Model comparison
DeepSeek-V3.2-Speciale vs Gemini 3.8 Flash
Gemini 3.8 Flash is the stronger model overall, scoring 61.8 to 39.7 on the Noometry Index. DeepSeek-V3.2-Speciale costs 1.8× less per token, which makes it the better buy when Gemini 3.8 Flash's lead doesn't matter for your workload.
Last verified . 2 shared benchmarks.
DeepSeek
GoogleSummary
- They share 2 benchmarks with published results for both. DeepSeek-V3.2-Speciale scores higher in 0 categories and Gemini 3.8 Flash in 3 categories; 3 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Gemini 3.8 Flash leads 76.9 to 32.9.
- The biggest single-benchmark swing is WeirdML: 46.7% for DeepSeek-V3.2-Speciale and 84.8% for Gemini 3.8 Flash.
- DeepSeek-V3.2-Speciale is cheaper at $0.58 / $1.68 per million input/output tokens, against $0.75 / $3.75 for Gemini 3.8 Flash.
- Gemini 3.8 Flash 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 3.8 Flash | |
|---|---|---|
| Provider | DeepSeek | |
| Noometry Index | 39.7 | 61.8 |
| Released | 2025-12-01 | 2026-09-02 |
| Weights | Open | Proprietary |
| Context window | 128K | 1.05M |
| Max output | 128K | 66K |
| Input $ / M tokens | $0.58 | $0.75 |
| Output $ / M tokens | $1.68 | $3.75 |
| Results tracked | 3 | 50 |
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Category by category
Coding Gemini 3.8 Flash leads
DeepSeek-V3.2-Speciale: 40.4 (#140), Gemini 3.8 Flash: 59.2 (#15)
| Benchmark | DeepSeek-V3.2-Speciale | Gemini 3.8 Flash |
|---|---|---|
| WeirdML | 46.7% | 84.8% |
| DeepSWE | — | 73.8% |
| FrontierCode | — | 41.2% |
| CursorBench | — | 39.6% |
| LMArena WebDev | — | 1584 |
| FrontierSWE | — | 19.6% |
| SciCode | — | 56.6% |
| LMArena Coding | — | 1510 |
| ALE-Bench | — | 1,270 |
Agentic & Tool Use Not comparable
DeepSeek-V3.2-Speciale: —, Gemini 3.8 Flash: 41.8 (#21)
| Benchmark | DeepSeek-V3.2-Speciale | Gemini 3.8 Flash |
|---|---|---|
| APEX-Agents | — | 64.3% |
| Remote Labor Index | — | 5.8% |
| GDP.pdf | — | 23.4% |
| Vending-Bench 2 | — | 5,094 |
Reasoning Gemini 3.8 Flash leads
DeepSeek-V3.2-Speciale: 32.9 (#73), Gemini 3.8 Flash: 76.9 (#5)
| Benchmark | DeepSeek-V3.2-Speciale | Gemini 3.8 Flash |
|---|---|---|
| ARC-AGI-2 | — | 89.2% |
| SimpleBench | 52.6% | — |
| NYT Connections (extended) | — | 97.4% |
| ARC-AGI-1 | — | 98.5% |
| CritPt | — | 18.3% |
| Chess Puzzles | — | 61% |
| LMArena Hard Prompts | — | 1508 |
| Mystery Game Puzzles | — | 47% |
| DTBench | — | 95.7% |
| LMCA | — | 52.9% |
| Surface Evolver Bench | — | 76.9% |
| Epoch Capabilities Index | — | 156.71 |
Math Not comparable
DeepSeek-V3.2-Speciale: —, Gemini 3.8 Flash: 65.3 (#28)
| Benchmark | DeepSeek-V3.2-Speciale | Gemini 3.8 Flash |
|---|---|---|
| FrontierMath (Tiers 1-3) | — | 68.4% |
| FrontierMath Tier 4 | — | 22% |
