DeepSeek V3.1 vs Kimi K2 Thinking
Updated October 21, 2026. 2 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.
DeepSeek V3.1 has the edge where output tokens cost 1.7x less, and where the benchmark that matters is AA Intelligence Index, where it leads.
- Shared results
- 2
- DeepSeek V3.1 only
- 8
- Kimi K2 Thinking only
- 28
- Like-for-like categories
- 2 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
Kimi K2 Thinking
Kimi K2 Thinking leads, 22.0 against 13.7.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only Kimi K2 Thinking has a GPQA Diamond result, 78.5%.
Directional only
Math
AIME
No call
Only Kimi K2 Thinking has a AIME result, 85.4%.
Directional only
Knowledge
MMLU Pro
No call
Only Kimi K2 Thinking has a MMLU Pro result, 81.1%.
Directional only
Web design
Design Arena: websites
DeepSeek V3.1
DeepSeek V3.1 leads, 1123 against 1113.
Same board, both measured
Long documents
Listed context window
Kimi K2 Thinking
Kimi K2 Thinking takes 262K tokens against 164K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V3.1
DeepSeek V3.1 $0.0013, Kimi K2 Thinking $0.0019 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Kimi K2 Thinking has the number, $0.61.
Directional only
At a glance
- Coding index
- DeepSeek V3.1 not benchmarked, Kimi K2 Thinking not benchmarked.
- Widest category
- Intelligence, 8.3 points apart on average over 1 shared benchmark.
- Widest single gap
- AA Intelligence Index: DeepSeek V3.1 13.7 against Kimi K2 Thinking 22.0, 8.3 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3.1 $1.50 against Kimi K2 Thinking $2.50 per million output tokens, 1.7x apart.
- Context window
- DeepSeek V3.1 takes 164K tokens, Kimi K2 Thinking 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 V3.1 | Bars | Kimi K2 Thinking | Ties |
|---|---|---|---|---|
| Intelligence | 0 | 1 | ||
| Web design | 1 | 0 | ||
| All shared boards | 1 | 1 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index8 boards, none shared
| Benchmark | DeepSeek V3.1 | Kimi K2 Thinking | Leader |
|---|---|---|---|
| SWE-bench Verified | Not measured | 63.4% | Only Kimi K2 Thinking measured |
| LiveCodeBench (Vals AI) | Not measured | 63.1% | Only Kimi K2 Thinking measured |
| SWE-bench Verified (Vals AI) | Not measured | 60.2% | Only Kimi K2 Thinking measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 85.3% | Only Kimi K2 Thinking measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 31.1% | Only Kimi K2 Thinking measured |
| Terminal-Bench | Not measured | 35.7% | Only Kimi K2 Thinking measured |
| WeirdML | Not measured | 42.8% | Only Kimi K2 Thinking measured |
| ALE-Bench | Not measured | 597.5 | Only Kimi K2 Thinking measured |
| Neither model has a result on the other 30 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V3.1 | Kimi K2 Thinking | Leader |
|---|---|---|---|
| AA Intelligence Index | 13.7 | 22.0 | Kimi K2 Thinking |
| Epoch Capabilities Index | Not measured | 145.8 | Only Kimi K2 Thinking measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | DeepSeek V3.1 | Kimi K2 Thinking | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 93.0% | Only Kimi K2 Thinking measured |
| METR time horizons | Not measured | 59.2% | Only Kimi K2 Thinking measured |
| Neither model has a result on the other 37 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | DeepSeek V3.1 | Kimi K2 Thinking | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 78.5% | Only Kimi K2 Thinking measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 83.8% | Only Kimi K2 Thinking measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 23.8% | Only Kimi K2 Thinking measured |
| Neither model has a result on the other 18 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | DeepSeek V3.1 | Kimi K2 Thinking | Leader |
|---|---|---|---|
