DeepSeek V3.2 Exp vs Gemini 2.5 Flash Lite
Updated October 21, 2026. 14 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Neither model has a coding score yet, so the two cannot be ranked on coding.
Compare them on price, context and the boards below.
- Shared results
- 14
- DeepSeek V3.2 Exp only
- 43
- Gemini 2.5 Flash Lite only
- 14
- Like-for-like categories
- 7 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
DeepSeek V3.2 Exp
DeepSeek V3.2 Exp leads, 16.6 against 10.4.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only Gemini 2.5 Flash Lite has a GPQA Diamond result, 70.2%.
Directional only
Math
AIME
No call
Only Gemini 2.5 Flash Lite has a AIME result, 42.1%.
Directional only
Knowledge
MMLU Pro
No call
Only Gemini 2.5 Flash Lite has a MMLU Pro result, 79.1%.
Directional only
Web design
Design Arena: websites
No call
Only DeepSeek V3.2 Exp has a Design Arena: websites result, 1179.
Directional only
Long documents
Listed context window
Gemini 2.5 Flash Lite
Gemini 2.5 Flash Lite takes 1.05M tokens against 164K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Gemini 2.5 Flash Lite
DeepSeek V3.2 Exp $0.0005, Gemini 2.5 Flash Lite $0.0003 per turn.
List prices
At a glance
- Coding index
- DeepSeek V3.2 Exp not benchmarked, Gemini 2.5 Flash Lite not benchmarked.
- Widest category
- Math, 34.4 points apart on average over 2 shared benchmarks.
- Widest single gap
- AA Math Index: DeepSeek V3.2 Exp 87.7 against Gemini 2.5 Flash Lite 53.3, 34.4 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3.2 Exp $0.41 against Gemini 2.5 Flash Lite $0.40 per million output tokens, 1.0x apart.
- Context window
- DeepSeek V3.2 Exp takes 164K tokens, Gemini 2.5 Flash Lite 1.05M.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
| Category | DeepSeek V3.2 Exp | Bars | Gemini 2.5 Flash Lite | Ties |
|---|---|---|---|---|
| Coding | 2 | 0 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 2 | 0 | ||
| Math | 2 | 0 | ||
| Knowledge | 1 | 0 | ||
| General | 4 | 0 | ||
| All shared boards | 14 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index20
| Benchmark | DeepSeek V3.2 Exp | Gemini 2.5 Flash Lite | Leader |
|---|---|---|---|
| Aider polyglot | 74.2% | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena WebDev | 1272 | Not measured | Only DeepSeek V3.2 Exp measured |
| LiveCodeBench (Vals AI) | Not measured | 71.4% | Only Gemini 2.5 Flash Lite measured |
| LiveCodeBench (Artificial Analysis run) | 78.9% | 59.3% | DeepSeek V3.2 Exp |
| Terminal-Bench Hard (Artificial Analysis run) | 31.1% | 4.5% | DeepSeek V3.2 Exp |
| WeirdML | 39.5% | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: coding | 1438 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: software and IT | 1438 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena WebDev: HTML | 1281 | Not measured | Only DeepSeek V3.2 Exp measured |
| Neither model has a result on the other 29 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | DeepSeek V3.2 Exp | Gemini 2.5 Flash Lite | Leader |
|---|---|---|---|
| AA Intelligence Index | 16.6 | 10.4 | DeepSeek V3.2 Exp |
| Epoch Capabilities Index | 144.9 | 133.8 | DeepSeek V3.2 Exp |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V3.2 Exp | Gemini 2.5 Flash Lite | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 33.9% | 19.0% | DeepSeek V3.2 Exp |
| TheAgentCompany | 42.9% | Not measured | Only DeepSeek V3.2 Exp measured |
| Neither model has a result on the other 37 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | DeepSeek V3.2 Exp | Gemini 2.5 Flash Lite | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 70.2% | Only Gemini 2.5 Flash Lite measured |
| MMMU Pro (Vals AI) | Not measured | 75.4% | Only Gemini 2.5 Flash Lite measured |
| GPQA Diamond (Artificial Analysis run) | 79.7% | 62.5% | DeepSeek V3.2 Exp |
| Humanity's Last Exam (Artificial Analysis run) | 14.9% | 6.8% | DeepSeek V3.2 Exp |
| LMArena Text: hard prompts | 1429 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: expert | 1422 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: hard prompts in English | 1444 | Not measured | Only DeepSeek V3.2 Exp measured |
| Neither model has a result on the other 14 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | DeepSeek V3.2 Exp | Gemini 2.5 Flash Lite | Leader |
|---|---|---|---|
| AIME | Not measured | 42.1% | Only Gemini 2.5 Flash Lite measured |
| MGSM (Vals AI) | Not measured | 89.5% | Only Gemini 2.5 Flash Lite measured |
| AA Math Index | 87.7 | 53.3 | DeepSeek V3.2 Exp |
| AIME 2025 (Artificial Analysis run) | 87.7% | 53.3% | DeepSeek V3.2 Exp |
| LMArena Text: math | 1422 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: mathematical work | 1433 | Not measured | Only DeepSeek V3.2 Exp measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V3.2 Exp | Gemini 2.5 Flash Lite | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 79.1% | Only Gemini 2.5 Flash Lite measured |
| LegalBench (Vals AI) | Not measured | 82.0% | Only Gemini 2.5 Flash Lite measured |
| CorpFin (Vals AI) | Not measured | 57.6% | Only Gemini 2.5 Flash Lite measured |
| TaxEval (Vals AI) | Not measured | 66.2% | Only Gemini 2.5 Flash Lite measured |