| OTIS Mock AIME 2024-2025 | — | 98.9% |
| ProofBench | — | 48% |
| LMArena Math | — | 1528 |
Knowledge Not comparable
DeepSeek-V3.2-Speciale: —, Gemini 3.8 Flash: 74.8 (#2)
| Benchmark | DeepSeek-V3.2-Speciale | Gemini 3.8 Flash |
|---|---|---|
| GPQA Diamond | — | 95.4% |
| Humanity's Last Exam | — | 44.5% |
| SimpleQA Verified | — | 69.7% |
| LMArena Expert | — | 1524 |
Multimodal Not comparable
DeepSeek-V3.2-Speciale: —, Gemini 3.8 Flash: 40.7 (#45)
| Benchmark | DeepSeek-V3.2-Speciale | Gemini 3.8 Flash |
|---|---|---|
| LMArena Vision | — | 1314 |
| Blueprint-Bench 2 | — | 38.6% |
| Furniture Assembly | — | 31.7% |
Multilingual Not comparable
DeepSeek-V3.2-Speciale: —, Gemini 3.8 Flash: 58.0 (#5)
| Benchmark | DeepSeek-V3.2-Speciale | Gemini 3.8 Flash |
|---|---|---|
| LMArena Non-English | — | 1491 |
| LMArena Chinese | — | 1554 |
| LMArena French | — | 1498 |
| LMArena German | — | 1493 |
| LMArena Japanese | — | 1502 |
| LMArena Korean | — | 1459 |
| LMArena Russian | — | 1515 |
| LMArena Spanish | — | 1485 |
Instruction Following Not comparable
DeepSeek-V3.2-Speciale: —, Gemini 3.8 Flash: 78.0 (#13)
| Benchmark | DeepSeek-V3.2-Speciale | Gemini 3.8 Flash |
|---|---|---|
| LMArena Instruction Following | — | 1490 |
Long Context Not comparable
DeepSeek-V3.2-Speciale: —, Gemini 3.8 Flash: 46.3 (#24)
| Benchmark | DeepSeek-V3.2-Speciale | Gemini 3.8 Flash |
|---|---|---|
| LMArena Longer Query | — | 1508 |
Writing & Preference Gemini 3.8 Flash leads
DeepSeek-V3.2-Speciale: 46.0 (#222), Gemini 3.8 Flash: 72.2 (#15)
| Benchmark | DeepSeek-V3.2-Speciale | Gemini 3.8 Flash |
|---|---|---|
| EQ-Bench Creative Writing | 1276 | 1748 |
| LMArena Text | — | 1499 |
| LMArena Creative Writing | — | 1492 |
| LMArena Multi-Turn | — | 1501 |
Frequently asked questions
Is DeepSeek-V3.2-Speciale better than Gemini 3.8 Flash?
Gemini 3.8 Flash is the stronger model overall, scoring 61.8 to 39.7 on the Noometry Index. DeepSeek-V3.2-Speciale costs 1.8× less per token, which makes it the better buy when Gemini 3.8 Flash's lead doesn't matter for your workload.
Which is cheaper, DeepSeek-V3.2-Speciale or Gemini 3.8 Flash?
DeepSeek-V3.2-Speciale is cheaper. It lists at $0.58 per million input tokens and $1.68 per million output tokens; Gemini 3.8 Flash lists at $0.75 and $3.75.
Is DeepSeek-V3.2-Speciale or Gemini 3.8 Flash better for coding?
Gemini 3.8 Flash scores higher on coding benchmarks: 59.2 versus 40.4 in the Noometry coding category.
Which has the bigger context window?
Gemini 3.8 Flash does, with 1.05M tokens against 128K.
How many benchmarks do DeepSeek-V3.2-Speciale and Gemini 3.8 Flash share?
2 benchmarks have published results for both models. DeepSeek-V3.2-Speciale has 3 scored results on Noometry and Gemini 3.8 Flash has 50.