Model comparison
DeepSeek-V3.2-Exp vs Gemini 2.0 Flash-Lite
DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.3 to 37.8 on the Noometry Index.
Last verified . 18 shared benchmarks.
DeepSeek
GoogleSummary
- They share 18 benchmarks with published results for both. DeepSeek-V3.2-Exp scores higher in 8 categories and Gemini 2.0 Flash-Lite in 0 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where DeepSeek-V3.2-Exp leads 51.7 to 35.0.
- The biggest single-benchmark swing is DTBench: 87.7% for DeepSeek-V3.2-Exp and 52.5% for Gemini 2.0 Flash-Lite.
- DeepSeek-V3.2-Exp has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-V3.2-Exp | Gemini 2.0 Flash-Lite | |
|---|---|---|
| Provider | DeepSeek | |
| Noometry Index | 44.3 | 37.8 |
| Released | 2025-09-29 | 2025-02-05 |
| Weights | Open | Proprietary |
| Context window | 164K | — |
| Max output | 66K | — |
| Input $ / M tokens | $0.26 | — |
| Output $ / M tokens | $0.38 | — |
| Results tracked | 49 | 32 |
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Category by category
Coding DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 46.5 (#65), Gemini 2.0 Flash-Lite: 37.9 (#185)
| Benchmark | DeepSeek-V3.2-Exp | Gemini 2.0 Flash-Lite |
|---|---|---|
| LMArena Coding | 1454 | 1322 |
| SWE-bench Verified (bash only) | 70% | — |
| Aider Polyglot | 74.2% | — |
| LMArena WebDev | 1362 | — |
| SWE-bench Multilingual | 59% | — |
| SciCode | 38.9% | — |
| WeirdML | 39.5% | — |
| LiveBench Coding | — | 47.1% |
Agentic & Tool Use Not comparable
DeepSeek-V3.2-Exp: 32.7 (#59), Gemini 2.0 Flash-Lite: —
| Benchmark | DeepSeek-V3.2-Exp | Gemini 2.0 Flash-Lite |
|---|---|---|
| Terminal-Bench | 39.6% | — |
| APEX-Agents | 21.3% | — |
| Berkeley Function Calling Leaderboard | 56.7% | — |
| TheAgentCompany | 42.9% | — |
| Vending-Bench 2 | 1,034 | — |
Reasoning Too close to call
DeepSeek-V3.2-Exp: 22.1 (#208), Gemini 2.0 Flash-Lite: 22.0 (#210)
| Benchmark | DeepSeek-V3.2-Exp | Gemini 2.0 Flash-Lite |
|---|---|---|
| LMArena Hard Prompts | 1434 | 1324 |
| DTBench | 87.7% | 52.5% |
| 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% | — |
| LiveBench Reasoning | — | 50.1% |
| LiveBench Data Analysis | — | 65.5% |
| LMCA | 29.1% | — |
| Epoch Capabilities Index | 146.27 | — |
| ForecastBench | — | 57.1 |
| LiveBench | — | 54.3% |
Math DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 41.7 (#87), Gemini 2.0 Flash-Lite: 34.1 (#196)
| Benchmark | DeepSeek-V3.2-Exp | Gemini 2.0 Flash-Lite |
|---|---|---|
| LMArena Math | 1435 | 1309 |
| MathArena Final-Answer Competitions | 57.7% | — |
| OTIS Mock AIME 2024-2025 | 87.8% | — |
| ProofBench | 8% | — |
| Omni-MATH | — | 37.4% |
| LiveBench Math | — | 58.1% |
| FrontierMath (Feb 2025 set) | 22.1% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 51.7 (#66), Gemini 2.0 Flash-Lite: 35.0 (#189)
| Benchmark | DeepSeek-V3.2-Exp | Gemini 2.0 Flash-Lite |
|---|---|---|
| LMArena Expert | 1436 | 1305 |
| GPQA Diamond | 83.4% | — |
| MMLU-Pro | — | 72% |
| Vectara Hallucination Rate | 5.3% | — |
| GPQA (HELM) | — | 50% |
Multimodal Not comparable
DeepSeek-V3.2-Exp: —, Gemini 2.0 Flash-Lite: 31.2 (#109)
| Benchmark | DeepSeek-V3.2-Exp | Gemini 2.0 Flash-Lite |
|---|---|---|
| LMArena Vision | — | 1100 |
Multilingual DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 52.2 (#90), Gemini 2.0 Flash-Lite: 46.0 (#161)
| Benchmark | DeepSeek-V3.2-Exp | Gemini 2.0 Flash-Lite |
|---|---|---|
| LMArena Non-English | 1409 | 1323 |
| LMArena Chinese | 1461 | 1339 |
| LMArena French | 1433 | 1347 |
| LMArena German | 1440 | 1306 |
| LMArena Japanese | 1374 | 1301 |
| LMArena Korean | 1371 | 1325 |
| LMArena Russian | 1424 | 1328 |
| LMArena Spanish | 1440 | 1313 |
Instruction Following DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 74.5 (#93), Gemini 2.0 Flash-Lite: 70.4 (#163)
| Benchmark | DeepSeek-V3.2-Exp | Gemini 2.0 Flash-Lite |
|---|---|---|
| LMArena Instruction Following | 1413 | 1305 |
| LiveBench Instruction Following | — | 78.3% |
| IFEval | — | 82.4% |
Long Context DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 47.6 (#16), Gemini 2.0 Flash-Lite: 40.1 (#160)
| Benchmark | DeepSeek-V3.2-Exp | Gemini 2.0 Flash-Lite |
|---|---|---|
| LMArena Longer Query | 1428 | 1320 |
| 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 2.0 Flash-Lite: 51.7 (#177)
| Benchmark | DeepSeek-V3.2-Exp | Gemini 2.0 Flash-Lite |
|---|---|---|
| LMArena Text | 1425 | 1330 |
| LMArena Creative Writing | 1403 | 1319 |
| LMArena Multi-Turn | 1427 | 1307 |
| EQ-Bench Creative Writing | 1515 | — |
| WildBench | — | 79% |
| LiveBench Language | — | 34.3% |
Frequently asked questions
Is DeepSeek-V3.2-Exp better than Gemini 2.0 Flash-Lite?
DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.3 to 37.8 on the Noometry Index.
Is DeepSeek-V3.2-Exp or Gemini 2.0 Flash-Lite better for coding?
DeepSeek-V3.2-Exp scores higher on coding benchmarks: 46.5 versus 37.9 in the Noometry coding category.
How many benchmarks do DeepSeek-V3.2-Exp and Gemini 2.0 Flash-Lite share?
18 benchmarks have published results for both models. DeepSeek-V3.2-Exp has 49 scored results on Noometry and Gemini 2.0 Flash-Lite has 32.