DeepSeek V4.1 Flash vs MiMo-V2.6-Pro
MiMo-V2.6-Pro leads DeepSeek V4.1 Flash on coding, 65.4 to 62.5 on the Vibe Coding Index, at about the same output price ($0.87 vs $0.84 per million). Updated October 22, 2026; 70 benchmarks have a result for both.
DeepSeek V4.1 Flash 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 62.5 for DeepSeek V4.1 Flash, though on 4 benchmarks against 5.
Pick DeepSeek V4.1 Flash for output tokens 1.0x cheaper ($0.84 vs $0.87 per million) and work measured by LiveBench coding or Code migration (Vals AI), where it leads.
- Shared results
- 70
- DeepSeek V4.1 Flash only
- 31
- MiMo-V2.6-Pro only
- 3
- 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 62.5.
5 and 4 benchmarks behind the scores
Intelligence
AA Intelligence Index
MiMo-V2.6-Pro
MiMo-V2.6-Pro leads, 46.3 against 24.7.
Same board, both measured
Web design
Design Arena: websites
No call
Tied at 1308.
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
DeepSeek V4.1 Flash
DeepSeek V4.1 Flash $0.0006, MiMo-V2.6-Pro $0.0009 per turn.
List prices
At a glance
- Coding index
- DeepSeek V4.1 Flash 62.5 from 5 benchmarks, MiMo-V2.6-Pro 65.4 from 4 benchmarks.
- Widest category
- Intelligence, 21.6 points apart on average over 1 shared benchmark.
- Widest single gap
- AA Intelligence Index: DeepSeek V4.1 Flash 24.7 against MiMo-V2.6-Pro 46.3, 21.6 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V4.1 Flash $0.84 against MiMo-V2.6-Pro $0.87 per million output tokens, 1.0x apart.
- Context window
- DeepSeek V4.1 Flash 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 V4.1 Flash | Bars | MiMo-V2.6-Pro | Ties |
|---|---|---|---|---|
| Coding | 7 | 9 | 1 tie | |
| Intelligence | 0 | 1 | ||
| Agentic work | 1 | 9 | ||
| Reasoning | 4 | 4 | ||
| Math | 0 | 3 | ||
| Knowledge | 0 | 5 | ||
| Web design | 4 | 2 | 1 tie | |
| General | 5 | 14 | ||
| All shared boards | 21 | 47 | 2 ties |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index79
| Benchmark | DeepSeek V4.1 Flash | MiMo-V2.6-Pro | Leader |
|---|---|---|---|
| LiveBench coding | 90.2% | 81.5% | DeepSeek V4.1 Flash |
| LMArena WebDev | 1618 | 1625 | MiMo-V2.6-Pro |
| Terminal-Bench 4 (Vals AI) | 19.7% | 31.3% | MiMo-V2.6-Pro |
| Vibe Code Bench (Vals AI) | 84.7% | 85.2% | MiMo-V2.6-Pro |
| Code migration (Vals AI) | 45.6% | 43.0% | DeepSeek V4.1 Flash |
| ProgramBench (Vals AI) | 0.5% | 0.5% | Tie |
| IOI (Vals AI) | 40.3% | 39.3% | DeepSeek V4.1 Flash |
| Terminal-Bench 2.1 (Vals AI) | 74.5% | 67.8% | DeepSeek V4.1 Flash |
| SWE-bench Multimodal (Vals AI) | 46.5% | 41.3% | DeepSeek V4.1 Flash |
| Vibe Code Bench 1-100 (Vals AI) | 16.4% | Not measured | Only DeepSeek V4.1 Flash measured |
| SkillsBench (Vals AI) | 69.8% | Not measured | Only DeepSeek V4.1 Flash measured |
| CyberBench (Vals AI) | 73.7% | 72.9% | DeepSeek V4.1 Flash |
| SciCode | 51.9% | 60.9% | MiMo-V2.6-Pro |
| FrontierCode | 43.9% | Not measured | Only DeepSeek V4.1 Flash measured |
| ALE-Bench | 1092.3 | 1158.1 | MiMo-V2.6-Pro |
| LMArena Text: coding | 1502 | 1533 | MiMo-V2.6-Pro |
| LMArena Text: software and IT | 1496 | 1528 | MiMo-V2.6-Pro |
| LMArena WebDev: React | 1628 | 1639 | MiMo-V2.6-Pro |
| LMArena WebDev: HTML | 1612 | 1616 | MiMo-V2.6-Pro |
| LMArena WebDev: from an image | 1606 | 1598 | DeepSeek V4.1 Flash |
| Neither model has a result on the other 19 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V4.1 Flash | MiMo-V2.6-Pro | Leader |
|---|---|---|---|
| AA Intelligence Index | 24.7 | 46.3 | MiMo-V2.6-Pro |
| Epoch Capabilities Index | 155.9 | Not measured | Only DeepSeek V4.1 Flash measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index19
| Benchmark | DeepSeek V4.1 Flash | MiMo-V2.6-Pro | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | Not measured | 1290 | Only MiMo-V2.6-Pro measured |
| LiveBench research | 86.8% | 76.7% | DeepSeek V4.1 Flash |
