Model comparison
DeepSeek-R1 vs Gemini 3.1 Flash Lite
DeepSeek-R1 is the stronger model overall, scoring 42.3 to 40.8 on the Noometry Index. Gemini 3.1 Flash Lite costs 1.6× less per token, which makes it the better buy when DeepSeek-R1's lead doesn't matter for your workload.
Last verified . 28 shared benchmarks.
DeepSeek
GoogleSummary
- They share 28 benchmarks with published results for both. DeepSeek-R1 scores higher in 7 categories and Gemini 3.1 Flash Lite in 2 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in coding, where DeepSeek-R1 leads 46.3 to 37.8.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 66.4% for DeepSeek-R1 and 80% for Gemini 3.1 Flash Lite.
- Gemini 3.1 Flash Lite is cheaper at $0.25 / $1.50 per million input/output tokens, against $0.50 / $2.15 for DeepSeek-R1.
- Gemini 3.1 Flash Lite accepts more context: 1.05M tokens versus 164K.
Side by side
| DeepSeek-R1 | Gemini 3.1 Flash Lite | |
|---|---|---|
| Provider | DeepSeek | |
| Noometry Index | 42.3 | 40.8 |
| Released | 2025-01-20 | 2026-03-03 |
| Weights | Proprietary | Proprietary |
| Context window | 164K | 1.05M |
| Max output | 64K | 66K |
| Input $ / M tokens | $0.50 | $0.25 |
| Output $ / M tokens | $2.15 | $1.50 |
| Results tracked | 52 | 38 |
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Category by category
Coding DeepSeek-R1 leads
DeepSeek-R1: 46.3 (#68), Gemini 3.1 Flash Lite: 37.8 (#188)
| Benchmark | DeepSeek-R1 | Gemini 3.1 Flash Lite |
|---|---|---|
| SciCode | 35.7% | 41.9% |
| WeirdML | 41.6% | 52.2% |
| LMArena Coding | 1427 | 1400 |
| ALE-Bench | 804.12 | 797.73 |
| Aider Polyglot | 71.4% | — |
| LMArena WebDev | — | 1256 |
| LiveBench Coding | 66.7% | — |
| AlgoTune | 1.7 | — |
Agentic & Tool Use Too close to call
DeepSeek-R1: 30.7 (#75), Gemini 3.1 Flash Lite: 30.2 (#79)
| Benchmark | DeepSeek-R1 | Gemini 3.1 Flash Lite |
|---|---|---|
| DeepResearch Bench | 35.1% | 37.3% |
| BALROG | 34.9% | — |
| METR Time Horizons | 53.8% | — |
Reasoning Gemini 3.1 Flash Lite leads
DeepSeek-R1: 18.6 (#278), Gemini 3.1 Flash Lite: 22.9 (#186)
| Benchmark | DeepSeek-R1 | Gemini 3.1 Flash Lite |
|---|---|---|
| Kagi LLM Benchmark | 69.4% | 67.2% |
| CritPt | 1.1% | 1.1% |
| LMArena Hard Prompts | 1416 | 1407 |
| Epoch Capabilities Index | 141.29 | 144.47 |
| ForecastBench | 60 | 54.4 |
| ARC-AGI-2 | 1.3% | — |
| SimpleBench | 40.8% | — |
| NYT Connections (extended) | — | 8.2% |
| ARC-AGI-1 | 21.2% | — |
| Chess Puzzles | — | 25% |
| EnigmaEval | — | 3% |
| Thematic Generalization | — | 63.3% |
| LiveBench Reasoning | 83.2% | — |
| DTBench | — | 76.8% |
| LiveBench Data Analysis | 69.8% | — |
| LMCA | — | 35% |
| LiveBench | 71.6% | — |
Math DeepSeek-R1 leads
DeepSeek-R1: 43.8 (#79), Gemini 3.1 Flash Lite: 40.7 (#90)
| Benchmark | DeepSeek-R1 | Gemini 3.1 Flash Lite |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 66.4% | 80% |
| LMArena Math | 1400 | 1428 |
| FrontierMath (Tiers 1-3) | — | 27.7% |
