Model comparison
DeepSeek-V3.2-Exp vs Gemini 2.0 Pro
DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.9 to 38.6 on the Noometry Index.
Last verified . 4 shared benchmarks.
DeepSeek
GoogleSummary
- They share 4 benchmarks with published results for both. DeepSeek-V3.2-Exp scores higher in 6 categories and Gemini 2.0 Pro in 1 category; 5 gaps are clear of the uncertainty.
- The widest gap is in long context, where DeepSeek-V3.2-Exp leads 47.6 to 29.1.
- The biggest single-benchmark swing is Fiction.LiveBench: 83.3% for DeepSeek-V3.2-Exp and 41.7% for Gemini 2.0 Pro.
- DeepSeek-V3.2-Exp has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-V3.2-Exp | Gemini 2.0 Pro | |
|---|---|---|
| Provider | DeepSeek | |
| Noometry Index | 44.9 | 38.6 |
| 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 | 48 | 14 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 46.4 (#61), Gemini 2.0 Pro: 37.4 (#182)
| Benchmark | DeepSeek-V3.2-Exp | Gemini 2.0 Pro |
|---|---|---|
| Aider Polyglot | 74.2% | 35.6% |
| SWE-bench Verified (bash only) | 70% | — |
| LMArena WebDev | 1362 | — |
| SWE-bench Multilingual | 59% | — |
| SciCode | 38.9% | — |
| WeirdML | 39.5% | — |
| LiveBench Coding | — | 63.5% |
| LMArena Coding | 1454 | — |
Agentic & Tool Use Not comparable
DeepSeek-V3.2-Exp: 37.9 (#31), Gemini 2.0 Pro: —
| Benchmark | DeepSeek-V3.2-Exp | Gemini 2.0 Pro |
|---|---|---|
| Terminal-Bench | 39.6% | — |
| Berkeley Function Calling Leaderboard | 56.7% | — |
| TheAgentCompany | 42.9% | — |
| Vending-Bench 2 | 1,034 | — |
Reasoning Too close to call
DeepSeek-V3.2-Exp: 22.0 (#185), Gemini 2.0 Pro: 21.6 (#194)
| Benchmark | DeepSeek-V3.2-Exp | Gemini 2.0 Pro |
|---|---|---|
| Epoch Capabilities Index | 146.28 | 135.06 |
| ARC-AGI-2 | 4% | — |
| Kagi LLM Benchmark | 52.2% | — |
| NYT Connections (extended) | 36.7% | — |
| ARC-AGI-1 | 57% | — |
| CritPt | 2.9% | — |
| Chess Puzzles | 14% | — |
| EnigmaEval | — | 0.7% |
| Thematic Generalization | 65% | — |
| LiveBench Reasoning | — | 60.1% |
| LMArena Hard Prompts | 1433 | — |
| DTBench | 87.7% | — |
| LiveBench Data Analysis | — | 68% |
| LMCA | 29.1% | — |
| LiveBench | — | 65.1% |
Math DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 40.8 (#75), Gemini 2.0 Pro: 37.1 (#101)
| Benchmark | DeepSeek-V3.2-Exp | Gemini 2.0 Pro |
|---|---|---|
| MathArena Final-Answer Competitions | 57.7% | — |
| OTIS Mock AIME 2024-2025 | 87.8% | — |
| ProofBench | 8% | — |
| LiveBench Math | — | 71% |
| LMArena Math | 1435 | — |
| MATH Level 5 | — | 83.5% |
| FrontierMath (Feb 2025 set) | 22.1% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 51.6 (#61), Gemini 2.0 Pro: 36.5 (#156)
| Benchmark | DeepSeek-V3.2-Exp | Gemini 2.0 Pro |
|---|---|---|
| GPQA Diamond | 83.4% | 65.7% |
| Confabulations | — | 18.4% |
| Vectara Hallucination Rate | 5.3% | — |
| LMArena Expert | 1436 | — |
Multilingual Not comparable
DeepSeek-V3.2-Exp: 52.4 (#85), Gemini 2.0 Pro: —
| Benchmark | DeepSeek-V3.2-Exp | Gemini 2.0 Pro |
|---|---|---|
| LMArena Non-English | 1409 | — |
| LMArena Chinese | 1461 | — |
| LMArena French | 1435 | — |
| LMArena German | 1440 | — |
| LMArena Japanese | 1377 | — |
| LMArena Korean | 1372 | — |
| LMArena Russian | 1423 | — |
| LMArena Spanish | 1441 | — |
Instruction Following Too close to call
DeepSeek-V3.2-Exp: 74.7 (#89), Gemini 2.0 Pro: 75.6 (#57)
| Benchmark | DeepSeek-V3.2-Exp | Gemini 2.0 Pro |
|---|---|---|
| LiveBench Instruction Following | — | 83.4% |
| LMArena Instruction Following | 1413 | — |
Long Context DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 47.6 (#15), Gemini 2.0 Pro: 29.1 (#287)
| Benchmark | DeepSeek-V3.2-Exp | Gemini 2.0 Pro |
|---|---|---|
| Fiction.LiveBench | 83.3% | 41.7% |
| CL-bench | 13.2% | — |
| CL-bench Life | 9.5% | — |
| LMArena Longer Query | 1428 | — |
Writing & Preference DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 62.5 (#73), Gemini 2.0 Pro: 52.7 (#164)
| Benchmark | DeepSeek-V3.2-Exp | Gemini 2.0 Pro |
|---|---|---|
| LMArena Text | 1425 | — |
| LMArena Creative Writing | 1403 | — |
| EQ-Bench Creative Writing | 1515 | — |
| LMArena Multi-Turn | 1427 | — |
| LiveBench Language | — | 44.9% |
Frequently asked questions
Is DeepSeek-V3.2-Exp better than Gemini 2.0 Pro?
DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.9 to 38.6 on the Noometry Index.
Is DeepSeek-V3.2-Exp or Gemini 2.0 Pro better for coding?
DeepSeek-V3.2-Exp scores higher on coding benchmarks: 46.4 versus 37.4 in the Noometry coding category.
How many benchmarks do DeepSeek-V3.2-Exp and Gemini 2.0 Pro share?
4 benchmarks have published results for both models. DeepSeek-V3.2-Exp has 48 scored results on Noometry and Gemini 2.0 Pro has 14.