| LiveBench browser | 84.0% | 88.0% | MiMo-V2.6-Pro |
| LiveBench long agentic | 40.1% | 47.5% | MiMo-V2.6-Pro |
| SREBench (Vals AI) | 0.8% | 3.0% | MiMo-V2.6-Pro |
| Finance Agent (Vals AI) | 53.5% | 57.3% | MiMo-V2.6-Pro |
| Tax Agent Bench (Vals AI) | 26.6% | 32.4% | MiMo-V2.6-Pro |
| Legal Research Bench (Vals AI) | 41.4% | 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) | 57.2% | 62.9% | MiMo-V2.6-Pro |
| BioMysteryBench (Vals AI) | 67.8% | Not measured | Only DeepSeek V4.1 Flash measured |
| Terminal-Bench-Science (Vals AI) | 0.0% | 2.9% | MiMo-V2.6-Pro |
| Vals Science Index | 29.6% | Not measured | Only DeepSeek V4.1 Flash measured |
| APEX-Agents | 39.5% | 54.4% | MiMo-V2.6-Pro |
| Neither model has a result on the other 27 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index44
| Benchmark | DeepSeek V4.1 Flash | MiMo-V2.6-Pro | Leader |
|---|---|---|---|
| LiveBench reasoning | 86.7% | 79.6% | DeepSeek V4.1 Flash |
| MysteryMechanism (Vals AI) | 21.2% | 15.3% | DeepSeek V4.1 Flash |
| Humanity's Last Exam (Artificial Analysis run) | 39.2% | 49.4% | MiMo-V2.6-Pro |
| GPQA Diamond (Epoch AI run) | 89.8% | Not measured | Only DeepSeek V4.1 Flash measured |
| ARC-AGI | 94.5% | Not measured | Only DeepSeek V4.1 Flash measured |
| ARC-AGI-2 | 72.9% | Not measured | Only DeepSeek V4.1 Flash measured |
| Chess puzzles | 48.0% | Not measured | Only DeepSeek V4.1 Flash measured |
| Mystery game puzzles | 43.0% | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: hard prompts | 1483 | 1511 | MiMo-V2.6-Pro |
| LMArena Text: expert | 1501 | 1541 | MiMo-V2.6-Pro |
| LMArena Text: hard prompts in English | 1488 | 1520 | MiMo-V2.6-Pro |
| LMArena Vision: diagrams | 1297 | 1281 | DeepSeek V4.1 Flash |
| LMArena Vision: homework | 1307 | 1264 | DeepSeek V4.1 Flash |
| 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 V4.1 Flash | MiMo-V2.6-Pro | Leader |
|---|---|---|---|
| OTIS Mock AIME | 98.3% | Not measured | Only DeepSeek V4.1 Flash measured |
| FrontierMath, tiers 1 to 3 | 67.4% | Not measured | Only DeepSeek V4.1 Flash measured |
| FrontierMath, tier 4 | 26.8% | Not measured | Only DeepSeek V4.1 Flash measured |
| ProofBench | 54.0% | 70.0% | MiMo-V2.6-Pro |
| LMArena Text: math | 1478 | 1500 | MiMo-V2.6-Pro |
| LMArena Text: mathematical work | 1485 | 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 V4.1 Flash | MiMo-V2.6-Pro | Leader |
|---|---|---|---|
| LegalBench (Vals AI) | 83.3% | Not measured | Only DeepSeek V4.1 Flash measured |
| MedCode (Vals AI) | 41.2% | 45.0% | MiMo-V2.6-Pro |
| SimpleQA Verified | 42.9% | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: science | 1469 | 1506 | MiMo-V2.6-Pro |
| LMArena Text: medicine and healthcare | 1484 | 1497 | MiMo-V2.6-Pro |
| LMArena Text: legal and government | 1443 | 1485 | MiMo-V2.6-Pro |
| LMArena Text: business and finance | 1461 | 1468 | MiMo-V2.6-Pro |
| Neither model has a result on the other 11 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index42
| Benchmark | DeepSeek V4.1 Flash | MiMo-V2.6-Pro | Leader |
|---|---|---|---|
| Design Arena: websites | 1308 | 1308 | Tie |
| Design Arena: UI components | 1312 | 1340 | MiMo-V2.6-Pro |
| Design Arena: game dev | 1330 | 1314 | DeepSeek V4.1 Flash |
| Design Arena: data viz | 1291 | 1336 | MiMo-V2.6-Pro |
| Design Arena: 3D | 1355 | 1330 | DeepSeek V4.1 Flash |
| Design Arena: all code categories | 1320 | 1317 | DeepSeek V4.1 Flash |
| Design Arena: SVG | 1245 | Not measured | Only DeepSeek V4.1 Flash measured |
| Design Arena: ASCII art | 1265 | Not measured | Only DeepSeek V4.1 Flash measured |
| LiveBench design | 79.2% | 78.5% | DeepSeek V4.1 Flash |
GeneralLike-for-likePublic boards, shown as published, kept out of the index514
| Benchmark | DeepSeek V4.1 Flash | MiMo-V2.6-Pro | Leader |
|---|---|---|---|
