DeepSeek V4 Pro 0423 vs Kimi K2.7 Code
Kimi K2.7 Code leads DeepSeek V4 Pro 0423 on coding, 53.4 to 38.7 on the Vibe Coding Index, and DeepSeek V4 Pro 0423 costs 1.3x less per output token ($2.976 vs $4.00 per million). Updated October 22, 2026; 36 benchmarks have a result for both.
DeepSeek V4 Pro 0423 or Kimi K2.7 Code: which to pick
Pick Kimi K2.7 Code for coding: it scores 53.4 on the Vibe Coding Index against 38.7 for DeepSeek V4 Pro 0423, though on 4 benchmarks against 5.
Pick DeepSeek V4 Pro 0423 for output tokens 1.3x cheaper ($2.976 vs $4.00 per million) and a larger context window (1.05M against 262K tokens).
- Shared results
- 36
- DeepSeek V4 Pro 0423 only
- 48
- Kimi K2.7 Code only
- 15
- 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
Kimi K2.7 Code
Kimi K2.7 Code scores 53.4 against 38.7.
5 and 4 benchmarks behind the scores
Intelligence
AA Intelligence Index
DeepSeek V4 Pro 0423
DeepSeek V4 Pro 0423 leads, 30.4 against 25.8.
Same board, both measured
Agentic work
AA Agentic Index
DeepSeek V4 Pro 0423
DeepSeek V4 Pro 0423 leads, 26.3 against 21.0.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only DeepSeek V4 Pro 0423 has a GPQA Diamond result, 89.4%.
Directional only
Knowledge
MMLU Pro
No call
Only DeepSeek V4 Pro 0423 has a MMLU Pro result, 87.3%.
Directional only
Web design
Design Arena: websites
Kimi K2.7 Code
Kimi K2.7 Code leads, 1263 against 1234.
Same board, both measured
Long documents
Listed context window
DeepSeek V4 Pro 0423
DeepSeek V4 Pro 0423 takes 1.05M tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V4 Pro 0423
DeepSeek V4 Pro 0423 $0.0028, Kimi K2.7 Code $0.0029 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
Kimi K2.7 Code
DeepSeek V4 Pro 0423 $0.57, Kimi K2.7 Code $0.35.
Published attempt cost and pass rate
At a glance
- Coding index
- DeepSeek V4 Pro 0423 38.7 from 5 benchmarks, Kimi K2.7 Code 53.4 from 4 benchmarks.
- Widest category
- Agentic work, 15.6 points apart on average over 5 shared benchmarks.
- Widest single gap
- Design Arena: agents building full-stack apps: DeepSeek V4 Pro 0423 948 against Kimi K2.7 Code 1175, 28.4 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V4 Pro 0423 $2.976 against Kimi K2.7 Code $4.00 per million output tokens, 1.3x apart.
- Context window
- DeepSeek V4 Pro 0423 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 0423 | Bars | Kimi K2.7 Code | Ties |
|---|---|---|---|---|
| Coding | 5 | 8 | 1 tie | |
| Intelligence | 1 | 1 | ||
| Agentic work | 1 | 4 | ||
| Reasoning | 2 | 0 | ||
| Math | 1 | 2 | ||
| Knowledge | 1 | 0 | ||
| Web design | 2 | 6 | ||
| General | 0 | 1 | ||
| All shared boards | 13 | 22 | 1 tie |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index58
| Benchmark | DeepSeek V4 Pro 0423 | Kimi K2.7 Code | Leader |
|---|---|---|---|
| LMArena WebDev | 1447 | 1474 | Kimi K2.7 Code |
| AA Coding Index | 59.4 | 60.8 | Kimi K2.7 Code |
| LiveCodeBench (Vals AI) | 87.5% | 82.0% | DeepSeek V4 Pro 0423 |
| SWE-bench Verified (Vals AI) | 77.4% | 78.2% | Kimi K2.7 Code |
| Terminal-Bench 4 (Vals AI) | 11.1% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Vibe Code Bench (Vals AI) | 49.9% | 47.2% | DeepSeek V4 Pro 0423 |
| Code migration (Vals AI) | 26.2% | 25.4% | DeepSeek V4 Pro 0423 |
| ProgramBench (Vals AI) | 0.0% | 0.0% | Tie |
| Terminal-Bench 2.1 (Vals AI) | 50.2% | 67.0% | Kimi K2.7 Code |
| SkillsBench (Vals AI) | 51.3% | 50.0% | DeepSeek V4 Pro 0423 |
| SciCode | Not measured | 47.8% | Only Kimi K2.7 Code measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 44.7% | Only Kimi K2.7 Code measured |
| FrontierCode | 17.6% | 30.1% | Kimi K2.7 Code |
| WeirdML | 48.9% | 54.1% | Kimi K2.7 Code |
| ALE-Bench | 1006.1 | 886.2 | DeepSeek V4 Pro 0423 |
| LMArena Text: coding | 1470 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: software and IT | 1469 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena WebDev: React | 1441 | 1475 | Kimi K2.7 Code |
| LMArena WebDev: HTML | 1435 | 1451 | Kimi K2.7 Code |
| LMArena WebDev: from an image | Not measured | 1513 | Only Kimi K2.7 Code measured |
