DeepSeek V3.2 vs Gemini 2.5 Flash Lite
DeepSeek V3.2 leads Gemini 2.5 Flash Lite on the Artificial Analysis Intelligence Index, 21.5 to 10.4, and Gemini 2.5 Flash Lite costs 2.2x less per output token ($0.40 vs $0.88 per million). Updated October 21, 2026; 23 benchmarks have a result for both.
DeepSeek V3.2 or Gemini 2.5 Flash Lite: which to pick
Only DeepSeek V3.2 has a coding score, 30.0 on the Vibe Coding Index; Gemini 2.5 Flash Lite has none, so the two cannot be ranked on coding.
Pick Gemini 2.5 Flash Lite for output tokens 2.2x cheaper ($0.40 vs $0.88 per million) and a larger context window (1.05M against 164K tokens).
- Shared results
- 23
- DeepSeek V3.2 only
- 52
- Gemini 2.5 Flash Lite only
- 7
- Like-for-like categories
- 7 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
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
Only DeepSeek V3.2 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
DeepSeek V3.2
DeepSeek V3.2 leads, 21.5 against 10.4.
Same board, both measured
Reasoning
GPQA Diamond
DeepSeek V3.2
DeepSeek V3.2 leads, 80.3% against 70.2%.
Same board, both measured
Math
AIME
DeepSeek V3.2
DeepSeek V3.2 leads, 84.6% against 42.1%.
Same board, both measured
Knowledge
MMLU Pro
DeepSeek V3.2
DeepSeek V3.2 leads, 84.9% against 79.1%.
Same board, both measured
Web design
Design Arena: websites
No call
Only DeepSeek V3.2 has a Design Arena: websites result, 1175.
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 $0.0007, Gemini 2.5 Flash Lite $0.0003 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only DeepSeek V3.2 has the number, $0.99.
Directional only
At a glance
- Coding index
- DeepSeek V3.2 30.0 from 3 benchmarks, Gemini 2.5 Flash Lite not benchmarked.
- Widest category
- Agentic work, 71.6 points apart on average over 1 shared benchmark.
- Widest single gap
- τ²-Bench Telecom: DeepSeek V3.2 90.6% against Gemini 2.5 Flash Lite 19.0%, 71.6 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3.2 $0.88 against Gemini 2.5 Flash Lite $0.40 per million output tokens, 2.2x apart.
- Context window
- DeepSeek V3.2 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 | Bars | Gemini 2.5 Flash Lite | Ties |
|---|---|---|---|---|
| Coding | 3 | 0 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 3 | 0 | ||
| Math | 3 | 1 | ||
| Knowledge | 4 | 2 | ||
| General | 4 | 0 | ||
| All shared boards | 20 | 3 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index30
| Benchmark | DeepSeek V3.2 | Gemini 2.5 Flash Lite | Leader |
|---|---|---|---|
| SWE-bench Verified | 70.0% | Not measured | Only DeepSeek V3.2 measured |
| SWE-bench Multilingual | 59.0% | Not measured | Only DeepSeek V3.2 measured |
| LMArena WebDev | 1362 | Not measured | Only DeepSeek V3.2 measured |
| AA Coding Index | 44.2 | Not measured | Only DeepSeek V3.2 measured |
| LiveCodeBench (Vals AI) | 80.7% | 71.4% | DeepSeek V3.2 |
| SWE-bench Verified (Vals AI) | 67.6% | Not measured | Only DeepSeek V3.2 measured |
| Vibe Code Bench (Vals AI) | 5.1% | Not measured | Only DeepSeek V3.2 measured |
| LiveCodeBench (Artificial Analysis run) | 86.2% | 59.3% | DeepSeek V3.2 |
| Terminal-Bench Hard (Artificial Analysis run) | 35.6% | 4.5% | DeepSeek V3.2 |
| Terminal-Bench | 39.6% | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: coding | 1453 | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: software and IT | 1449 | Not measured | Only DeepSeek V3.2 measured |
| LMArena WebDev: React | 1350 | Not measured | Only DeepSeek V3.2 measured |
| LMArena WebDev: HTML | 1371 | Not measured | Only DeepSeek V3.2 measured |
| Neither model has a result on the other 25 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | DeepSeek V3.2 | Gemini 2.5 Flash Lite | Leader |
|---|---|---|---|
| AA Intelligence Index | 21.5 | 10.4 | DeepSeek V3.2 |
| Epoch Capabilities Index | 146.1 | 133.8 | DeepSeek V3.2 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V3.2 | Gemini 2.5 Flash Lite | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 90.6% | 19.0% | DeepSeek V3.2 |
| APEX-Agents | 21.3% | Not measured | Only DeepSeek V3.2 measured |
| Vending-Bench 2 | $1,034 | Not measured | Only DeepSeek V3.2 measured |
| Berkeley Function Calling Leaderboard (BFCL) | Not measured | 36.9% | Only Gemini 2.5 Flash Lite measured |
| Neither model has a result on the other 37 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | DeepSeek V3.2 | Gemini 2.5 Flash Lite | Leader |
|---|---|---|---|
| GPQA Diamond | 80.3% | 70.2% | DeepSeek V3.2 |
| MMMU Pro (Vals AI) | Not measured | 75.4% | Only Gemini 2.5 Flash Lite measured |
| GPQA Diamond (Artificial Analysis run) | 84.0% | 62.5% | DeepSeek V3.2 |
| Humanity's Last Exam (Artificial Analysis run) | 24.6% | 6.8% | DeepSeek V3.2 |
| ARC-AGI | 57.0% | Not measured | Only DeepSeek V3.2 measured |
| ARC-AGI-2 | 4.0% | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: hard prompts | 1434 | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: expert | 1436 | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: hard prompts in English | 1446 | Not measured | Only DeepSeek V3.2 measured |
| Kagi LLM Benchmark | Not measured | 40.5% | Only Gemini 2.5 Flash Lite measured |
| Neither model has a result on the other 12 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index31
| Benchmark | DeepSeek V3.2 | Gemini 2.5 Flash Lite | Leader |
|---|---|---|---|
| AIME | 84.6% | 42.1% | DeepSeek V3.2 |
