

## Writing & Preference

- DeepSeek-R1: 61.5 (#84)
- Kimi K2.7 Code: —

| Benchmark | DeepSeek-R1 | Kimi K2.7 Code |
|---|---|---|
| LMArena Text | 1428 | — |
| LMArena Creative Writing | 1406 | — |
| Short-Story Creative Writing | 83% | — |
| EQ-Bench Creative Writing | 1500 | — |
| WildBench | 82.8% | — |
| LMArena Multi-Turn | 1404 | — |
| LiveBench Language | 48.5% | — |

## FAQ

### Is DeepSeek-R1 better than Kimi K2.7 Code?

DeepSeek-R1 and Kimi K2.7 Code score almost the same on the Noometry Index (42.0 vs 43.0), so choose on price, context window or the category you care about most.

### Which is cheaper, DeepSeek-R1 or Kimi K2.7 Code?

DeepSeek-R1 is cheaper. It lists at $0.50 per million input tokens and $2.15 per million output tokens; Kimi K2.7 Code lists at $0.95 and $4.

### Is DeepSeek-R1 or Kimi K2.7 Code better for coding?

DeepSeek-R1 scores higher on coding benchmarks: 45.9 versus 42.4 in the Noometry coding category.

### Which has the bigger context window?

Kimi K2.7 Code does, with 262K tokens against 164K.

### How many benchmarks do DeepSeek-R1 and Kimi K2.7 Code share?

8 benchmarks have published results for both models. DeepSeek-R1 has 52 scored results on Noometry and Kimi K2.7 Code has 19.