| Neither model has a result on the other 19 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | DeepSeek V4 Pro 0423 | Kimi K2.7 Code | Leader |
|---|---|---|---|
| AA Intelligence Index | 30.4 | 25.8 | DeepSeek V4 Pro 0423 |
| Epoch Capabilities Index | 149.1 | 150.1 | Kimi K2.7 Code |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index14
| Benchmark | DeepSeek V4 Pro 0423 | Kimi K2.7 Code | Leader |
|---|---|---|---|
| AA Agentic Index | 26.3 | 21.0 | DeepSeek V4 Pro 0423 |
| Design Arena: agents building web apps | 1000 | 1176 | Kimi K2.7 Code |
| Design Arena: agents building full-stack apps | 948 | 1175 | Kimi K2.7 Code |
| 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 | 1059 | 1147 | Kimi K2.7 Code |
| 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 |
| Finance Agent (Vals AI) | 44.1% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Tax Agent Bench (Vals AI) | 25.2% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Legal Research Bench (Vals AI) | 23.1% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Excel Modeling Benchmark (Vals AI) | 51.6% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| τ²-Bench Telecom | Not measured | 90.1% | Only Kimi K2.7 Code measured |
| APEX-Agents | Not measured | 37.6% | Only Kimi K2.7 Code measured |
| Vending-Bench 2 | $3,285 | $5,083 | Kimi K2.7 Code |
| Neither model has a result on the other 25 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | DeepSeek V4 Pro 0423 | Kimi K2.7 Code | Leader |
|---|---|---|---|
| GPQA Diamond | 89.4% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 89.6% | Only Kimi K2.7 Code measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 35.0% | Only Kimi K2.7 Code measured |
| GPQA Diamond (Epoch AI run) | 90.9% | 87.9% | DeepSeek V4 Pro 0423 |
| SimpleBench | Not measured | 57.9% | Only Kimi K2.7 Code measured |
| Chess puzzles | 19.0% | 14.0% | DeepSeek V4 Pro 0423 |
| Mystery game puzzles | 17.0% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: hard prompts | 1461 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: expert | 1463 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: hard prompts in English | 1466 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Kagi LLM Benchmark | 53.5% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Neither model has a result on the other 11 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index12
| Benchmark | DeepSeek V4 Pro 0423 | Kimi K2.7 Code | Leader |
|---|---|---|---|
| OTIS Mock AIME | 96.7% | 95.6% | DeepSeek V4 Pro 0423 |
| FrontierMath, tiers 1 to 3 | 45.3% | 54.0% | Kimi K2.7 Code |
| FrontierMath, tier 4 | 2.4% | 12.2% | Kimi K2.7 Code |
| ProofBench | 16.0% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: math | 1443 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: mathematical work | 1455 | Not measured | Only DeepSeek V4 Pro 0423 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 0423 | Kimi K2.7 Code | Leader |
|---|---|---|---|
| MMLU Pro | 87.3% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LegalBench (Vals AI) | 80.3% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| CaseLaw (Vals AI) | 59.4% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| CorpFin (Vals AI) | 61.4% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| TaxEval (Vals AI) | 72.1% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| MedCode (Vals AI) | 40.5% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| SimpleQA Verified | 47.0% | 36.5% | DeepSeek V4 Pro 0423 |
| LMArena Text: science | 1470 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: medicine and healthcare | 1466 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: legal and government | 1464 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: business and finance | 1443 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Neither model has a result on the other 7 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index26
| Benchmark | DeepSeek V4 Pro 0423 | Kimi K2.7 Code | Leader |
|---|---|---|---|
| Design Arena: websites | 1234 | 1263 | Kimi K2.7 Code |
| Design Arena: UI components | 1219 | 1255 | Kimi K2.7 Code |
