DeepSeek V3.2 Exp vs Gemini 2.5 Pro Preview 06-05
Updated October 21, 2026. 6 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick Gemini 2.5 Pro Preview 06-05 for coding: it scores 83.1 on the Vibe Coding Index against 55.0 for DeepSeek V3.2 Exp, though on 1 benchmark against 2.
Pick DeepSeek V3.2 Exp when output tokens cost 24.4x less, or when the benchmark that matters is LiveCodeBench (Artificial Analysis run), where it leads.
- Shared results
- 6
- DeepSeek V3.2 Exp only
- 51
- Gemini 2.5 Pro Preview 06-05 only
- 8
- Like-for-like categories
- 4 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, four public coding benchmarks
Gemini 2.5 Pro Preview 06-05
Gemini 2.5 Pro Preview 06-05 scores 83.1 against 55.0.
2 and 1 benchmarks behind the scores
Intelligence
AA Intelligence Index
No call
Only DeepSeek V3.2 Exp has a AA Intelligence Index result, 16.6.
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 Pro Preview 06-05
Gemini 2.5 Pro Preview 06-05 takes 1.05M tokens against 164K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V3.2 Exp
DeepSeek V3.2 Exp $0.0005, Gemini 2.5 Pro Preview 06-05 $0.0063 per turn.
List prices
At a glance
- Coding index
- DeepSeek V3.2 Exp 55.0 from 2 benchmarks, Gemini 2.5 Pro Preview 06-05 83.1 from 1 benchmark.
- Widest category
- General, 8.4 points apart on average over 1 shared benchmark.
- Widest single gap
- Aider polyglot: DeepSeek V3.2 Exp 74.2% against Gemini 2.5 Pro Preview 06-05 83.1%, 8.9 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3.2 Exp $0.41 against Gemini 2.5 Pro Preview 06-05 $10.00 per million output tokens, 24.4x apart.
- Context window
- DeepSeek V3.2 Exp takes 164K tokens, Gemini 2.5 Pro Preview 06-05 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 Pro Preview 06-05 | Ties |
|---|---|---|---|---|
| Coding | 1 | 1 | ||
| Reasoning | 0 | 2 | ||
| Knowledge | 0 | 1 | ||
| General | 0 | 1 | ||
| All shared boards | 1 | 5 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index11
| Benchmark | DeepSeek V3.2 Exp | Gemini 2.5 Pro Preview 06-05 | Leader |
|---|---|---|---|
| Aider polyglot | 74.2% | 83.1% | Gemini 2.5 Pro Preview 06-05 |
| LMArena WebDev | 1272 | Not measured | Only DeepSeek V3.2 Exp measured |
| LiveCodeBench (Vals AI) | Not measured | 79.2% | Only Gemini 2.5 Pro Preview 06-05 measured |
| LiveCodeBench (Artificial Analysis run) | 78.9% | 77.8% | DeepSeek V3.2 Exp |
| Terminal-Bench Hard (Artificial Analysis run) | 31.1% | Not measured | Only DeepSeek V3.2 Exp measured |
| GSO-Bench | Not measured | 3.9% | Only Gemini 2.5 Pro Preview 06-05 measured |
| 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 28 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | DeepSeek V3.2 Exp | Gemini 2.5 Pro Preview 06-05 | Leader |
|---|---|---|---|
| AA Intelligence Index | 16.6 | Not measured | Only DeepSeek V3.2 Exp measured |
| Epoch Capabilities Index | 144.9 | Not measured | Only DeepSeek V3.2 Exp measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | DeepSeek V3.2 Exp | Gemini 2.5 Pro Preview 06-05 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 33.9% | Not measured | Only DeepSeek V3.2 Exp measured |
| METR time horizons | Not measured | 55.4% | Only Gemini 2.5 Pro Preview 06-05 measured |
| Remote Labor Index | Not measured | 0.8% | Only Gemini 2.5 Pro Preview 06-05 measured |
| DeepResearch Bench | Not measured | 42.8% | Only Gemini 2.5 Pro Preview 06-05 measured |
| TheAgentCompany | 42.9% | Not measured | Only DeepSeek V3.2 Exp measured |
| Neither model has a result on the other 34 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | DeepSeek V3.2 Exp | Gemini 2.5 Pro Preview 06-05 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 79.7% | 83.6% | Gemini 2.5 Pro Preview 06-05 |
| Humanity's Last Exam (Artificial Analysis run) | 14.9% | 19.9% | Gemini 2.5 Pro Preview 06-05 |
