Model comparison
DeepSeek-V2 (MoE-236B, May 2024) vs Gemini 3.7 Flash
Gemini 3.7 Flash has enough public results to be ranked (#11); DeepSeek-V2 (MoE-236B, May 2024) does not yet, so treat this comparison as directional.
Last verified . 1 shared benchmarks.
DeepSeek
GoogleSummary
- They share 1 benchmark with published results for both. DeepSeek-V2 (MoE-236B, May 2024) scores higher in 0 categories and Gemini 3.7 Flash in 1 category; one gap is clear of the uncertainty.
- The widest gap is in coding, where Gemini 3.7 Flash leads 55.7 to 39.8.
- DeepSeek-V2 (MoE-236B, May 2024) has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-V2 (MoE-236B, May 2024) | Gemini 3.7 Flash | |
|---|---|---|
| Provider | DeepSeek | |
| Noometry Index | 39.7 | 59.8 |
| Released | 2024-05-07 | 2026-08-13 |
| Weights | Open | Proprietary |
| Context window | — | 1.05M |
| Max output | — | 66K |
| Input $ / M tokens | — | $0.75 |
| Output $ / M tokens | — | $3.75 |
| Results tracked | 10 | 44 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding Gemini 3.7 Flash leads
DeepSeek-V2 (MoE-236B, May 2024): 39.8 (#141), Gemini 3.7 Flash: 55.7 (#19)
| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | Gemini 3.7 Flash |
|---|---|---|
| DeepSWE | — | 65.5% |
| FrontierCode | — | 43.6% |
| LMArena WebDev | — | 1593 |
| FrontierSWE | — | 20.3% |
| SciCode | — | 59.8% |
| BigCodeBench Instruct | 48.9% | — |
| LMArena Coding | — | 1499 |
| BigCodeBench Complete | 59.4% | — |
| ALE-Bench | — | 904.3 |
Agentic & Tool Use Not comparable
DeepSeek-V2 (MoE-236B, May 2024): —, Gemini 3.7 Flash: 42.2 (#18)
| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | Gemini 3.7 Flash |
|---|---|---|
| APEX-Agents | — | 67.8% |
| Remote Labor Index | — | 5% |
| GDP.pdf | — | 23.8% |
Reasoning Not comparable
DeepSeek-V2 (MoE-236B, May 2024): —, Gemini 3.7 Flash: 70.1 (#12)
| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | Gemini 3.7 Flash |
|---|---|---|
| Epoch Capabilities Index | 124.78 | 157.72 |
| ARC-AGI-2 | — | 84.6% |
| NYT Connections (extended) | — | 94% |
| ARC-AGI-1 | — | 95.5% |
| CritPt | — | 14.3% |
| Chess Puzzles | — | 47% |
| LMArena Hard Prompts | — | 1497 |
| Mystery Game Puzzles | — | 37% |
| DTBench | — | 96.8% |
| LMCA | — | 50.4% |
| BIG-Bench Hard | 78.8% | — |
| HellaSwag | 87.1% | — |
| PIQA | 83.9% | — |
| WinoGrande | 86.3% | — |
Math Not comparable
DeepSeek-V2 (MoE-236B, May 2024): —, Gemini 3.7 Flash: 69.5 (#18)
| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | Gemini 3.7 Flash |
|---|---|---|
| FrontierMath (Tiers 1-3) | — | 71.6% |
| FrontierMath Tier 4 | — | 36.6% |
| OTIS Mock AIME 2024-2025 | — | 97.2% |
| ProofBench | — | 58% |
| LMArena Math | — | 1511 |
Knowledge Not comparable
DeepSeek-V2 (MoE-236B, May 2024): —, Gemini 3.7 Flash: 69.8 (#4)
| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | Gemini 3.7 Flash |
|---|---|---|
| GPQA Diamond | — | 94.8% |
| SimpleQA Verified | — | 69.2% |
| LMArena Expert | — | 1508 |
| ARC (AI2) Challenge | 92.2% | — |
| MMLU | 78.4% | — |
| TriviaQA | 80% | — |
Multimodal Not comparable
DeepSeek-V2 (MoE-236B, May 2024): —, Gemini 3.7 Flash: 36.9 (#69)
| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | Gemini 3.7 Flash |
|---|---|---|
| LMArena Vision | — | 1314 |
| Furniture Assembly | — | 26.7% |
Multilingual Not comparable
DeepSeek-V2 (MoE-236B, May 2024): —, Gemini 3.7 Flash: 57.8 (#7)
| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | Gemini 3.7 Flash |
|---|---|---|
| LMArena Non-English | — | 1485 |
| LMArena Chinese | — | 1553 |
| LMArena French | — | 1512 |
| LMArena German | — | 1485 |
| LMArena Japanese | — | 1506 |
| LMArena Korean | — | 1486 |
| LMArena Russian | — | 1519 |
| LMArena Spanish | — | 1507 |
Instruction Following Not comparable
DeepSeek-V2 (MoE-236B, May 2024): —, Gemini 3.7 Flash: 78.0 (#12)
| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | Gemini 3.7 Flash |
|---|---|---|
| LMArena Instruction Following | — | 1486 |
Long Context Not comparable
DeepSeek-V2 (MoE-236B, May 2024): —, Gemini 3.7 Flash: 45.8 (#28)
| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | Gemini 3.7 Flash |
|---|---|---|
| LMArena Longer Query | — | 1493 |
Writing & Preference Not comparable
DeepSeek-V2 (MoE-236B, May 2024): —, Gemini 3.7 Flash: 71.4 (#19)
| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | Gemini 3.7 Flash |
|---|---|---|
| LMArena Text | — | 1487 |
| LMArena Creative Writing | — | 1490 |
| EQ-Bench Creative Writing | — | 1723 |
| LMArena Multi-Turn | — | 1495 |
Frequently asked questions
Is DeepSeek-V2 (MoE-236B, May 2024) better than Gemini 3.7 Flash?
Gemini 3.7 Flash has enough public results to be ranked (#11); DeepSeek-V2 (MoE-236B, May 2024) does not yet, so treat this comparison as directional.
Is DeepSeek-V2 (MoE-236B, May 2024) or Gemini 3.7 Flash better for coding?
Gemini 3.7 Flash scores higher on coding benchmarks: 55.7 versus 39.8 in the Noometry coding category.
How many benchmarks do DeepSeek-V2 (MoE-236B, May 2024) and Gemini 3.7 Flash share?
1 benchmark has published results for both models. DeepSeek-V2 (MoE-236B, May 2024) has 10 scored results on Noometry and Gemini 3.7 Flash has 44.