Model comparison
DeepSeek-V3.2-Speciale vs Gemini 2.5 Flash-Lite
DeepSeek-V3.2-Speciale is the stronger model overall, scoring 39.7 to 37.0 on the Noometry Index. Gemini 2.5 Flash-Lite costs 4.9× less per token, which makes it the better buy when DeepSeek-V3.2-Speciale's lead doesn't matter for your workload.
Last verified . 1 shared benchmarks.
DeepSeek
GoogleSummary
- They share 1 benchmark with published results for both. DeepSeek-V3.2-Speciale scores higher in 2 categories and Gemini 2.5 Flash-Lite in 1 category; 3 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where Gemini 2.5 Flash-Lite leads 56.8 to 46.0.
- The biggest single-benchmark swing is WeirdML: 46.7% for DeepSeek-V3.2-Speciale and 35.2% for Gemini 2.5 Flash-Lite.
- Gemini 2.5 Flash-Lite is cheaper at $0.10 / $0.40 per million input/output tokens, against $0.58 / $1.68 for DeepSeek-V3.2-Speciale.
- Gemini 2.5 Flash-Lite accepts more context: 1.05M tokens versus 128K.
- DeepSeek-V3.2-Speciale has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-V3.2-Speciale | Gemini 2.5 Flash-Lite | |
|---|---|---|
| Provider | DeepSeek | |
| Noometry Index | 39.7 | 37.0 |
| Released | 2025-12-01 | 2025-06-17 |
| Weights | Open | Proprietary |
| Context window | 128K | 1.05M |
| Max output | 128K | 66K |
| Input $ / M tokens | $0.58 | $0.10 |
| Output $ / M tokens | $1.68 | $0.40 |
| Results tracked | 3 | 33 |
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Category by category
Coding DeepSeek-V3.2-Speciale leads
DeepSeek-V3.2-Speciale: 40.4 (#140), Gemini 2.5 Flash-Lite: 38.5 (#173)
| Benchmark | DeepSeek-V3.2-Speciale | Gemini 2.5 Flash-Lite |
|---|---|---|
| WeirdML | 46.7% | 35.2% |
| LMArena Coding | — | 1373 |
| ALE-Bench | — | 325.9 |
Agentic & Tool Use Not comparable
DeepSeek-V3.2-Speciale: —, Gemini 2.5 Flash-Lite: 28.0 (#96)
| Benchmark | DeepSeek-V3.2-Speciale | Gemini 2.5 Flash-Lite |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 36.9% |
Reasoning DeepSeek-V3.2-Speciale leads
DeepSeek-V3.2-Speciale: 32.9 (#73), Gemini 2.5 Flash-Lite: 22.2 (#205)
| Benchmark | DeepSeek-V3.2-Speciale | Gemini 2.5 Flash-Lite |
|---|---|---|
| SimpleBench | 52.6% | — |
| Kagi LLM Benchmark | — | 40.5% |
| LMArena Hard Prompts | — | 1377 |
| DTBench | — | 62.8% |
| LMCA | — | 18.1% |
| Epoch Capabilities Index | — | 133.94 |
Math Not comparable
DeepSeek-V3.2-Speciale: —, Gemini 2.5 Flash-Lite: 38.0 (#144)
| Benchmark | DeepSeek-V3.2-Speciale | Gemini 2.5 Flash-Lite |
|---|---|---|
| Omni-MATH | — | 48% |
| LMArena Math | — | 1373 |
Knowledge Not comparable
DeepSeek-V3.2-Speciale: —, Gemini 2.5 Flash-Lite: 32.5 (#210)
| Benchmark | DeepSeek-V3.2-Speciale | Gemini 2.5 Flash-Lite |
|---|---|---|
| MMLU-Pro | — | 53.7% |
| Vectara Hallucination Rate | — | 3.3% |
| GPQA (HELM) | — | 30.9% |
| LMArena Expert | — | 1373 |
Multimodal Not comparable
DeepSeek-V3.2-Speciale: —, Gemini 2.5 Flash-Lite: 29.1 (#114)
| Benchmark | DeepSeek-V3.2-Speciale | Gemini 2.5 Flash-Lite |
|---|---|---|
| LMArena Vision | — | 1198 |
| VPCT | — | 30% |
Multilingual Not comparable
DeepSeek-V3.2-Speciale: —, Gemini 2.5 Flash-Lite: 49.3 (#134)
| Benchmark | DeepSeek-V3.2-Speciale | Gemini 2.5 Flash-Lite |
|---|---|---|
| LMArena Non-English | — | 1369 |
| LMArena Chinese | — | 1404 |
| LMArena French | — | 1388 |
| LMArena German | — | 1389 |
| LMArena Japanese | — | 1359 |
| LMArena Korean | — | 1360 |
| LMArena Russian | — | 1373 |
| LMArena Spanish | — | 1396 |
Instruction Following Not comparable
DeepSeek-V3.2-Speciale: —, Gemini 2.5 Flash-Lite: 70.0 (#168)
| Benchmark | DeepSeek-V3.2-Speciale | Gemini 2.5 Flash-Lite |
|---|---|---|
| IFEval | — | 81% |
| LMArena Instruction Following | — | 1367 |
Long Context Not comparable
DeepSeek-V3.2-Speciale: —, Gemini 2.5 Flash-Lite: 33.3 (#262)
| Benchmark | DeepSeek-V3.2-Speciale | Gemini 2.5 Flash-Lite |
|---|---|---|
| Fiction.LiveBench | — | 47.2% |
| LMArena Longer Query | — | 1373 |
Writing & Preference Gemini 2.5 Flash-Lite leads
DeepSeek-V3.2-Speciale: 46.0 (#222), Gemini 2.5 Flash-Lite: 56.8 (#135)
| Benchmark | DeepSeek-V3.2-Speciale | Gemini 2.5 Flash-Lite |
|---|---|---|
| LMArena Text | — | 1379 |
| LMArena Creative Writing | — | 1367 |
| EQ-Bench Creative Writing | 1276 | — |
| WildBench | — | 81.8% |
| LMArena Multi-Turn | — | 1366 |
Frequently asked questions
Is DeepSeek-V3.2-Speciale better than Gemini 2.5 Flash-Lite?
DeepSeek-V3.2-Speciale is the stronger model overall, scoring 39.7 to 37.0 on the Noometry Index. Gemini 2.5 Flash-Lite costs 4.9× less per token, which makes it the better buy when DeepSeek-V3.2-Speciale's lead doesn't matter for your workload.
Which is cheaper, DeepSeek-V3.2-Speciale or Gemini 2.5 Flash-Lite?
Gemini 2.5 Flash-Lite is cheaper. It lists at $0.10 per million input tokens and $0.40 per million output tokens; DeepSeek-V3.2-Speciale lists at $0.58 and $1.68.
Is DeepSeek-V3.2-Speciale or Gemini 2.5 Flash-Lite better for coding?
DeepSeek-V3.2-Speciale scores higher on coding benchmarks: 40.4 versus 38.5 in the Noometry coding category.
Which has the bigger context window?
Gemini 2.5 Flash-Lite does, with 1.05M tokens against 128K.
How many benchmarks do DeepSeek-V3.2-Speciale and Gemini 2.5 Flash-Lite share?
1 benchmark has published results for both models. DeepSeek-V3.2-Speciale has 3 scored results on Noometry and Gemini 2.5 Flash-Lite has 33.