| MortgageTax (Vals AI) | Not measured | 57.6% | Only Gemini 2.5 Flash Lite measured |
| MedQA (Vals AI) | Not measured | 88.9% | Only Gemini 2.5 Flash Lite measured |
| MedCode (Vals AI) | Not measured | 34.2% | Only Gemini 2.5 Flash Lite measured |
| MMLU Pro (Artificial Analysis run) | 85.0% | 75.9% | DeepSeek V3.2 Exp |
| LMArena Text: science | 1443 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: medicine and healthcare | 1468 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: legal and government | 1419 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: business and finance | 1412 | Not measured | Only DeepSeek V3.2 Exp measured |
| Neither model has a result on the other 5 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | DeepSeek V3.2 Exp | Gemini 2.5 Flash Lite | Leader |
|---|---|---|---|
| Design Arena: websites | 1179 | Not measured | Only DeepSeek V3.2 Exp measured |
| Design Arena: UI components | 1167 | Not measured | Only DeepSeek V3.2 Exp measured |
| Design Arena: game dev | 1144 | Not measured | Only DeepSeek V3.2 Exp measured |
| Design Arena: data viz | 1152 | Not measured | Only DeepSeek V3.2 Exp measured |
| Design Arena: 3D | 1148 | Not measured | Only DeepSeek V3.2 Exp measured |
| Design Arena: all code categories | 1171 | Not measured | Only DeepSeek V3.2 Exp measured |
| Design Arena: SVG | 1032 | Not measured | Only DeepSeek V3.2 Exp measured |
| Neither model has a result on the other 2 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | DeepSeek V3.2 Exp | Gemini 2.5 Flash Lite | Leader |
|---|---|---|---|
| SAGE (Vals AI) | Not measured | 30.8% | Only Gemini 2.5 Flash Lite measured |
| MedScribe (Vals AI) | Not measured | 75.8% | Only Gemini 2.5 Flash Lite measured |
| IFBench | 54.1% | 49.9% | DeepSeek V3.2 Exp |
| Long context reasoning (AA-LCR) | 72.3% | 55.7% | DeepSeek V3.2 Exp |
| DTBench | 87.7% | 62.8% | DeepSeek V3.2 Exp |
| LMCA | 29.1% | 18.1% | DeepSeek V3.2 Exp |
| Fiction.LiveBench | 83.3% | Not measured | Only DeepSeek V3.2 Exp measured |
| CL-bench | 13.2% | Not measured | Only DeepSeek V3.2 Exp measured |
| CL-bench Life | 9.5% | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text | 1425 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: instruction following | 1401 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: creative writing | 1404 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: English | 1441 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: non-English | 1404 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: Chinese | 1461 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: German | 1440 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: Japanese | 1331 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: Korean | 1372 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: French | 1434 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: Spanish | 1441 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: Russian | 1412 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: Polish | 1435 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: multi-turn | 1422 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: longer queries | 1418 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: writing and language | 1407 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: entertainment and media | 1407 | Not measured | Only DeepSeek V3.2 Exp measured |
| Neither model has a result on the other 20 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3.2 Exp | Gemini 2.5 Flash Lite | Leader |
|---|---|---|---|
| Neither model has a result on 5 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
Gemini 2.5 Flash Lite costs less
Repository review
50K input + 3K output tokens
Gemini 2.5 Flash Lite costs less
Agent loop
200K input + 10K output tokens
Gemini 2.5 Flash Lite costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | DeepSeek V3.2 Exp | Google Gemini 2.5 Flash Lite |
|---|---|---|
| Maker | DeepSeek | |
| Weights | Open weights | No public weights listed |
| Released | September 29, 2025 | July 22, 2025 |
| Context window | 164K | 1.05M |
| Input $/Mtok | $0.27 | $0.10 |
| Output $/Mtok | $0.41 | $0.40 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 1 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V3.2 Exp better than Gemini 2.5 Flash Lite for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Compare them on price, context and the boards below.
Which is cheaper, DeepSeek V3.2 Exp or Gemini 2.5 Flash Lite?
DeepSeek V3.2 Exp costs $0.27 per million input tokens and $0.41 per million output tokens; Gemini 2.5 Flash Lite costs $0.10 and $0.40. Cost per solved task is known for neither of them.
Which has the bigger context window, DeepSeek V3.2 Exp or Gemini 2.5 Flash Lite?
Gemini 2.5 Flash Lite, with 1.05M tokens against 164K.
How many benchmarks is the DeepSeek V3.2 Exp vs Gemini 2.5 Flash Lite comparison based on?
14 benchmarks have a result for both models as of October 21, 2026. DeepSeek V3.2 Exp's coding index rests on 1 independent benchmark and Gemini 2.5 Flash Lite's on 0. Where only one model has a result, the page says so instead of naming a winner.