Model comparison
DeepSeek-V3.1 vs Gemini 1.5 Flash (May 2024)
DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 33.2 on the Noometry Index.
Last verified . 21 shared benchmarks.
DeepSeek
GoogleSummary
- They share 21 benchmarks with published results for both. DeepSeek-V3.1 scores higher in 7 categories and Gemini 1.5 Flash (May 2024) in 1 category; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where DeepSeek-V3.1 leads 43.7 to 26.2.
- The biggest single-benchmark swing is DTBench: 82.7% for DeepSeek-V3.1 and 53.8% for Gemini 1.5 Flash (May 2024).
- DeepSeek-V3.1 has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-V3.1 | Gemini 1.5 Flash (May 2024) | |
|---|---|---|
| Provider | DeepSeek | |
| Noometry Index | 42.8 | 33.2 |
| Released | 2025-08-21 | 2024-05-14 |
| Weights | Open | Proprietary |
| Context window | 164K | — |
| Max output | 8K | — |
| Input $ / M tokens | $0.25 | — |
| Output $ / M tokens | $0.95 | — |
| Results tracked | 27 | 42 |
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Category by category
Coding DeepSeek-V3.1 leads
DeepSeek-V3.1: 40.3 (#144), Gemini 1.5 Flash (May 2024): 34.4 (#236)
| Benchmark | DeepSeek-V3.1 | Gemini 1.5 Flash (May 2024) |
|---|---|---|
| WeirdML | 38.4% | 24.9% |
| LMArena Coding | 1417 | 1261 |
| BigCodeBench Instruct | — | 43.5% |
| BigCodeBench Complete | — | 55.1% |
| HumanEval+ | — | 75.6% |
| MBPP+ | — | 67.5% |
Agentic & Tool Use Not comparable
DeepSeek-V3.1: —, Gemini 1.5 Flash (May 2024): 26.6 (#102)
| Benchmark | DeepSeek-V3.1 | Gemini 1.5 Flash (May 2024) |
|---|---|---|
| BALROG | — | 14.6% |
Reasoning DeepSeek-V3.1 leads
DeepSeek-V3.1: 27.9 (#110), Gemini 1.5 Flash (May 2024): 21.7 (#215)
| Benchmark | DeepSeek-V3.1 | Gemini 1.5 Flash (May 2024) |
|---|---|---|
| LMArena Hard Prompts | 1417 | 1257 |
| DTBench | 82.7% | 53.8% |
| Epoch Capabilities Index | 139.92 | 129.36 |
| ForecastBench | 58 | 53.9 |
| SimpleBench | 40% | — |
| Kagi LLM Benchmark | 53.2% | — |
| LMCA | 24.3% | — |
| PIQA | — | 87.5% |
Math DeepSeek-V3.1 leads
DeepSeek-V3.1: 38.9 (#122), Gemini 1.5 Flash (May 2024): 22.1 (#281)
| Benchmark | DeepSeek-V3.1 | Gemini 1.5 Flash (May 2024) |
|---|---|---|
| LMArena Math | 1420 | 1269 |
| OTIS Mock AIME 2024-2025 | — | 16.3% |
| Omni-MATH | — | 30.4% |
| MATH Level 5 | — | 61.9% |
| FrontierMath (Feb 2025 set) | — | 0% |
| GSM8K | — | 82.4% |
Knowledge DeepSeek-V3.1 leads
DeepSeek-V3.1: 43.7 (#90), Gemini 1.5 Flash (May 2024): 26.2 (#260)
| Benchmark | DeepSeek-V3.1 | Gemini 1.5 Flash (May 2024) |
|---|---|---|
| LMArena Expert | 1405 | 1233 |
| GPQA Diamond | — | 47.3% |
| MMLU-Pro | — | 67.8% |
| Vectara Hallucination Rate | 5.5% | — |
| GPQA (HELM) | — | 43.7% |
| BoolQ | — | 85.8% |
| MMLU | — | 77.9% |
Multimodal Not comparable
DeepSeek-V3.1: —, Gemini 1.5 Flash (May 2024): 36.0 (#81)
| Benchmark | DeepSeek-V3.1 | Gemini 1.5 Flash (May 2024) |
|---|---|---|
| LMArena Vision | — | 1141 |
| Video-MME | — | 70.3% |
| GeoBench | — | 76% |
Multilingual DeepSeek-V3.1 leads
DeepSeek-V3.1: 51.6 (#106), Gemini 1.5 Flash (May 2024): 42.9 (#189)
| Benchmark | DeepSeek-V3.1 | Gemini 1.5 Flash (May 2024) |
|---|---|---|
| LMArena Non-English | 1400 | 1278 |
| LMArena Chinese | 1469 | 1295 |
| LMArena French | 1447 | 1258 |
| LMArena German | 1411 | 1262 |
| LMArena Japanese | 1378 | 1252 |
| LMArena Korean | 1337 | 1221 |
| LMArena Russian | 1405 | 1288 |
| LMArena Spanish | 1431 | 1243 |
Instruction Following DeepSeek-V3.1 leads
DeepSeek-V3.1: 73.9 (#110), Gemini 1.5 Flash (May 2024): 66.8 (#205)
| Benchmark | DeepSeek-V3.1 | Gemini 1.5 Flash (May 2024) |
|---|---|---|
| LMArena Instruction Following | 1400 | 1258 |
| IFEval | — | 83.1% |
Long Context Gemini 1.5 Flash (May 2024) leads
DeepSeek-V3.1: 36.3 (#232), Gemini 1.5 Flash (May 2024): 39.0 (#187)
| Benchmark | DeepSeek-V3.1 | Gemini 1.5 Flash (May 2024) |
|---|---|---|
| LMArena Longer Query | 1422 | 1284 |
| Fiction.LiveBench | 52.8% | — |
Writing & Preference DeepSeek-V3.1 leads
DeepSeek-V3.1: 60.3 (#98), Gemini 1.5 Flash (May 2024): 48.7 (#196)
| Benchmark | DeepSeek-V3.1 | Gemini 1.5 Flash (May 2024) |
|---|---|---|
| LMArena Text | 1420 | 1287 |
| LMArena Creative Writing | 1401 | 1285 |
| LMArena Multi-Turn | 1408 | 1253 |
| EQ-Bench Creative Writing | 1436 | — |
| WildBench | — | 79.2% |
Frequently asked questions
Is DeepSeek-V3.1 better than Gemini 1.5 Flash (May 2024)?
DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 33.2 on the Noometry Index.
Is DeepSeek-V3.1 or Gemini 1.5 Flash (May 2024) better for coding?
DeepSeek-V3.1 scores higher on coding benchmarks: 40.3 versus 34.4 in the Noometry coding category.
How many benchmarks do DeepSeek-V3.1 and Gemini 1.5 Flash (May 2024) share?
21 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and Gemini 1.5 Flash (May 2024) has 42.