Model comparison
DeepSeek Coder 1.3B vs Gemini 2.5 Flash
Gemini 2.5 Flash has enough public results to be ranked (#170); DeepSeek Coder 1.3B 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 Coder 1.3B scores higher in 0 categories and Gemini 2.5 Flash in 1 category; one gap is clear of the uncertainty.
- The widest gap is in coding, where Gemini 2.5 Flash leads 35.8 to 31.2.
- DeepSeek Coder 1.3B has downloadable open weights; the other is API-only.
Side by side
| DeepSeek Coder 1.3B | Gemini 2.5 Flash | |
|---|---|---|
| Provider | DeepSeek | |
| Noometry Index | 35.0 | 39.3 |
| Released | 2023-11-02 | 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 | 9 | 54 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding Gemini 2.5 Flash leads
DeepSeek Coder 1.3B: 31.2 (#287), Gemini 2.5 Flash: 35.8 (#220)
| Benchmark | DeepSeek Coder 1.3B | Gemini 2.5 Flash |
|---|---|---|
| SWE-bench Verified (bash only) | — | 28.7% |
| Aider Polyglot | — | 55.1% |
| WeirdML | — | 41.9% |
| BigCodeBench Instruct | 22.8% | — |
| LMArena Coding | — | 1424 |
| BigCodeBench Complete | 29.6% | — |
| ALE-Bench | — | 661.88 |
| HumanEval+ | 60.4% | — |
| MBPP+ | 54.8% | — |
Agentic & Tool Use Not comparable
DeepSeek Coder 1.3B: —, Gemini 2.5 Flash: 30.8 (#74)
| Benchmark | DeepSeek Coder 1.3B | 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 Not comparable
DeepSeek Coder 1.3B: —, Gemini 2.5 Flash: 18.1 (#286)
| Benchmark | DeepSeek Coder 1.3B | Gemini 2.5 Flash |
|---|---|---|
| Epoch Capabilities Index | 63.6 | 143.03 |
| ARC-AGI-2 | — | 2.5% |
| SimpleBench | — | 41.2% |
| Kagi LLM Benchmark | — | 56.8% |
| ARC-AGI-1 | — | 33.3% |
| CritPt | — | 1.1% |
| EnigmaEval | — | 2.7% |
| LMArena Hard Prompts | — | 1422 |
| DTBench | — | 76.5% |
| LMCA | — | 27.5% |
| ForecastBench | — | 60.6 |
| WinoGrande | 53.3% | — |
Math Not comparable
DeepSeek Coder 1.3B: —, Gemini 2.5 Flash: 39.9 (#98)
| Benchmark | DeepSeek Coder 1.3B | Gemini 2.5 Flash |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 73.1% |
| Omni-MATH | — | 38.5% |
| LMArena Math | — | 1415 |
| FrontierMath (Feb 2025 set) | — | 4.8% |
| FrontierMath Tier 4 (v1) | — | 4.2% |
| GSM8K | 4.4% | — |
Knowledge Not comparable
DeepSeek Coder 1.3B: —, Gemini 2.5 Flash: 36.4 (#168)
| Benchmark | DeepSeek Coder 1.3B | Gemini 2.5 Flash |
|---|---|---|
| Humanity's Last Exam | — | 12.1% |
| MMLU-Pro | — | 63.9% |
| Confabulations | — | 16.8% |
| Vectara Hallucination Rate | — | 7.8% |
| GPQA (HELM) | — | 39% |
| LMArena Expert | — | 1426 |
| ARC (AI2) Challenge | 25.4% | — |
| MMLU | 25.8% | — |
Multimodal Not comparable
DeepSeek Coder 1.3B: —, Gemini 2.5 Flash: 41.8 (#32)
| Benchmark | DeepSeek Coder 1.3B | Gemini 2.5 Flash |
|---|---|---|
| LMArena Vision | — | 1253 |
| GeoBench | — | 76% |
| VPCT | — | 46.2% |
| SpatialViz-Bench | — | 36.9% |
Multilingual Not comparable
DeepSeek Coder 1.3B: —, Gemini 2.5 Flash: 52.3 (#88)
| Benchmark | DeepSeek Coder 1.3B | 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 Coder 1.3B: —, Gemini 2.5 Flash: 75.7 (#54)
| Benchmark | DeepSeek Coder 1.3B | Gemini 2.5 Flash |
|---|---|---|
| IFEval | — | 89.8% |
| LMArena Instruction Following | — | 1405 |
Long Context Not comparable
DeepSeek Coder 1.3B: —, Gemini 2.5 Flash: 47.5 (#17)
| Benchmark | DeepSeek Coder 1.3B | Gemini 2.5 Flash |
|---|---|---|
| Fiction.LiveBench | — | 77.8% |
| LMArena Longer Query | — | 1419 |
Writing & Preference Not comparable
DeepSeek Coder 1.3B: —, Gemini 2.5 Flash: 53.8 (#157)
| Benchmark | DeepSeek Coder 1.3B | 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 Coder 1.3B better than Gemini 2.5 Flash?
Gemini 2.5 Flash has enough public results to be ranked (#170); DeepSeek Coder 1.3B does not yet, so treat this comparison as directional.
Is DeepSeek Coder 1.3B or Gemini 2.5 Flash better for coding?
Gemini 2.5 Flash scores higher on coding benchmarks: 35.8 versus 31.2 in the Noometry coding category.
How many benchmarks do DeepSeek Coder 1.3B and Gemini 2.5 Flash share?
1 benchmark has published results for both models. DeepSeek Coder 1.3B has 9 scored results on Noometry and Gemini 2.5 Flash has 54.