| MGSM (Vals AI) | 86.0% | 89.5% | Gemini 2.5 Flash Lite |
| AA Math Index | 92.0 | 53.3 | DeepSeek V3.2 |
| AIME 2025 (Artificial Analysis run) | 92.0% | 53.3% | DeepSeek V3.2 |
| LMArena Text: math | 1435 | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: mathematical work | 1440 | Not measured | Only DeepSeek V3.2 measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index42
| Benchmark | DeepSeek V3.2 | Gemini 2.5 Flash Lite | Leader |
|---|---|---|---|
| MMLU Pro | 84.9% | 79.1% | DeepSeek V3.2 |
| LegalBench (Vals AI) | 76.1% | 82.0% | Gemini 2.5 Flash Lite |
| CaseLaw (Vals AI) | 55.4% | Not measured | Only DeepSeek V3.2 measured |
| CorpFin (Vals AI) | 51.0% | 57.6% | Gemini 2.5 Flash Lite |
| TaxEval (Vals AI) | 68.2% | 66.2% | DeepSeek V3.2 |
| MortgageTax (Vals AI) | Not measured | 57.6% | Only Gemini 2.5 Flash Lite measured |
| MedQA (Vals AI) | 93.9% | 88.9% | DeepSeek V3.2 |
| MedCode (Vals AI) | Not measured | 34.2% | Only Gemini 2.5 Flash Lite measured |
| MMLU Pro (Artificial Analysis run) | 86.2% | 75.9% | DeepSeek V3.2 |
| LMArena Text: science | 1446 | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: medicine and healthcare | 1440 | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: legal and government | 1425 | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: business and finance | 1410 | Not measured | Only DeepSeek V3.2 measured |
| Neither model has a result on the other 5 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | DeepSeek V3.2 | Gemini 2.5 Flash Lite | Leader |
|---|---|---|---|
| Design Arena: websites | 1175 | Not measured | Only DeepSeek V3.2 measured |
| Design Arena: UI components | 1149 | Not measured | Only DeepSeek V3.2 measured |
| Design Arena: game dev | 1132 | Not measured | Only DeepSeek V3.2 measured |
| Design Arena: data viz | 1158 | Not measured | Only DeepSeek V3.2 measured |
| Design Arena: 3D | 1133 | Not measured | Only DeepSeek V3.2 measured |
| Design Arena: all code categories | 1164 | Not measured | Only DeepSeek V3.2 measured |
| Design Arena: SVG | 1032 | Not measured | Only DeepSeek V3.2 measured |
| Design Arena: ASCII art | 1083 | Not measured | Only DeepSeek V3.2 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | DeepSeek V3.2 | 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 | 60.7% | 49.9% | DeepSeek V3.2 |
| Long context reasoning (AA-LCR) | 73.3% | 55.7% | DeepSeek V3.2 |
| DTBench | 85.6% | 62.8% | DeepSeek V3.2 |
| LMCA | 28.8% | 18.1% | DeepSeek V3.2 |
| CL-bench | 12.4% | Not measured | Only DeepSeek V3.2 measured |
| CL-bench Life | 7.4% | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text | 1425 | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: instruction following | 1413 | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: creative writing | 1400 | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: English | 1439 | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: non-English | 1409 | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: Chinese | 1455 | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: German | 1413 | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: Japanese | 1374 | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: Korean | 1370 | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: French | 1429 | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: Spanish | 1424 | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: Russian | 1424 | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: Polish | 1419 | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: multi-turn | 1427 | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: longer queries | 1428 | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: writing and language | 1407 | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: entertainment and media | 1395 | Not measured | Only DeepSeek V3.2 measured |
| Neither model has a result on the other 21 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3.2 | 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 | Google Gemini 2.5 Flash Lite |
|---|---|---|
| Maker | DeepSeek | |
| Weights | Open weights | No public weights listed |
| Released | December 1, 2025 | July 22, 2025 |
| Context window | 164K | 1.05M |
| Input $/Mtok | $0.29 | $0.10 |
| Output $/Mtok | $0.88 | $0.40 |
| Coding index | 30.0 | Not benchmarked |
| Independent measurements behind it | 3 | 0 |
| Cost per solved task | $0.99 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V3.2 better than Gemini 2.5 Flash Lite for coding?
Only DeepSeek V3.2 has a coding score, 30.0 on the Vibe Coding Index; Gemini 2.5 Flash Lite has none, so the two cannot be ranked on coding. Pick Gemini 2.5 Flash Lite for output tokens 2.2x cheaper ($0.40 vs $0.88 per million) and a larger context window (1.05M against 164K tokens).
Which is cheaper, DeepSeek V3.2 or Gemini 2.5 Flash Lite?
DeepSeek V3.2 costs $0.29 per million input tokens and $0.88 per million output tokens; Gemini 2.5 Flash Lite costs $0.10 and $0.40. Cost per solved task is known for DeepSeek V3.2 only.
Which has the bigger context window, DeepSeek V3.2 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 vs Gemini 2.5 Flash Lite comparison based on?
23 benchmarks have a result for both models as of October 21, 2026. DeepSeek V3.2's coding index rests on 3 independent benchmarks 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.