| AIME | Not measured | 85.4% | Only Kimi K2 Thinking measured |
| MGSM (Vals AI) | Not measured | 90.2% | Only Kimi K2 Thinking measured |
| AA Math Index | Not measured | 94.7 | Only Kimi K2 Thinking measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 94.7% | Only Kimi K2 Thinking measured |
| Neither model has a result on the other 9 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | DeepSeek V3.1 | Kimi K2 Thinking | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 81.1% | Only Kimi K2 Thinking measured |
| LegalBench (Vals AI) | Not measured | 80.2% | Only Kimi K2 Thinking measured |
| CaseLaw (Vals AI) | Not measured | 65.7% | Only Kimi K2 Thinking measured |
| CorpFin (Vals AI) | Not measured | 60.6% | Only Kimi K2 Thinking measured |
| TaxEval (Vals AI) | Not measured | 71.7% | Only Kimi K2 Thinking measured |
| MedQA (Vals AI) | Not measured | 92.6% | Only Kimi K2 Thinking measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 84.8% | Only Kimi K2 Thinking measured |
| Neither model has a result on the other 10 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V3.1 | Kimi K2 Thinking | Leader |
|---|---|---|---|
| Design Arena: websites | 1123 | 1113 | DeepSeek V3.1 |
| Design Arena: UI components | 1085 | Not measured | Only DeepSeek V3.1 measured |
| Design Arena: game dev | 1084 | Not measured | Only DeepSeek V3.1 measured |
| Design Arena: data viz | 1095 | Not measured | Only DeepSeek V3.1 measured |
| Design Arena: 3D | 1077 | Not measured | Only DeepSeek V3.1 measured |
| Design Arena: all code categories | 1112 | Not measured | Only DeepSeek V3.1 measured |
| Design Arena: SVG | 967 | Not measured | Only DeepSeek V3.1 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 V3.1 | Kimi K2 Thinking | Leader |
|---|---|---|---|
| IFBench | Not measured | 68.1% | Only Kimi K2 Thinking measured |
| Long context reasoning (AA-LCR) | Not measured | 72.0% | Only Kimi K2 Thinking measured |
| DTBench | 82.7% | Not measured | Only DeepSeek V3.1 measured |
| LMCA | 24.3% | Not measured | Only DeepSeek V3.1 measured |
| CL-bench | Not measured | 17.6% | Only Kimi K2 Thinking 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 V3.1 | Kimi K2 Thinking | 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 V3.1 costs less
Repository review
50K input + 3K output tokens
DeepSeek V3.1 costs less
Agent loop
200K input + 10K output tokens
DeepSeek V3.1 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 V3.1 | MoonshotAI Kimi K2 Thinking |
|---|---|---|
| Maker | DeepSeek | Moonshot AI |
| Weights | Open weights | Open weights |
| Released | August 21, 2025 | November 6, 2025 |
| Context window | 164K | 262K |
| Input $/Mtok | $0.55 | $0.60 |
| Output $/Mtok | $1.50 | $2.50 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 1 |
| Cost per solved task | Not computable | $0.61 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V3.1 better than Kimi K2 Thinking for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. DeepSeek V3.1 has the edge where output tokens cost 1.7x less, and where the benchmark that matters is AA Intelligence Index, where it leads.
Which is cheaper, DeepSeek V3.1 or Kimi K2 Thinking?
DeepSeek V3.1 costs $0.55 per million input tokens and $1.50 per million output tokens; Kimi K2 Thinking costs $0.60 and $2.50. Cost per solved task is known for Kimi K2 Thinking only.
Which has the bigger context window, DeepSeek V3.1 or Kimi K2 Thinking?
Kimi K2 Thinking, with 262K tokens against 164K.
How many benchmarks is the DeepSeek V3.1 vs Kimi K2 Thinking comparison based on?
2 benchmarks have a result for both models as of October 21, 2026. DeepSeek V3.1's coding index rests on 0 independent benchmarks and Kimi K2 Thinking's on 1. Where only one model has a result, the page says so instead of naming a winner.