Model comparison
DeepSeek-R1 vs Gemini 2.0 Flash-Lite
DeepSeek-R1 is the stronger model overall, scoring 42.3 to 37.8 on the Noometry Index.
Last verified . 30 shared benchmarks.
DeepSeek
GoogleSummary
- They share 30 benchmarks with published results for both. DeepSeek-R1 scores higher in 7 categories and Gemini 2.0 Flash-Lite in 1 category; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where DeepSeek-R1 leads 43.8 to 34.1.
- The biggest single-benchmark swing is LiveBench Reasoning: 83.2% for DeepSeek-R1 and 50.1% for Gemini 2.0 Flash-Lite.
Side by side
| DeepSeek-R1 | Gemini 2.0 Flash-Lite | |
|---|---|---|
| Provider | DeepSeek | |
| Noometry Index | 42.3 | 37.8 |
| Released | 2025-01-20 | 2025-02-05 |
| Weights | Proprietary | Proprietary |
| Context window | 164K | — |
| Max output | 64K | — |
| Input $ / M tokens | $0.50 | — |
| Output $ / M tokens | $2.15 | — |
| Results tracked | 52 | 32 |
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Category by category
Coding DeepSeek-R1 leads
DeepSeek-R1: 46.3 (#68), Gemini 2.0 Flash-Lite: 37.9 (#185)
| Benchmark | DeepSeek-R1 | Gemini 2.0 Flash-Lite |
|---|---|---|
| LiveBench Coding | 66.7% | 47.1% |
| LMArena Coding | 1427 | 1322 |
| Aider Polyglot | 71.4% | — |
| SciCode | 35.7% | — |
| WeirdML | 41.6% | — |
| ALE-Bench | 804.12 | — |
| AlgoTune | 1.7 | — |
Agentic & Tool Use Not comparable
DeepSeek-R1: 30.7 (#75), Gemini 2.0 Flash-Lite: —
| Benchmark | DeepSeek-R1 | Gemini 2.0 Flash-Lite |
|---|---|---|
| DeepResearch Bench | 35.1% | — |
| BALROG | 34.9% | — |
| METR Time Horizons | 53.8% | — |
Reasoning Gemini 2.0 Flash-Lite leads
DeepSeek-R1: 18.6 (#278), Gemini 2.0 Flash-Lite: 22.0 (#210)
| Benchmark | DeepSeek-R1 | Gemini 2.0 Flash-Lite |
|---|---|---|
| LiveBench Reasoning | 83.2% | 50.1% |
| LMArena Hard Prompts | 1416 | 1324 |
| LiveBench Data Analysis | 69.8% | 65.5% |
| ForecastBench | 60 | 57.1 |
| LiveBench | 71.6% | 54.3% |
| ARC-AGI-2 | 1.3% | — |
| SimpleBench | 40.8% | — |
| Kagi LLM Benchmark | 69.4% | — |
| ARC-AGI-1 | 21.2% | — |
| CritPt | 1.1% | — |
| DTBench | — | 52.5% |
| Epoch Capabilities Index | 141.29 | — |
Math DeepSeek-R1 leads
DeepSeek-R1: 43.8 (#79), Gemini 2.0 Flash-Lite: 34.1 (#196)
| Benchmark | DeepSeek-R1 | Gemini 2.0 Flash-Lite |
|---|---|---|
| Omni-MATH | 42.4% | 37.4% |
| LiveBench Math | 80.7% | 58.1% |
| LMArena Math | 1400 | 1309 |
| OTIS Mock AIME 2024-2025 | 66.4% | — |
| MATH Level 5 | 96.6% | — |
Knowledge DeepSeek-R1 leads
DeepSeek-R1: 44.5 (#87), Gemini 2.0 Flash-Lite: 35.0 (#189)
| Benchmark | DeepSeek-R1 | Gemini 2.0 Flash-Lite |
|---|---|---|
| MMLU-Pro | 79.3% | 72% |
| GPQA (HELM) | 66.6% | 50% |
| LMArena Expert | 1394 | 1305 |
| GPQA Diamond | 76.3% | — |
| Confabulations | 12.7% | — |
| Vectara Hallucination Rate | 11.3% | — |
Multimodal Not comparable
DeepSeek-R1: —, Gemini 2.0 Flash-Lite: 31.2 (#109)
| Benchmark | DeepSeek-R1 | Gemini 2.0 Flash-Lite |
|---|---|---|
| LMArena Vision | — | 1100 |
Multilingual DeepSeek-R1 leads
DeepSeek-R1: 52.4 (#85), Gemini 2.0 Flash-Lite: 46.0 (#161)
| Benchmark | DeepSeek-R1 | Gemini 2.0 Flash-Lite |
|---|---|---|
| LMArena Non-English | 1412 | 1323 |
| LMArena Chinese | 1442 | 1339 |
| LMArena French | 1417 | 1347 |
| LMArena German | 1404 | 1306 |
| LMArena Japanese | 1391 | 1301 |
| LMArena Korean | 1360 | 1325 |
| LMArena Russian | 1423 | 1328 |
| LMArena Spanish | 1411 | 1313 |
Instruction Following DeepSeek-R1 leads
DeepSeek-R1: 72.0 (#143), Gemini 2.0 Flash-Lite: 70.4 (#163)
| Benchmark | DeepSeek-R1 | Gemini 2.0 Flash-Lite |
|---|---|---|
| LiveBench Instruction Following | 80.5% | 78.3% |
| IFEval | 78.4% | 82.4% |
| LMArena Instruction Following | 1382 | 1305 |
Long Context DeepSeek-R1 leads
DeepSeek-R1: 45.4 (#36), Gemini 2.0 Flash-Lite: 40.1 (#160)
| Benchmark | DeepSeek-R1 | Gemini 2.0 Flash-Lite |
|---|---|---|
| LMArena Longer Query | 1391 | 1320 |
| Fiction.LiveBench | 75% | — |
Writing & Preference DeepSeek-R1 leads
DeepSeek-R1: 61.4 (#88), Gemini 2.0 Flash-Lite: 51.7 (#177)
| Benchmark | DeepSeek-R1 | Gemini 2.0 Flash-Lite |
|---|---|---|
| LMArena Text | 1428 | 1330 |
| LMArena Creative Writing | 1405 | 1319 |
| WildBench | 82.8% | 79% |
| LMArena Multi-Turn | 1405 | 1307 |
| LiveBench Language | 48.5% | 34.3% |
| Short-Story Creative Writing | 83% | — |
| EQ-Bench Creative Writing | 1500 | — |
Frequently asked questions
Is DeepSeek-R1 better than Gemini 2.0 Flash-Lite?
DeepSeek-R1 is the stronger model overall, scoring 42.3 to 37.8 on the Noometry Index.
Is DeepSeek-R1 or Gemini 2.0 Flash-Lite better for coding?
DeepSeek-R1 scores higher on coding benchmarks: 46.3 versus 37.9 in the Noometry coding category.
How many benchmarks do DeepSeek-R1 and Gemini 2.0 Flash-Lite share?
30 benchmarks have published results for both models. DeepSeek-R1 has 52 scored results on Noometry and Gemini 2.0 Flash-Lite has 32.