DeepSeek V3 vs Kimi K2 0905
DeepSeek V3 costs 2.6x less per output token than Kimi K2 0905 ($0.9594 vs $2.50 per million). Updated October 21, 2026; 3 benchmarks have a result for both.
DeepSeek V3 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 for output tokens 2.6x cheaper ($0.9594 vs $2.50 per million).
- Shared results
- 3
- DeepSeek V3 only
- 11
- Kimi K2 0905 only
- 14
- Like-for-like categories
- 2 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
No call
Only Kimi K2 0905 has a AA Intelligence Index result, 15.3.
Directional only
Web design
Design Arena: websites
DeepSeek V3
DeepSeek V3 leads, 1120 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
DeepSeek V3 $0.0008, Kimi K2 0905 $0.0019 per turn.
List prices
At a glance
- Coding index
- DeepSeek V3 not benchmarked, Kimi K2 0905 not benchmarked.
- Widest category
- Web design, 2.6 points apart on average over 2 shared benchmarks.
- Widest single gap
- Design Arena: websites: DeepSeek V3 1120 against Kimi K2 0905 1089, 3.9 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3 $0.9594 against Kimi K2 0905 $2.50 per million output tokens, 2.6x apart.
- Context window
- DeepSeek V3 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 | Bars | Kimi K2 0905 | Ties |
|---|---|---|---|---|
| Reasoning | 1 | 0 | ||
| Web design | 2 | 0 | ||
| All shared boards | 3 | 0 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index6 boards, none shared
| Benchmark | DeepSeek V3 | Kimi K2 0905 | Leader |
|---|---|---|---|
| Aider polyglot | 70.2% | Not measured | Only DeepSeek V3 measured |
| SWE-bench Lite | 30.7% | Not measured | Only DeepSeek V3 measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 61.0% | Only Kimi K2 0905 measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 23.5% | Only Kimi K2 0905 measured |
| WeirdML | Not measured | 36.7% | Only Kimi K2 0905 measured |
| ALE-Bench | Not measured | 267.1 | Only Kimi K2 0905 measured |
| Neither model has a result on the other 33 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | DeepSeek V3 | Kimi K2 0905 | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 15.3 | Only Kimi K2 0905 measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | DeepSeek V3 | Kimi K2 0905 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 73.4% | Only Kimi K2 0905 measured |
| Neither model has a result on the other 40 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V3 | Kimi K2 0905 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 76.7% | Only Kimi K2 0905 measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 6.4% | Only Kimi K2 0905 measured |
| GPQA Diamond (Epoch AI run) | 71.2% | Not measured | Only DeepSeek V3 measured |
| Kagi LLM Benchmark | 52.3% | 52.2% | DeepSeek V3 |
| Neither model has a result on the other 18 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | DeepSeek V3 | Kimi K2 0905 | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 57.3 | Only Kimi K2 0905 measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 57.3% | Only Kimi K2 0905 measured |
| OTIS Mock AIME | 48.9% | Not measured | Only DeepSeek V3 measured |
| Neither model has a result on the other 10 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | DeepSeek V3 | Kimi K2 0905 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 81.9% | Only Kimi K2 0905 measured |
| Neither model has a result on the other 17 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | DeepSeek V3 | Kimi K2 0905 | Leader |
|---|---|---|---|
| Design Arena: websites | 1120 | 1089 | DeepSeek V3 |
| Design Arena: UI components | 1093 | Not measured | Only DeepSeek V3 measured |
| Design Arena: game dev | 1053 | Not measured | Only DeepSeek V3 measured |
| Design Arena: data viz | 1089 | Not measured | Only DeepSeek V3 measured |
| Design Arena: 3D | 1087 | Not measured | Only DeepSeek V3 measured |
| Design Arena: all code categories | 1108 | 1098 | DeepSeek V3 |
| Design Arena: SVG | 976 | Not measured | Only DeepSeek V3 measured |
| Neither model has a result on the other 2 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | DeepSeek V3 | Kimi K2 0905 | Leader |
|---|---|---|---|
| IFBench | Not measured | 41.7% | Only Kimi K2 0905 measured |
| Long context reasoning (AA-LCR) | Not measured | 53.0% | Only Kimi K2 0905 measured |
| DTBench | 62.7% | Not measured | Only DeepSeek V3 measured |
| LMCA | 15.2% | Not measured | Only DeepSeek V3 measured |
| Fiction.LiveBench | Not measured | 66.7% | Only Kimi K2 0905 measured |
| Neither model has a result on the other 41 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3 | 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 costs less
Repository review
50K input + 3K output tokens
DeepSeek V3 costs less
Agent loop
200K input + 10K output tokens
DeepSeek V3 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 | MoonshotAI Kimi K2 0905 |
|---|---|---|
| Maker | DeepSeek | Moonshot AI |
| Weights | Open weights | Open weights |
| Released | December 26, 2024 | September 4, 2025 |
| Context window | 164K | 262K |
| Input $/Mtok | $0.2887 | $0.60 |
| Output $/Mtok | $0.9594 | $2.50 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V3 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 for output tokens 2.6x cheaper ($0.9594 vs $2.50 per million).
Which is cheaper, DeepSeek V3 or Kimi K2 0905?
DeepSeek V3 costs $0.2887 per million input tokens and $0.9594 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 or Kimi K2 0905?
Kimi K2 0905, with 262K tokens against 164K.
How many benchmarks is the DeepSeek V3 vs Kimi K2 0905 comparison based on?
3 benchmarks have a result for both models as of October 21, 2026. DeepSeek V3's coding index rests on 0 independent benchmarks and Kimi K2 0905's on 0. Where only one model has a result, the page says so instead of naming a winner.