DeepSeek V4 Pro 0813 vs Kimi K2.7 Code
DeepSeek V4 Pro 0813 leads Kimi K2.7 Code on coding, 56.1 to 53.4 on the Vibe Coding Index, and DeepSeek V4 Pro 0813 costs 2.0x less per output token ($1.98 vs $4.00 per million). Updated October 22, 2026; 28 benchmarks have a result for both.
DeepSeek V4 Pro 0813 or Kimi K2.7 Code: which to pick
Pick DeepSeek V4 Pro 0813 for coding: it scores 56.1 on the Vibe Coding Index against 53.4 for Kimi K2.7 Code.
Pick Kimi K2.7 Code for work measured by Terminal-Bench 2.1 (Vals AI) or FrontierCode, where it leads.
- Shared results
- 28
- DeepSeek V4 Pro 0813 only
- 50
- Kimi K2.7 Code only
- 23
- 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
DeepSeek V4 Pro 0813
DeepSeek V4 Pro 0813 scores 56.1 against 53.4.
5 and 4 benchmarks behind the scores
Intelligence
AA Intelligence Index
DeepSeek V4 Pro 0813
DeepSeek V4 Pro 0813 leads, 36.0 against 25.8.
Same board, both measured
Agentic work
AA Agentic Index
DeepSeek V4 Pro 0813
DeepSeek V4 Pro 0813 leads, 41.3 against 21.0.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only DeepSeek V4 Pro 0813 has a GPQA Diamond result, 92.4%.
Directional only
Knowledge
MMLU Pro
No call
Only DeepSeek V4 Pro 0813 has a MMLU Pro result, 87.0%.
Directional only
Web design
Design Arena: websites
No call
Only Kimi K2.7 Code has a Design Arena: websites result, 1263.
Directional only
General
Vals Index
No call
Only DeepSeek V4 Pro 0813 has a Vals Index result, 53.0%.
Directional only
Long documents
Listed context window
DeepSeek V4 Pro 0813
DeepSeek V4 Pro 0813 takes 1.05M tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V4 Pro 0813
DeepSeek V4 Pro 0813 $0.0016, Kimi K2.7 Code $0.0029 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
DeepSeek V4 Pro 0813
DeepSeek V4 Pro 0813 $0.10, Kimi K2.7 Code $0.35.
Published attempt cost and pass rate
At a glance
- Coding index
- DeepSeek V4 Pro 0813 56.1 from 5 benchmarks, Kimi K2.7 Code 53.4 from 4 benchmarks.
- Widest category
- Knowledge, 16.4 points apart on average over 1 shared benchmark.
- Widest single gap
- Vibe Code Bench (Vals AI): DeepSeek V4 Pro 0813 82.3% against Kimi K2.7 Code 47.2%, 35.1 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V4 Pro 0813 $1.98 against Kimi K2.7 Code $4.00 per million output tokens, 2.0x apart.
- Context window
- DeepSeek V4 Pro 0813 takes 1.05M tokens, Kimi K2.7 Code 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 V4 Pro 0813 | Bars | Kimi K2.7 Code | Ties |
|---|---|---|---|---|
| Coding | 12 | 2 | 1 tie | |
| Intelligence | 2 | 0 | ||
| Agentic work | 2 | 0 | ||
| Reasoning | 4 | 0 | ||
| Math | 3 | 0 | ||
| Knowledge | 1 | 0 | ||
| General | 1 | 0 | ||
| All shared boards | 25 | 2 | 1 tie |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index122
| Benchmark | DeepSeek V4 Pro 0813 | Kimi K2.7 Code | Leader |
|---|---|---|---|
| LMArena WebDev | 1582 | 1474 | DeepSeek V4 Pro 0813 |
| AA Coding Index | 68.8 | 60.8 | DeepSeek V4 Pro 0813 |
| LiveCodeBench (Vals AI) | 87.5% | 82.0% | DeepSeek V4 Pro 0813 |
| SWE-bench Verified (Vals AI) | 96.4% | 78.2% | DeepSeek V4 Pro 0813 |
| Terminal-Bench 4 (Vals AI) | 14.1% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Vibe Code Bench (Vals AI) | 82.3% | 47.2% | DeepSeek V4 Pro 0813 |
| Code migration (Vals AI) | 41.5% | 25.4% | DeepSeek V4 Pro 0813 |
| ProgramBench (Vals AI) | 0.0% | 0.0% | Tie |
| IOI (Vals AI) | 51.6% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Terminal-Bench 2.1 (Vals AI) | 54.7% | 67.0% | Kimi K2.7 Code |
| Vibe Code Bench 1-100 (Vals AI) | 17.5% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| SkillsBench (Vals AI) | 53.8% | 50.0% | DeepSeek V4 Pro 0813 |
| CyberBench (Vals AI) | 72.9% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| SciCode | 51.0% | 47.8% | DeepSeek V4 Pro 0813 |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 44.7% | Only Kimi K2.7 Code measured |
| FrontierCode | 28.5% | 30.1% | Kimi K2.7 Code |
| WeirdML | 66.2% | 54.1% | DeepSeek V4 Pro 0813 |
| ALE-Bench | 1403.2 | 886.2 | DeepSeek V4 Pro 0813 |
| LMArena Text: coding | 1469 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: software and IT | 1462 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena WebDev: React | 1588 | 1475 | DeepSeek V4 Pro 0813 |
| LMArena WebDev: HTML | 1566 | 1451 | DeepSeek V4 Pro 0813 |
| LMArena WebDev: from an image | Not measured | 1513 | Only Kimi K2.7 Code measured |
