DeepSeek V4 Pro 0423 vs MiMo-V2.6-Pro
MiMo-V2.6-Pro leads DeepSeek V4 Pro 0423 on coding, 65.4 to 38.7 on the Vibe Coding Index, and MiMo-V2.6-Pro costs 3.4x less per output token ($0.87 vs $2.976 per million). Updated October 22, 2026; 47 benchmarks have a result for both.
DeepSeek V4 Pro 0423 or MiMo-V2.6-Pro: which to pick
Pick MiMo-V2.6-Pro for coding: it scores 65.4 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 work measured by LMArena Text: multi-turn, where it leads.
- Shared results
- 47
- DeepSeek V4 Pro 0423 only
- 37
- MiMo-V2.6-Pro only
- 26
- 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
MiMo-V2.6-Pro
MiMo-V2.6-Pro scores 65.4 against 38.7.
5 and 4 benchmarks behind the scores
Intelligence
AA Intelligence Index
MiMo-V2.6-Pro
MiMo-V2.6-Pro leads, 46.3 against 30.4.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only DeepSeek V4 Pro 0423 has a AA Agentic Index result, 26.3.
Directional only
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
MiMo-V2.6-Pro
MiMo-V2.6-Pro leads, 1308 against 1234.
Same board, both measured
General
Vals Index
No call
Only MiMo-V2.6-Pro has a Vals Index result, 57.8%.
Directional only
Long documents
Listed context window
MiMo-V2.6-Pro
MiMo-V2.6-Pro takes 1.05M tokens against 1.05M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
MiMo-V2.6-Pro
DeepSeek V4 Pro 0423 $0.0028, MiMo-V2.6-Pro $0.0009 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only DeepSeek V4 Pro 0423 has the number, $0.57.
Directional only
At a glance
- Coding index
- DeepSeek V4 Pro 0423 38.7 from 5 benchmarks, MiMo-V2.6-Pro 65.4 from 4 benchmarks.
- Widest category
- Math, 24.3 points apart on average over 3 shared benchmarks.
- Widest single gap
- ProofBench: DeepSeek V4 Pro 0423 16.0% against MiMo-V2.6-Pro 70.0%, 54.0 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V4 Pro 0423 $2.976 against MiMo-V2.6-Pro $0.87 per million output tokens, 3.4x apart.
- Context window
- DeepSeek V4 Pro 0423 takes 1.05M tokens, MiMo-V2.6-Pro 1.05M.
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 | MiMo-V2.6-Pro | Ties |
|---|---|---|---|---|
| Coding | 0 | 11 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 5 | ||
| Reasoning | 0 | 3 | ||
| Math | 0 | 3 | ||
| Knowledge | 0 | 5 | ||
| Web design | 0 | 6 | ||
| General | 1 | 12 | ||
| All shared boards | 1 | 46 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index011
| Benchmark | DeepSeek V4 Pro 0423 | MiMo-V2.6-Pro | Leader |
|---|---|---|---|
| LiveBench coding | Not measured | 81.5% | Only MiMo-V2.6-Pro measured |
| LMArena WebDev | 1447 | 1625 | MiMo-V2.6-Pro |
| AA Coding Index | 59.4 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LiveCodeBench (Vals AI) | 87.5% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| SWE-bench Verified (Vals AI) | 77.4% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Terminal-Bench 4 (Vals AI) | 11.1% | 31.3% | MiMo-V2.6-Pro |
| Vibe Code Bench (Vals AI) | 49.9% | 85.2% | MiMo-V2.6-Pro |
| Code migration (Vals AI) | 26.2% | 43.0% | MiMo-V2.6-Pro |
| ProgramBench (Vals AI) | 0.0% | 0.5% | MiMo-V2.6-Pro |
| IOI (Vals AI) | Not measured | 39.3% | Only MiMo-V2.6-Pro measured |
| Terminal-Bench 2.1 (Vals AI) | 50.2% | 67.8% | MiMo-V2.6-Pro |
| SWE-bench Multimodal (Vals AI) | Not measured | 41.3% | Only MiMo-V2.6-Pro measured |
| SkillsBench (Vals AI) | 51.3% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| CyberBench (Vals AI) | Not measured | 72.9% | Only MiMo-V2.6-Pro measured |
| SciCode | Not measured | 60.9% | Only MiMo-V2.6-Pro measured |
| FrontierCode | 17.6% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| WeirdML | 48.9% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| ALE-Bench | 1006.1 | 1158.1 | MiMo-V2.6-Pro |
| LMArena Text: coding | 1470 | 1533 | MiMo-V2.6-Pro |
| LMArena Text: software and IT | 1469 | 1528 | MiMo-V2.6-Pro |
| LMArena WebDev: React | 1441 | 1639 | MiMo-V2.6-Pro |
| LMArena WebDev: HTML | 1435 | 1616 | MiMo-V2.6-Pro |
| LMArena WebDev: from an image | Not measured | 1598 | Only MiMo-V2.6-Pro measured |