| GPQA Diamond (Epoch AI run) | Not measured | 84.8% | Only Gemini 2.5 Pro Preview 06-05 measured |
| SimpleBench | Not measured | 62.4% | Only Gemini 2.5 Pro Preview 06-05 measured |
| VPCT | Not measured | 46.4% | Only Gemini 2.5 Pro Preview 06-05 measured |
| 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 13 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | DeepSeek V3.2 Exp | Gemini 2.5 Pro Preview 06-05 | Leader |
|---|---|---|---|
| AA Math Index | 87.7 | Not measured | Only DeepSeek V3.2 Exp measured |
| AIME 2025 (Artificial Analysis run) | 87.7% | Not measured | Only DeepSeek V3.2 Exp measured |
| 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 9 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V3.2 Exp | Gemini 2.5 Pro Preview 06-05 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 85.0% | 85.8% | Gemini 2.5 Pro Preview 06-05 |
| 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 12 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 Pro Preview 06-05 | Leader |
|---|---|---|---|
| Design Arena: websites | 1179 | Not measured | Only DeepSeek V3.2 Exp measured |
| Design Arena: UI components | 1166 | Not measured | Only DeepSeek V3.2 Exp measured |
| Design Arena: game dev | 1145 | 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 | 1031 | 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 index01
| Benchmark | DeepSeek V3.2 Exp | Gemini 2.5 Pro Preview 06-05 | Leader |
|---|---|---|---|
| IFBench | 54.1% | Not measured | Only DeepSeek V3.2 Exp measured |
| Long context reasoning (AA-LCR) | 72.3% | Not measured | Only DeepSeek V3.2 Exp measured |
| DTBench | 87.7% | Not measured | Only DeepSeek V3.2 Exp measured |
| LMCA | 29.1% | Not measured | Only DeepSeek V3.2 Exp measured |
| Fiction.LiveBench | 83.3% | 91.7% | Gemini 2.5 Pro Preview 06-05 |
| 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 22 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 Pro Preview 06-05 | 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
DeepSeek V3.2 Exp costs less
Repository review
50K input + 3K output tokens
DeepSeek V3.2 Exp costs less
Agent loop
200K input + 10K output tokens
DeepSeek V3.2 Exp 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 Pro Preview 06-05 |
|---|---|---|
| Maker | DeepSeek | |
| Weights | Open weights | No public weights listed |
| Released | September 29, 2025 | June 5, 2025 |
| Context window | 164K | 1.05M |
| Input $/Mtok | $0.27 | $1.25 |
| Output $/Mtok | $0.41 | $10.00 |
| Coding index | 55.0 | 83.1 |
| Independent measurements behind it | 2 | 1 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V3.2 Exp better than Gemini 2.5 Pro Preview 06-05 for coding?
Pick Gemini 2.5 Pro Preview 06-05 for coding: it scores 83.1 on the Vibe Coding Index against 55.0 for DeepSeek V3.2 Exp, though on 1 benchmark against 2. Pick DeepSeek V3.2 Exp when output tokens cost 24.4x less, or when the benchmark that matters is LiveCodeBench (Artificial Analysis run), where it leads.
Which is cheaper, DeepSeek V3.2 Exp or Gemini 2.5 Pro Preview 06-05?
DeepSeek V3.2 Exp costs $0.27 per million input tokens and $0.41 per million output tokens; Gemini 2.5 Pro Preview 06-05 costs $1.25 and $10.00. Cost per solved task is known for neither of them.
Which has the bigger context window, DeepSeek V3.2 Exp or Gemini 2.5 Pro Preview 06-05?
Gemini 2.5 Pro Preview 06-05, with 1.05M tokens against 164K.
How many benchmarks is the DeepSeek V3.2 Exp vs Gemini 2.5 Pro Preview 06-05 comparison based on?
6 benchmarks have a result for both models as of October 21, 2026. DeepSeek V3.2 Exp's coding index rests on 2 independent benchmarks and Gemini 2.5 Pro Preview 06-05's on 1. Where only one model has a result, the page says so instead of naming a winner.