| Omni-MATH | 42.4% | — |
| LiveBench Math | 80.7% | — |
| MATH Level 5 | 96.6% | — |
Knowledge DeepSeek-R1 leads
DeepSeek-R1: 44.5 (#87), Gemini 3.1 Flash Lite: 41.9 (#104)
| Benchmark | DeepSeek-R1 | Gemini 3.1 Flash Lite |
|---|---|---|
| GPQA Diamond | 76.3% | 81.8% |
| Vectara Hallucination Rate | 11.3% | 8.2% |
| LMArena Expert | 1394 | 1398 |
| Humanity's Last Exam | — | 8.6% |
| MMLU-Pro | 79.3% | — |
| Confabulations | 12.7% | — |
| GPQA (HELM) | 66.6% | — |
Multimodal Not comparable
DeepSeek-R1: —, Gemini 3.1 Flash Lite: 39.4 (#60)
| Benchmark | DeepSeek-R1 | Gemini 3.1 Flash Lite |
|---|---|---|
| LMArena Vision | — | 1240 |
Multilingual Too close to call
DeepSeek-R1: 52.4 (#85), Gemini 3.1 Flash Lite: 52.3 (#86)
| Benchmark | DeepSeek-R1 | Gemini 3.1 Flash Lite |
|---|---|---|
| LMArena Non-English | 1412 | 1411 |
| LMArena Chinese | 1442 | 1461 |
| LMArena French | 1417 | 1424 |
| LMArena German | 1404 | 1429 |
| LMArena Japanese | 1391 | 1413 |
| LMArena Korean | 1360 | 1392 |
| LMArena Russian | 1423 | 1420 |
| LMArena Spanish | 1411 | 1421 |
Instruction Following Too close to call
DeepSeek-R1: 72.0 (#143), Gemini 3.1 Flash Lite: 72.7 (#131)
| Benchmark | DeepSeek-R1 | Gemini 3.1 Flash Lite |
|---|---|---|
| LMArena Instruction Following | 1382 | 1377 |
| LiveBench Instruction Following | 80.5% | — |
| IFEval | 78.4% | — |
Long Context DeepSeek-R1 leads
DeepSeek-R1: 45.4 (#36), Gemini 3.1 Flash Lite: 42.5 (#122)
| Benchmark | DeepSeek-R1 | Gemini 3.1 Flash Lite |
|---|---|---|
| LMArena Longer Query | 1391 | 1394 |
| Fiction.LiveBench | 75% | — |
Writing & Preference Too close to call
DeepSeek-R1: 61.4 (#88), Gemini 3.1 Flash Lite: 60.9 (#94)
| Benchmark | DeepSeek-R1 | Gemini 3.1 Flash Lite |
|---|---|---|
| LMArena Text | 1428 | 1416 |
| LMArena Creative Writing | 1405 | 1401 |
| LMArena Multi-Turn | 1405 | 1417 |
| Short-Story Creative Writing | 83% | — |
| EQ-Bench Creative Writing | 1500 | — |
| WildBench | 82.8% | — |
| LiveBench Language | 48.5% | — |
Frequently asked questions
Is DeepSeek-R1 better than Gemini 3.1 Flash Lite?
DeepSeek-R1 is the stronger model overall, scoring 42.3 to 40.8 on the Noometry Index. Gemini 3.1 Flash Lite costs 1.6× less per token, which makes it the better buy when DeepSeek-R1's lead doesn't matter for your workload.
Which is cheaper, DeepSeek-R1 or Gemini 3.1 Flash Lite?
Gemini 3.1 Flash Lite is cheaper. It lists at $0.25 per million input tokens and $1.50 per million output tokens; DeepSeek-R1 lists at $0.50 and $2.15.
Is DeepSeek-R1 or Gemini 3.1 Flash Lite better for coding?
DeepSeek-R1 scores higher on coding benchmarks: 46.3 versus 37.8 in the Noometry coding category.
Which has the bigger context window?
Gemini 3.1 Flash Lite does, with 1.05M tokens against 164K.
How many benchmarks do DeepSeek-R1 and Gemini 3.1 Flash Lite share?
28 benchmarks have published results for both models. DeepSeek-R1 has 52 scored results on Noometry and Gemini 3.1 Flash Lite has 38.