| Neither model has a result on the other 16 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V4 Pro 0423 | MiMo-V2.6-Pro | Leader |
|---|---|---|---|
| AA Intelligence Index | 30.4 | 46.3 | MiMo-V2.6-Pro |
| Epoch Capabilities Index | 149.1 | Not measured | Only DeepSeek V4 Pro 0423 measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | DeepSeek V4 Pro 0423 | MiMo-V2.6-Pro | Leader |
|---|---|---|---|
| AA Agentic Index | 26.3 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Design Arena: agents building web apps | 1000 | 1290 | MiMo-V2.6-Pro |
| Design Arena: agents building full-stack apps | 948 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Design Arena: agents building Godot games | 1059 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LiveBench research | Not measured | 76.7% | Only MiMo-V2.6-Pro measured |
| LiveBench browser | Not measured | 88.0% | Only MiMo-V2.6-Pro measured |
| LiveBench long agentic | Not measured | 47.5% | Only MiMo-V2.6-Pro measured |
| SREBench (Vals AI) | Not measured | 3.0% | Only MiMo-V2.6-Pro measured |
| Finance Agent (Vals AI) | 44.1% | 57.3% | MiMo-V2.6-Pro |
| Tax Agent Bench (Vals AI) | 25.2% | 32.4% | MiMo-V2.6-Pro |
| Legal Research Bench (Vals AI) | 23.1% | 47.1% | MiMo-V2.6-Pro |
| Harvey's Legal Agent Benchmark (Vals AI) | Not measured | 13.8% | Only MiMo-V2.6-Pro measured |
| Excel Modeling Benchmark (Vals AI) | 51.6% | 62.9% | MiMo-V2.6-Pro |
| Terminal-Bench-Science (Vals AI) | Not measured | 2.9% | Only MiMo-V2.6-Pro measured |
| APEX-Agents | Not measured | 54.4% | Only MiMo-V2.6-Pro measured |
| Vending-Bench 2 | $3,285 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Neither model has a result on the other 25 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | DeepSeek V4 Pro 0423 | MiMo-V2.6-Pro | Leader |
|---|---|---|---|
| GPQA Diamond | 89.4% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LiveBench reasoning | Not measured | 79.6% | Only MiMo-V2.6-Pro measured |
| MysteryMechanism (Vals AI) | Not measured | 15.3% | Only MiMo-V2.6-Pro measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 49.4% | Only MiMo-V2.6-Pro measured |
| GPQA Diamond (Epoch AI run) | 90.9% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Chess puzzles | 19.0% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Mystery game puzzles | 17.0% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: hard prompts | 1461 | 1511 | MiMo-V2.6-Pro |
| LMArena Text: expert | 1463 | 1541 | MiMo-V2.6-Pro |
| LMArena Text: hard prompts in English | 1466 | 1520 | MiMo-V2.6-Pro |
| LMArena Vision: diagrams | Not measured | 1281 | Only MiMo-V2.6-Pro measured |
| LMArena Vision: homework | Not measured | 1264 | Only MiMo-V2.6-Pro measured |
| Kagi LLM Benchmark | 53.5% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Neither model has a result on the other 9 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | DeepSeek V4 Pro 0423 | MiMo-V2.6-Pro | Leader |
|---|---|---|---|
| OTIS Mock AIME | 96.7% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| FrontierMath, tiers 1 to 3 | 45.3% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| FrontierMath, tier 4 | 2.4% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| ProofBench | 16.0% | 70.0% | MiMo-V2.6-Pro |
| LMArena Text: math | 1443 | 1500 | MiMo-V2.6-Pro |
| LMArena Text: mathematical work | 1455 | 1511 | MiMo-V2.6-Pro |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | DeepSeek V4 Pro 0423 | MiMo-V2.6-Pro | 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% | 45.0% | MiMo-V2.6-Pro |
| SimpleQA Verified | 47.0% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: science | 1470 | 1506 | MiMo-V2.6-Pro |
| LMArena Text: medicine and healthcare | 1466 | 1497 | MiMo-V2.6-Pro |
| LMArena Text: legal and government | 1464 | 1485 | MiMo-V2.6-Pro |
| LMArena Text: business and finance | 1443 | 1468 | MiMo-V2.6-Pro |
| Neither model has a result on the other 7 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | DeepSeek V4 Pro 0423 | MiMo-V2.6-Pro | Leader |