| Design Arena: game dev | 1228 | 1220 | DeepSeek V4 Pro 0423 |
| Design Arena: data viz | 1203 | 1227 | Kimi K2.7 Code |
| Design Arena: 3D | 1253 | 1240 | DeepSeek V4 Pro 0423 |
| Design Arena: all code categories | 1240 | 1252 | Kimi K2.7 Code |
| Design Arena: SVG | 1139 | 1176 | Kimi K2.7 Code |
| Design Arena: ASCII art | 1149 | 1196 | Kimi K2.7 Code |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V4 Pro 0423 | Kimi K2.7 Code | Leader |
|---|---|---|---|
| Public Benefits Bench (Vals AI) | 62.9% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| MedScribe (Vals AI) | 75.1% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| IFBench | Not measured | 63.1% | Only Kimi K2.7 Code measured |
| Long context reasoning (AA-LCR) | Not measured | 79.3% | Only Kimi K2.7 Code measured |
| DTBench | 90.7% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMCA | 41.2% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Surface Evolver Bench | 40.0% | 48.8% | Kimi K2.7 Code |
| CL-bench Life | 13.5% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text | 1451 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: instruction following | 1445 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: creative writing | 1446 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: English | 1459 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: non-English | 1439 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: Chinese | 1481 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: German | 1458 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: Japanese | 1446 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: Korean | 1409 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: French | 1473 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: Spanish | 1457 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: Russian | 1453 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: Polish | 1461 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: multi-turn | 1468 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: longer queries | 1458 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: writing and language | 1447 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: entertainment and media | 1431 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Neither model has a result on the other 21 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V4 Pro 0423 | 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 0423 costs less
Repository review
50K input + 3K output tokens
Kimi K2.7 Code costs less
Agent loop
200K input + 10K output tokens
Kimi K2.7 Code 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 0423 | MoonshotAI Kimi K2.7 Code |
|---|---|---|
| Maker | DeepSeek | Moonshot AI |
| Weights | Open weights | Open weights |
| Released | April 24, 2026 | June 12, 2026 |
| Context window | 1.05M | 262K |
| Input $/Mtok | $1.358 | $0.95 |
| Output $/Mtok | $2.976 | $4.00 |
| Coding index | 38.7 | 53.4 |
| Independent measurements behind it | 5 | 4 |
| Cost per solved task | $0.57 | $0.35 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V4 Pro 0423 better than Kimi K2.7 Code for coding?
Pick Kimi K2.7 Code for coding: it scores 53.4 on the Vibe Coding Index against 38.7 for DeepSeek V4 Pro 0423, though on 4 benchmarks against 5. Pick DeepSeek V4 Pro 0423 for output tokens 1.3x cheaper ($2.976 vs $4.00 per million) and a larger context window (1.05M against 262K tokens).
Which is cheaper, DeepSeek V4 Pro 0423 or Kimi K2.7 Code?
DeepSeek V4 Pro 0423 costs $1.358 per million input tokens and $2.976 per million output tokens; Kimi K2.7 Code costs $0.95 and $4.00. Per solved SWE-bench Verified task, DeepSeek V4 Pro 0423 costs $0.57 and Kimi K2.7 Code $0.35.
Which has the bigger context window, DeepSeek V4 Pro 0423 or Kimi K2.7 Code?
DeepSeek V4 Pro 0423, with 1.05M tokens against 262K.
How many benchmarks is the DeepSeek V4 Pro 0423 vs Kimi K2.7 Code comparison based on?
36 benchmarks have a result for both models as of October 22, 2026. DeepSeek V4 Pro 0423'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.