DeepSeek V3.2 Exp vs Kimi K2 0905
DeepSeek V3.2 Exp leads Kimi K2 0905 on the Artificial Analysis Intelligence Index, 16.6 to 15.3, and DeepSeek V3.2 Exp costs 6.1x less per output token ($0.41 vs $2.50 per million). Updated October 21, 2026; 16 benchmarks have a result for both.
DeepSeek V3.2 Exp or Kimi K2 0905: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick DeepSeek V3.2 Exp for output tokens 6.1x cheaper ($0.41 vs $2.50 per million) and work measured by τ²-Bench Telecom, where it leads.
- Shared results
- 16
- DeepSeek V3.2 Exp only
- 43
- Kimi K2 0905 only
- 1
- Like-for-like categories
- 8 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
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 15.3.
Same board, both measured
Web design
Design Arena: websites
DeepSeek V3.2 Exp
DeepSeek V3.2 Exp leads, 1179 against 1089.
Same board, both measured
Long documents
Listed context window
Kimi K2 0905
Kimi K2 0905 takes 262K tokens against 164K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V3.2 Exp
DeepSeek V3.2 Exp $0.0005, Kimi K2 0905 $0.0019 per turn.
List prices
At a glance
- Coding index
- DeepSeek V3.2 Exp not benchmarked, Kimi K2 0905 not benchmarked.
- Widest category
- Agentic work, 39.5 points apart on average over 1 shared benchmark.
- Widest single gap
- τ²-Bench Telecom: DeepSeek V3.2 Exp 33.9% against Kimi K2 0905 73.4%, 39.5 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3.2 Exp $0.41 against Kimi K2 0905 $2.50 per million output tokens, 6.1x apart.
- Context window
- DeepSeek V3.2 Exp takes 164K tokens, Kimi K2 0905 262K.
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 | Kimi K2 0905 | Ties |
|---|---|---|---|---|
| Coding | 3 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 2 | 0 | 1 tie | |
| Math | 2 | 0 | ||
| Knowledge | 1 | 0 | ||
| Web design | 2 | 0 | ||
| General | 3 | 0 | ||
| All shared boards | 14 | 1 | 1 tie |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index30
| Benchmark | DeepSeek V3.2 Exp | Kimi K2 0905 | 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 (Artificial Analysis run) | 78.9% | 61.0% | DeepSeek V3.2 Exp |
| Terminal-Bench Hard (Artificial Analysis run) | 31.1% | 23.5% | DeepSeek V3.2 Exp |
| WeirdML | 39.5% | 36.7% | DeepSeek V3.2 Exp |
| ALE-Bench | Not measured | 267.1 | Only Kimi K2 0905 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 30 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V3.2 Exp | Kimi K2 0905 | Leader |
|---|---|---|---|
| AA Intelligence Index | 16.6 | 15.3 | DeepSeek V3.2 Exp |
| Epoch Capabilities Index | 144.9 | Not measured | Only DeepSeek V3.2 Exp measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V3.2 Exp | Kimi K2 0905 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 33.9% | 73.4% | Kimi K2 0905 |
| TheAgentCompany | 42.9% | Not measured | Only DeepSeek V3.2 Exp measured |
| Berkeley Function Calling Leaderboard (BFCL) | 56.7% | Not measured | Only DeepSeek V3.2 Exp measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | DeepSeek V3.2 Exp | Kimi K2 0905 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 79.7% | 76.7% | DeepSeek V3.2 Exp |
| Humanity's Last Exam (Artificial Analysis run) | 14.9% | 6.4% | 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 |
| Kagi LLM Benchmark | 52.2% | 52.2% | Tie |
| Neither model has a result on the other 16 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | DeepSeek V3.2 Exp | Kimi K2 0905 | Leader |
|---|---|---|---|
| AA Math Index | 87.7 | 57.3 | DeepSeek V3.2 Exp |
| AIME 2025 (Artificial Analysis run) | 87.7% | 57.3% | 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 9 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V3.2 Exp | Kimi K2 0905 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 85.0% | 81.9% | 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 13 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | DeepSeek V3.2 Exp | Kimi K2 0905 | Leader |
|---|---|---|---|
| Design Arena: websites | 1179 | 1089 | DeepSeek V3.2 Exp |
| 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 | 1098 | DeepSeek V3.2 Exp |
| 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 index30
| Benchmark | DeepSeek V3.2 Exp | Kimi K2 0905 | Leader |
|---|---|---|---|
| IFBench | 54.1% | 41.7% | DeepSeek V3.2 Exp |
| Long context reasoning (AA-LCR) | 72.3% | 53.0% | DeepSeek V3.2 Exp |
| 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% | 66.7% | DeepSeek V3.2 Exp |
| 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 | Kimi K2 0905 | 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 | MoonshotAI Kimi K2 0905 |
|---|---|---|
| Maker | DeepSeek | Moonshot AI |
| Weights | Open weights | Open weights |
| Released | September 29, 2025 | September 4, 2025 |
| Context window | 164K | 262K |
| Input $/Mtok | $0.27 | $0.60 |
| Output $/Mtok | $0.41 | $2.50 |
| 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 Kimi K2 0905 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick DeepSeek V3.2 Exp for output tokens 6.1x cheaper ($0.41 vs $2.50 per million) and work measured by τ²-Bench Telecom, where it leads.
Which is cheaper, DeepSeek V3.2 Exp or Kimi K2 0905?
DeepSeek V3.2 Exp costs $0.27 per million input tokens and $0.41 per million output tokens; Kimi K2 0905 costs $0.60 and $2.50. Cost per solved task is known for neither of them.
Which has the bigger context window, DeepSeek V3.2 Exp or Kimi K2 0905?
Kimi K2 0905, with 262K tokens against 164K.
How many benchmarks is the DeepSeek V3.2 Exp vs Kimi K2 0905 comparison based on?
16 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 Kimi K2 0905's on 0. Where only one model has a result, the page says so instead of naming a winner.