Model comparison
DeepSeek-R1-Distill-Qwen-14B vs Gemini 2.5 Flash
Gemini 2.5 Flash is the stronger model overall, scoring 39.3 to 32.7 on the Noometry Index.
Last verified . 2 shared benchmarks.
DeepSeek
GoogleSummary
- They share 2 benchmarks with published results for both. DeepSeek-R1-Distill-Qwen-14B scores higher in 2 categories and Gemini 2.5 Flash in 2 categories; 4 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Gemini 2.5 Flash leads 36.4 to 24.1.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 50.6% for DeepSeek-R1-Distill-Qwen-14B and 73.1% for Gemini 2.5 Flash.
- DeepSeek-R1-Distill-Qwen-14B has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-R1-Distill-Qwen-14B | Gemini 2.5 Flash | |
|---|---|---|
| Provider | DeepSeek | |
| Noometry Index | 32.7 | 39.3 |
| Released | 2025-01-20 | 2025-04-17 |
| Weights | Open | Proprietary |
| Context window | — | 1.05M |
| Max output | — | 66K |
| Input $ / M tokens | — | $0.30 |
| Output $ / M tokens | — | $2.50 |
| Results tracked | 7 | 54 |
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Category by category
Coding DeepSeek-R1-Distill-Qwen-14B leads
DeepSeek-R1-Distill-Qwen-14B: 36.9 (#200), Gemini 2.5 Flash: 35.8 (#220)
| Benchmark | DeepSeek-R1-Distill-Qwen-14B | Gemini 2.5 Flash |
|---|---|---|
| SWE-bench Verified (bash only) | — | 28.7% |
| Aider Polyglot | — | 55.1% |
| WeirdML | — | 41.9% |
| BigCodeBench Instruct | 38.1% | — |
| LMArena Coding | — | 1424 |
| BigCodeBench Complete | 48.4% | — |
| ALE-Bench | — | 661.88 |
Agentic & Tool Use Not comparable
DeepSeek-R1-Distill-Qwen-14B: —, Gemini 2.5 Flash: 30.8 (#74)
| Benchmark | DeepSeek-R1-Distill-Qwen-14B | Gemini 2.5 Flash |
|---|---|---|
| Terminal-Bench | — | 17.1% |
| Berkeley Function Calling Leaderboard | — | 56.2% |
| TheAgentCompany | — | 41.1% |
| BALROG | — | 33.5% |
| Vending-Bench 2 | — | 548.84 |
Reasoning DeepSeek-R1-Distill-Qwen-14B leads
DeepSeek-R1-Distill-Qwen-14B: 19.2 (#263), Gemini 2.5 Flash: 18.1 (#286)
| Benchmark | DeepSeek-R1-Distill-Qwen-14B | Gemini 2.5 Flash |
|---|---|---|
| Epoch Capabilities Index | 135.43 | 143.03 |
| ARC-AGI-2 | — | 2.5% |
| SimpleBench | — | 41.2% |
| Kagi LLM Benchmark | — | 56.8% |
| ARC-AGI-1 | — | 33.3% |
| CritPt | — | 1.1% |
| Chess Puzzles | 1% | — |
| EnigmaEval | — | 2.7% |
| LMArena Hard Prompts | — | 1422 |
| DTBench | — | 76.5% |
| LMCA | — | 27.5% |
| ForecastBench | — | 60.6 |
Math Gemini 2.5 Flash leads
DeepSeek-R1-Distill-Qwen-14B: 35.5 (#184), Gemini 2.5 Flash: 39.9 (#98)
| Benchmark | DeepSeek-R1-Distill-Qwen-14B | Gemini 2.5 Flash |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 50.6% | 73.1% |
| Omni-MATH | — | 38.5% |
| LMArena Math | — | 1415 |
| MATH Level 5 | 87.1% | — |
| FrontierMath (Feb 2025 set) | — | 4.8% |
| FrontierMath Tier 4 (v1) | — | 4.2% |
Knowledge Gemini 2.5 Flash leads
DeepSeek-R1-Distill-Qwen-14B: 24.1 (#270), Gemini 2.5 Flash: 36.4 (#168)
| Benchmark | DeepSeek-R1-Distill-Qwen-14B | Gemini 2.5 Flash |
|---|---|---|
| GPQA Diamond | 44.7% | — |
| Humanity's Last Exam | — | 12.1% |
| MMLU-Pro | — | 63.9% |
| Confabulations | — | 16.8% |
| Vectara Hallucination Rate | — | 7.8% |
| GPQA (HELM) | — | 39% |
| LMArena Expert | — | 1426 |
Multimodal Not comparable
DeepSeek-R1-Distill-Qwen-14B: —, Gemini 2.5 Flash: 41.8 (#32)
| Benchmark | DeepSeek-R1-Distill-Qwen-14B | Gemini 2.5 Flash |
|---|---|---|
| LMArena Vision | — | 1253 |
| GeoBench | — | 76% |
| VPCT | — | 46.2% |
| SpatialViz-Bench | — | 36.9% |
Multilingual Not comparable
DeepSeek-R1-Distill-Qwen-14B: —, Gemini 2.5 Flash: 52.3 (#88)
| Benchmark | DeepSeek-R1-Distill-Qwen-14B | Gemini 2.5 Flash |
|---|---|---|
| LMArena Non-English | — | 1409 |
| LMArena Chinese | — | 1450 |
| LMArena French | — | 1433 |
| LMArena German | — | 1418 |
| LMArena Japanese | — | 1405 |
| LMArena Korean | — | 1385 |
| LMArena Russian | — | 1415 |
| LMArena Spanish | — | 1421 |
Instruction Following Not comparable
DeepSeek-R1-Distill-Qwen-14B: —, Gemini 2.5 Flash: 75.7 (#54)
| Benchmark | DeepSeek-R1-Distill-Qwen-14B | Gemini 2.5 Flash |
|---|---|---|
| IFEval | — | 89.8% |
| LMArena Instruction Following | — | 1405 |
Long Context Not comparable
DeepSeek-R1-Distill-Qwen-14B: —, Gemini 2.5 Flash: 47.5 (#17)
| Benchmark | DeepSeek-R1-Distill-Qwen-14B | Gemini 2.5 Flash |
|---|---|---|
| Fiction.LiveBench | — | 77.8% |
| LMArena Longer Query | — | 1419 |
Writing & Preference Not comparable
DeepSeek-R1-Distill-Qwen-14B: —, Gemini 2.5 Flash: 53.8 (#157)
| Benchmark | DeepSeek-R1-Distill-Qwen-14B | Gemini 2.5 Flash |
|---|---|---|
| LMArena Text | — | 1417 |
| LMArena Creative Writing | — | 1400 |
| Short-Story Creative Writing | — | 76.5% |
| EQ-Bench Creative Writing | — | 1137 |
| WildBench | — | 81.7% |
| LMArena Multi-Turn | — | 1408 |
Frequently asked questions
Is DeepSeek-R1-Distill-Qwen-14B better than Gemini 2.5 Flash?
Gemini 2.5 Flash is the stronger model overall, scoring 39.3 to 32.7 on the Noometry Index.
Is DeepSeek-R1-Distill-Qwen-14B or Gemini 2.5 Flash better for coding?
DeepSeek-R1-Distill-Qwen-14B scores higher on coding benchmarks: 36.9 versus 35.8 in the Noometry coding category.
How many benchmarks do DeepSeek-R1-Distill-Qwen-14B and Gemini 2.5 Flash share?
2 benchmarks have published results for both models. DeepSeek-R1-Distill-Qwen-14B has 7 scored results on Noometry and Gemini 2.5 Flash has 54.