Model comparison
DeepSeek-V3.2-Exp vs Gemini 1.5 Flash (May 2024)
DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.3 to 33.2 on the Noometry Index.
Last verified . 23 shared benchmarks.
DeepSeek
GoogleSummary
- They share 23 benchmarks with published results for both. DeepSeek-V3.2-Exp scores higher in 9 categories and Gemini 1.5 Flash (May 2024) in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where DeepSeek-V3.2-Exp leads 51.7 to 26.2.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 87.8% for DeepSeek-V3.2-Exp and 16.3% for Gemini 1.5 Flash (May 2024).
- DeepSeek-V3.2-Exp has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-V3.2-Exp | Gemini 1.5 Flash (May 2024) | |
|---|---|---|
| Provider | DeepSeek | |
| Noometry Index | 44.3 | 33.2 |
| Released | 2025-09-29 | 2024-05-14 |
| Weights | Open | Proprietary |
| Context window | 164K | — |
| Max output | 66K | — |
| Input $ / M tokens | $0.26 | — |
| Output $ / M tokens | $0.38 | — |
| Results tracked | 49 | 42 |
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Category by category
Coding DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 46.5 (#65), Gemini 1.5 Flash (May 2024): 34.4 (#236)
| Benchmark | DeepSeek-V3.2-Exp | Gemini 1.5 Flash (May 2024) |
|---|---|---|
| WeirdML | 39.5% | 24.9% |
| LMArena Coding | 1454 | 1261 |
| SWE-bench Verified (bash only) | 70% | — |
| Aider Polyglot | 74.2% | — |
| LMArena WebDev | 1362 | — |
| SWE-bench Multilingual | 59% | — |
| SciCode | 38.9% | — |
| BigCodeBench Instruct | — | 43.5% |
| BigCodeBench Complete | — | 55.1% |
| HumanEval+ | — | 75.6% |
| MBPP+ | — | 67.5% |
Agentic & Tool Use DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 32.7 (#59), Gemini 1.5 Flash (May 2024): 26.6 (#102)
| Benchmark | DeepSeek-V3.2-Exp | Gemini 1.5 Flash (May 2024) |
|---|---|---|
| Terminal-Bench | 39.6% | — |
| APEX-Agents | 21.3% | — |
| Berkeley Function Calling Leaderboard | 56.7% | — |
| TheAgentCompany | 42.9% | — |
| BALROG | — | 14.6% |
| Vending-Bench 2 | 1,034 | — |
Reasoning Too close to call
DeepSeek-V3.2-Exp: 22.1 (#208), Gemini 1.5 Flash (May 2024): 21.7 (#215)
| Benchmark | DeepSeek-V3.2-Exp | Gemini 1.5 Flash (May 2024) |
|---|---|---|
| LMArena Hard Prompts | 1434 | 1257 |
| DTBench | 87.7% | 53.8% |
| Epoch Capabilities Index | 146.27 | 129.36 |
| ARC-AGI-2 | 4% | — |
| Kagi LLM Benchmark | 52.2% | — |
| NYT Connections (extended) | 36.7% | — |
| ARC-AGI-1 | 57% | — |
| CritPt | 2.9% | — |
| Chess Puzzles | 14% | — |
| Thematic Generalization | 65% | — |
| LMCA | 29.1% | — |
| ForecastBench | — | 53.9 |
| PIQA | — | 87.5% |
Math DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 41.7 (#87), Gemini 1.5 Flash (May 2024): 22.1 (#281)
| Benchmark | DeepSeek-V3.2-Exp | Gemini 1.5 Flash (May 2024) |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 87.8% | 16.3% |
| LMArena Math | 1435 | 1269 |
| FrontierMath (Feb 2025 set) | 22.1% | 0% |
| MathArena Final-Answer Competitions | 57.7% | — |
| ProofBench | 8% | — |
| Omni-MATH | — | 30.4% |
| MATH Level 5 | — | 61.9% |
| FrontierMath Tier 4 (v1) | 2.1% | — |
| GSM8K | — | 82.4% |
Knowledge DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 51.7 (#66), Gemini 1.5 Flash (May 2024): 26.2 (#260)
| Benchmark | DeepSeek-V3.2-Exp | Gemini 1.5 Flash (May 2024) |
|---|---|---|
| GPQA Diamond | 83.4% | 47.3% |
| LMArena Expert | 1436 | 1233 |
| MMLU-Pro | — | 67.8% |
| Vectara Hallucination Rate | 5.3% | — |
| GPQA (HELM) | — | 43.7% |
| BoolQ | — | 85.8% |
| MMLU | — | 77.9% |
Multimodal Not comparable
DeepSeek-V3.2-Exp: —, Gemini 1.5 Flash (May 2024): 36.0 (#81)
| Benchmark | DeepSeek-V3.2-Exp | Gemini 1.5 Flash (May 2024) |
|---|---|---|
| LMArena Vision | — | 1141 |
| Video-MME | — | 70.3% |
| GeoBench | — | 76% |
Multilingual DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 52.2 (#90), Gemini 1.5 Flash (May 2024): 42.9 (#189)
| Benchmark | DeepSeek-V3.2-Exp | Gemini 1.5 Flash (May 2024) |
|---|---|---|
| LMArena Non-English | 1409 | 1278 |
| LMArena Chinese | 1461 | 1295 |
| LMArena French | 1433 | 1258 |
| LMArena German | 1440 | 1262 |
| LMArena Japanese | 1374 | 1252 |
| LMArena Korean | 1371 | 1221 |
| LMArena Russian | 1424 | 1288 |
| LMArena Spanish | 1440 | 1243 |
Instruction Following DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 74.5 (#93), Gemini 1.5 Flash (May 2024): 66.8 (#205)
| Benchmark | DeepSeek-V3.2-Exp | Gemini 1.5 Flash (May 2024) |
|---|---|---|
| LMArena Instruction Following | 1413 | 1258 |
| IFEval | — | 83.1% |
Long Context DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 47.6 (#16), Gemini 1.5 Flash (May 2024): 39.0 (#187)
| Benchmark | DeepSeek-V3.2-Exp | Gemini 1.5 Flash (May 2024) |
|---|---|---|
| LMArena Longer Query | 1428 | 1284 |
| Fiction.LiveBench | 83.3% | — |
| CL-bench | 13.2% | — |
| CL-bench Life | 9.5% | — |
Writing & Preference DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 62.4 (#77), Gemini 1.5 Flash (May 2024): 48.7 (#196)
| Benchmark | DeepSeek-V3.2-Exp | Gemini 1.5 Flash (May 2024) |
|---|---|---|
| LMArena Text | 1425 | 1287 |
| LMArena Creative Writing | 1403 | 1285 |
| LMArena Multi-Turn | 1427 | 1253 |
| EQ-Bench Creative Writing | 1515 | — |
| WildBench | — | 79.2% |
Frequently asked questions
Is DeepSeek-V3.2-Exp better than Gemini 1.5 Flash (May 2024)?
DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.3 to 33.2 on the Noometry Index.
Is DeepSeek-V3.2-Exp or Gemini 1.5 Flash (May 2024) better for coding?
DeepSeek-V3.2-Exp scores higher on coding benchmarks: 46.5 versus 34.4 in the Noometry coding category.
How many benchmarks do DeepSeek-V3.2-Exp and Gemini 1.5 Flash (May 2024) share?
23 benchmarks have published results for both models. DeepSeek-V3.2-Exp has 49 scored results on Noometry and Gemini 1.5 Flash (May 2024) has 42.