| Vals Index | Not measured | 57.8% | Only MiMo-V2.6-Pro measured |
| LiveBench data analysis | 79.3% | 72.3% | DeepSeek V4.1 Flash |
| Public Benefits Bench (Vals AI) | 64.3% | 68.9% | MiMo-V2.6-Pro |
| SAGE (Vals AI) | 47.9% | 45.0% | DeepSeek V4.1 Flash |
| MedScribe (Vals AI) | 85.5% | 88.3% | MiMo-V2.6-Pro |
| Long context reasoning (AA-LCR) | 84.0% | 86.3% | MiMo-V2.6-Pro |
| DTBench | 89.9% | Not measured | Only DeepSeek V4.1 Flash measured |
| LMCA | 47.0% | Not measured | Only DeepSeek V4.1 Flash measured |
| Surface Evolver Bench | 46.3% | Not measured | Only DeepSeek V4.1 Flash measured |
| Furniture assembly | 34.2% | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text | 1464 | 1491 | MiMo-V2.6-Pro |
| LMArena Text: instruction following | 1473 | 1491 | MiMo-V2.6-Pro |
| LMArena Text: creative writing | 1437 | 1466 | MiMo-V2.6-Pro |
| LMArena Vision | 1278 | 1263 | DeepSeek V4.1 Flash |
| LMArena Text: English | 1473 | 1504 | MiMo-V2.6-Pro |
| LMArena Text: non-English | 1449 | 1474 | MiMo-V2.6-Pro |
| LMArena Text: Chinese | 1501 | 1529 | MiMo-V2.6-Pro |
| LMArena Text: German | 1470 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: Japanese | 1432 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: Korean | 1436 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: French | 1465 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: Spanish | 1459 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: Russian | 1467 | 1479 | MiMo-V2.6-Pro |
| LMArena Text: Polish | 1457 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: multi-turn | 1461 | 1466 | MiMo-V2.6-Pro |
| LMArena Text: longer queries | 1473 | 1501 | MiMo-V2.6-Pro |
| LMArena Text: writing and language | 1450 | 1470 | MiMo-V2.6-Pro |
| LMArena Text: entertainment and media | 1445 | 1465 | MiMo-V2.6-Pro |
| LMArena Vision: English | 1288 | 1268 | DeepSeek V4.1 Flash |
| LMArena Vision: Chinese | 1327 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Vision: reading text | 1282 | 1270 | DeepSeek V4.1 Flash |
| LMArena Vision: humor | 1260 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Vision: creative writing | 1275 | Not measured | Only DeepSeek V4.1 Flash measured |
| Neither model has a result on the other 13 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V4.1 Flash | 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
DeepSeek V4.1 Flash costs less
Repository review
50K input + 3K output tokens
DeepSeek V4.1 Flash costs less
Agent loop
200K input + 10K output tokens
DeepSeek V4.1 Flash 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 V4.1 Flash | Xiaomi MiMo-V2.6-Pro |
|---|---|---|
| Maker | DeepSeek | Xiaomi |
| Weights | Open weights | Open weights |
| Released | September 10, 2026 | September 21, 2026 |
| Context window | 1.05M | 1.05M |
| Input $/Mtok | $0.2225 | $0.435 |
| Output $/Mtok | $0.84 | $0.87 |
| Coding index | 62.5 | 65.4 |
| Independent measurements behind it | 5 | 4 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V4.1 Flash 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 62.5 for DeepSeek V4.1 Flash, though on 4 benchmarks against 5. Pick DeepSeek V4.1 Flash for output tokens 1.0x cheaper ($0.84 vs $0.87 per million) and work measured by LiveBench coding or Code migration (Vals AI), where it leads.
Which is cheaper, DeepSeek V4.1 Flash or MiMo-V2.6-Pro?
DeepSeek V4.1 Flash costs $0.2225 per million input tokens and $0.84 per million output tokens; MiMo-V2.6-Pro costs $0.435 and $0.87. Cost per solved task is known for neither of them.
Which has the bigger context window, DeepSeek V4.1 Flash or MiMo-V2.6-Pro?
MiMo-V2.6-Pro, with 1.05M tokens against 1.05M.
How many benchmarks is the DeepSeek V4.1 Flash vs MiMo-V2.6-Pro comparison based on?
70 benchmarks have a result for both models as of October 22, 2026. DeepSeek V4.1 Flash'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.