Model comparison
DeepSeek-R1 vs Kimi K2.7 Code
DeepSeek-R1 and Kimi K2.7 Code score almost the same on the Noometry Index (42.3 vs 43.3), so choose on price, context window or the category you care about most.
Last verified . 8 shared benchmarks.
DeepSeekSummary
- 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 39.0 to 18.6.
- 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.
Side by side
| DeepSeek-R1 | Kimi K2.7 Code | |
|---|---|---|
| Provider | DeepSeek | Moonshot AI |
| Noometry Index | 42.3 | 43.3 |
| Released | 2025-01-20 | 2026-06-12 |
| Weights | Proprietary | Open |
| Context window | 164K | 262K |
| Max output | 64K | 262K |
| Input $ / M tokens | $0.50 | $0.95 |
| Output $ / M tokens | $2.15 | $4 |
| Results tracked | 52 | 19 |
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Category by category
Coding DeepSeek-R1 leads
DeepSeek-R1: 46.3 (#68), Kimi K2.7 Code: 42.9 (#95)
| 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 leads
DeepSeek-R1: 30.7 (#75), Kimi K2.7 Code: 24.0 (#122)
| 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 Kimi K2.7 Code leads
DeepSeek-R1: 18.6 (#278), Kimi K2.7 Code: 39.0 (#61)
| Benchmark | DeepSeek-R1 | Kimi K2.7 Code |
|---|---|---|
| SimpleBench | 40.8% | 57.9% |
| CritPt | 1.1% | 10% |
| Epoch Capabilities Index | 141.29 | 149.97 |
| 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 | 1416 | — |
| LiveBench Data Analysis | 69.8% | — |
| Surface Evolver Bench | — | 48.8% |
| ForecastBench | 60 | — |
| LiveBench | 71.6% | — |
Math Kimi K2.7 Code leads
DeepSeek-R1: 43.8 (#79), Kimi K2.7 Code: 52.9 (#48)
| 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 | 1400 | — |
| MATH Level 5 | 96.6% | — |
Knowledge Kimi K2.7 Code leads
DeepSeek-R1: 44.5 (#87), Kimi K2.7 Code: 53.5 (#57)
| 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 Not comparable
DeepSeek-R1: 52.4 (#85), Kimi K2.7 Code: —
| Benchmark | DeepSeek-R1 | Kimi K2.7 Code |
|---|---|---|
| LMArena Non-English | 1412 | — |
| LMArena Chinese | 1442 | — |
| LMArena French | 1417 | — |
| LMArena German | 1404 | — |
| LMArena Japanese | 1391 | — |
| LMArena Korean | 1360 | — |
| LMArena Russian | 1423 | — |
| LMArena Spanish | 1411 | — |
Instruction Following Not comparable
DeepSeek-R1: 72.0 (#143), 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 Not comparable
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 Not comparable
DeepSeek-R1: 61.4 (#88), Kimi K2.7 Code: —
| Benchmark | DeepSeek-R1 | Kimi K2.7 Code |
|---|---|---|
| LMArena Text | 1428 | — |
| LMArena Creative Writing | 1405 | — |
| Short-Story Creative Writing | 83% | — |
| EQ-Bench Creative Writing | 1500 | — |
| WildBench | 82.8% | — |
| LMArena Multi-Turn | 1405 | — |
| LiveBench Language | 48.5% | — |
Frequently asked questions
Is DeepSeek-R1 better than Kimi K2.7 Code?
DeepSeek-R1 and Kimi K2.7 Code score almost the same on the Noometry Index (42.3 vs 43.3), 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: 46.3 versus 42.9 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.