|---|---|---|---|
| Design Arena: websites | 1234 | 1308 | MiMo-V2.6-Pro |
| Design Arena: UI components | 1219 | 1340 | MiMo-V2.6-Pro |
| Design Arena: game dev | 1228 | 1314 | MiMo-V2.6-Pro |
| Design Arena: data viz | 1203 | 1336 | MiMo-V2.6-Pro |
| Design Arena: 3D | 1253 | 1330 | MiMo-V2.6-Pro |
| Design Arena: all code categories | 1240 | 1317 | MiMo-V2.6-Pro |
| Design Arena: SVG | 1139 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Design Arena: ASCII art | 1149 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LiveBench design | Not measured | 78.5% | Only MiMo-V2.6-Pro measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index112
| Benchmark | DeepSeek V4 Pro 0423 | MiMo-V2.6-Pro | Leader |
|---|---|---|---|
| Vals Index | Not measured | 57.8% | Only MiMo-V2.6-Pro measured |
| LiveBench data analysis | Not measured | 72.3% | Only MiMo-V2.6-Pro measured |
| Public Benefits Bench (Vals AI) | 62.9% | 68.9% | MiMo-V2.6-Pro |
| SAGE (Vals AI) | Not measured | 45.0% | Only MiMo-V2.6-Pro measured |
| MedScribe (Vals AI) | 75.1% | 88.3% | MiMo-V2.6-Pro |
| Long context reasoning (AA-LCR) | Not measured | 86.3% | Only MiMo-V2.6-Pro 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% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| CL-bench Life | 13.5% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text | 1451 | 1491 | MiMo-V2.6-Pro |
| LMArena Text: instruction following | 1445 | 1491 | MiMo-V2.6-Pro |
| LMArena Text: creative writing | 1446 | 1466 | MiMo-V2.6-Pro |
| LMArena Vision | Not measured | 1263 | Only MiMo-V2.6-Pro measured |
| LMArena Text: English | 1459 | 1504 | MiMo-V2.6-Pro |
| LMArena Text: non-English | 1439 | 1474 | MiMo-V2.6-Pro |
| LMArena Text: Chinese | 1481 | 1529 | MiMo-V2.6-Pro |
| 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 | 1479 | MiMo-V2.6-Pro |
| LMArena Text: Polish | 1461 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: multi-turn | 1468 | 1466 | DeepSeek V4 Pro 0423 |
| LMArena Text: longer queries | 1458 | 1501 | MiMo-V2.6-Pro |
| LMArena Text: writing and language | 1447 | 1470 | MiMo-V2.6-Pro |
| LMArena Text: entertainment and media | 1431 | 1465 | MiMo-V2.6-Pro |
| LMArena Vision: English | Not measured | 1268 | Only MiMo-V2.6-Pro measured |
| LMArena Vision: reading text | Not measured | 1270 | Only MiMo-V2.6-Pro measured |
| Neither model has a result on the other 16 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V4 Pro 0423 | MiMo-V2.6-Pro | 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
MiMo-V2.6-Pro costs less
Repository review
50K input + 3K output tokens
MiMo-V2.6-Pro costs less
Agent loop
200K input + 10K output tokens
MiMo-V2.6-Pro 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 | Xiaomi MiMo-V2.6-Pro |
|---|---|---|
| Maker | DeepSeek | Xiaomi |
| Weights | Open weights | Open weights |
| Released | April 24, 2026 | September 21, 2026 |
| Context window | 1.05M | 1.05M |
| Input $/Mtok | $1.358 | $0.435 |
| Output $/Mtok | $2.976 | $0.87 |
| Coding index | 38.7 | 65.4 |
| Independent measurements behind it | 5 | 4 |
| Cost per solved task | $0.57 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V4 Pro 0423 better than MiMo-V2.6-Pro for coding?
Pick MiMo-V2.6-Pro for coding: it scores 65.4 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 work measured by LMArena Text: multi-turn, where it leads.
Which is cheaper, DeepSeek V4 Pro 0423 or MiMo-V2.6-Pro?
DeepSeek V4 Pro 0423 costs $1.358 per million input tokens and $2.976 per million output tokens; MiMo-V2.6-Pro costs $0.435 and $0.87. Cost per solved task is known for DeepSeek V4 Pro 0423 only.
Which has the bigger context window, DeepSeek V4 Pro 0423 or MiMo-V2.6-Pro?
MiMo-V2.6-Pro, with 1.05M tokens against 1.05M.
How many benchmarks is the DeepSeek V4 Pro 0423 vs MiMo-V2.6-Pro comparison based on?
47 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 MiMo-V2.6-Pro's on 4. Where only one model has a result, the page says so instead of naming a winner.