| Neither model has a result on the other 16 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | DeepSeek V4 Pro 0813 | Kimi K2.7 Code | Leader |
|---|---|---|---|
| AA Intelligence Index | 36.0 | 25.8 | DeepSeek V4 Pro 0813 |
| Epoch Capabilities Index | 155.6 | 150.1 | DeepSeek V4 Pro 0813 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | DeepSeek V4 Pro 0813 | Kimi K2.7 Code | Leader |
|---|---|---|---|
| AA Agentic Index | 41.3 | 21.0 | DeepSeek V4 Pro 0813 |
| Design Arena: agents building web apps | Not measured | 1176 | Only Kimi K2.7 Code measured |
| Design Arena: agents building full-stack apps | Not measured | 1175 | Only Kimi K2.7 Code measured |
| Design Arena: agents building mobile apps | Not measured | 1143 | Only Kimi K2.7 Code measured |
| Design Arena: agents building Android apps | Not measured | 1138 | Only Kimi K2.7 Code measured |
| Design Arena: agents building Godot games | Not measured | 1147 | Only Kimi K2.7 Code measured |
| Design Arena: agents building games | Not measured | 1130 | Only Kimi K2.7 Code measured |
| Design Arena: HTML slides | Not measured | 1151 | Only Kimi K2.7 Code measured |
| Design Arena: PowerPoint slides | Not measured | 1129 | Only Kimi K2.7 Code measured |
| SREBench (Vals AI) | 1.9% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Finance Agent (Vals AI) | 50.4% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Tax Agent Bench (Vals AI) | 26.8% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Legal Research Bench (Vals AI) | 40.9% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Harvey's Legal Agent Benchmark (Vals AI) | 10.8% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Excel Modeling Benchmark (Vals AI) | 52.8% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Terminal-Bench-Science (Vals AI) | 4.3% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| τ²-Bench Telecom | Not measured | 90.1% | Only Kimi K2.7 Code measured |
| APEX-Agents | 47.3% | 37.6% | DeepSeek V4 Pro 0813 |
| Vending-Bench 2 | Not measured | $5,083 | Only Kimi K2.7 Code measured |
| Neither model has a result on the other 22 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | DeepSeek V4 Pro 0813 | Kimi K2.7 Code | Leader |
|---|---|---|---|
| GPQA Diamond | 92.4% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| GPQA Diamond (Artificial Analysis run) | 92.8% | 89.6% | DeepSeek V4 Pro 0813 |
| Humanity's Last Exam (Artificial Analysis run) | 41.0% | 35.0% | DeepSeek V4 Pro 0813 |
| GPQA Diamond (Epoch AI run) | 91.7% | 87.9% | DeepSeek V4 Pro 0813 |
| ARC-AGI | 90.5% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| ARC-AGI-2 | 61.3% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| SimpleBench | Not measured | 57.9% | Only Kimi K2.7 Code measured |
| Chess puzzles | 46.0% | 14.0% | DeepSeek V4 Pro 0813 |
| Mystery game puzzles | 43.0% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: hard prompts | 1458 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: expert | 1459 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: hard prompts in English | 1463 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Neither model has a result on the other 10 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | DeepSeek V4 Pro 0813 | Kimi K2.7 Code | Leader |
|---|---|---|---|
| OTIS Mock AIME | 98.6% | 95.6% | DeepSeek V4 Pro 0813 |
| FrontierMath, tiers 1 to 3 | 64.6% | 54.0% | DeepSeek V4 Pro 0813 |
| FrontierMath, tier 4 | 26.8% | 12.2% | DeepSeek V4 Pro 0813 |
| ProofBench | 50.0% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: math | 1441 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: mathematical work | 1443 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V4 Pro 0813 | Kimi K2.7 Code | Leader |
|---|---|---|---|
| MMLU Pro | 87.0% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LegalBench (Vals AI) | 82.4% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| CorpFin (Vals AI) | 65.4% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| TaxEval (Vals AI) | 73.1% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| MedCode (Vals AI) | 42.5% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| SimpleQA Verified | 52.9% | 36.5% | DeepSeek V4 Pro 0813 |
| LMArena Text: science | 1457 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: medicine and healthcare | 1446 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: legal and government | 1459 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: business and finance | 1442 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Neither model has a result on the other 8 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | DeepSeek V4 Pro 0813 | Kimi K2.7 Code | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1263 | Only Kimi K2.7 Code measured |
| Design Arena: UI components | Not measured | 1255 | Only Kimi K2.7 Code measured |
| Design Arena: game dev | Not measured | 1220 | Only Kimi K2.7 Code measured |
| Design Arena: data viz | Not measured | 1227 | Only Kimi K2.7 Code measured |
| Design Arena: 3D | Not measured | 1240 | Only Kimi K2.7 Code measured |
| Design Arena: all code categories | Not measured | 1252 | Only Kimi K2.7 Code measured |
| Design Arena: SVG | Not measured | 1176 | Only Kimi K2.7 Code measured |
| Design Arena: ASCII art | Not measured | 1196 | Only Kimi K2.7 Code measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V4 Pro 0813 | Kimi K2.7 Code | Leader |
|---|---|---|---|
| Vals Index | 53.0% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| MedScribe (Vals AI) | 80.2% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| IFBench | Not measured | 63.1% | Only Kimi K2.7 Code measured |
| Long context reasoning (AA-LCR) | 80.3% | 79.3% | DeepSeek V4 Pro 0813 |
| DTBench | 93.9% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMCA | 45.5% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Surface Evolver Bench | Not measured | 48.8% | Only Kimi K2.7 Code measured |
| LMArena Text | 1446 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: instruction following | 1448 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: creative writing | 1439 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: English | 1457 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: non-English | 1431 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: Chinese | 1481 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: Korean | 1449 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: French | 1446 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: Spanish | 1435 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: Russian | 1448 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: multi-turn | 1454 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: longer queries | 1458 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: writing and language | 1443 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: entertainment and media | 1424 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Neither model has a result on the other 25 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V4 Pro 0813 | Kimi K2.7 Code | 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 V4 Pro 0813 costs less
Repository review
50K input + 3K output tokens
DeepSeek V4 Pro 0813 costs less
Agent loop
200K input + 10K output tokens
DeepSeek V4 Pro 0813 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 V4 Pro 0813 | MoonshotAI Kimi K2.7 Code |
|---|---|---|
| Maker | DeepSeek | Moonshot AI |
| Weights | Open weights | Open weights |
| Released | August 12, 2026 | June 12, 2026 |
| Context window | 1.05M | 262K |
| Input $/Mtok | $0.66 | $0.95 |
| Output $/Mtok | $1.98 | $4.00 |
| Coding index | 56.1 | 53.4 |
| Independent measurements behind it | 5 | 4 |
| Cost per solved task | $0.10 | $0.35 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V4 Pro 0813 better than Kimi K2.7 Code for coding?
Pick DeepSeek V4 Pro 0813 for coding: it scores 56.1 on the Vibe Coding Index against 53.4 for Kimi K2.7 Code. Pick Kimi K2.7 Code for work measured by Terminal-Bench 2.1 (Vals AI) or FrontierCode, where it leads.
Which is cheaper, DeepSeek V4 Pro 0813 or Kimi K2.7 Code?
DeepSeek V4 Pro 0813 costs $0.66 per million input tokens and $1.98 per million output tokens; Kimi K2.7 Code costs $0.95 and $4.00. Per solved SWE-bench Verified task, DeepSeek V4 Pro 0813 costs $0.10 and Kimi K2.7 Code $0.35.
Which has the bigger context window, DeepSeek V4 Pro 0813 or Kimi K2.7 Code?
DeepSeek V4 Pro 0813, with 1.05M tokens against 262K.
How many benchmarks is the DeepSeek V4 Pro 0813 vs Kimi K2.7 Code comparison based on?
28 benchmarks have a result for both models as of October 22, 2026. DeepSeek V4 Pro 0813's coding index rests on 5 independent benchmarks and Kimi K2.7 Code's on 4. Where only one model has a result, the page says so instead of naming a winner.