DeepSeek V4 Flash 0731 vs MiniMax M2.7
DeepSeek V4 Flash 0731 leads MiniMax M2.7 on coding, 51.8 to 36.2 on the Vibe Coding Index, and DeepSeek V4 Flash 0731 costs 4.3x less per output token ($0.28 vs $1.20 per million). Updated October 22, 2026; 36 benchmarks have a result for both.
DeepSeek V4 Flash 0731 or MiniMax M2.7: which to pick
Pick DeepSeek V4 Flash 0731 for coding: it scores 51.8 on the Vibe Coding Index against 36.2 for MiniMax M2.7.
Pick MiniMax M2.7 for work measured by LegalBench (Vals AI) or Design Arena: websites, where it leads.
- Shared results
- 36
- DeepSeek V4 Flash 0731 only
- 19
- MiniMax M2.7 only
- 38
- 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
DeepSeek V4 Flash 0731
DeepSeek V4 Flash 0731 scores 51.8 against 36.2.
4 and 3 benchmarks behind the scores
Intelligence
AA Intelligence Index
DeepSeek V4 Flash 0731
DeepSeek V4 Flash 0731 leads, 34.3 against 22.8.
Same board, both measured
Agentic work
AA Agentic Index
DeepSeek V4 Flash 0731
DeepSeek V4 Flash 0731 leads, 41.0 against 15.3.
Same board, both measured
Reasoning
GPQA Diamond
DeepSeek V4 Flash 0731
DeepSeek V4 Flash 0731 leads, 89.9% against 86.6%.
Same board, both measured
Math
AIME
No call
Only MiniMax M2.7 has a AIME result, 91.0%.
Directional only
Knowledge
MMLU Pro
DeepSeek V4 Flash 0731
DeepSeek V4 Flash 0731 leads, 86.2% against 80.4%.
Same board, both measured
Web design
Design Arena: websites
MiniMax M2.7
MiniMax M2.7 leads, 1242 against 1237.
Same board, both measured
Long documents
Listed context window
DeepSeek V4 Flash 0731
DeepSeek V4 Flash 0731 takes 1.05M tokens against 205K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V4 Flash 0731
DeepSeek V4 Flash 0731 $0.0003, MiniMax M2.7 $0.0009 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
DeepSeek V4 Flash 0731
DeepSeek V4 Flash 0731 $0.01, MiniMax M2.7 $0.64.
Published attempt cost and pass rate
At a glance
- Coding index
- DeepSeek V4 Flash 0731 51.8 from 4 benchmarks, MiniMax M2.7 36.2 from 3 benchmarks.
- Widest category
- Math, 53.0 points apart on average over 1 shared benchmark.
- Widest single gap
- Vibe Code Bench (Vals AI): DeepSeek V4 Flash 0731 74.7% against MiniMax M2.7 11.9%, 62.8 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V4 Flash 0731 $0.28 against MiniMax M2.7 $1.20 per million output tokens, 4.3x apart.
- Context window
- DeepSeek V4 Flash 0731 takes 1.05M tokens, MiniMax M2.7 205K.
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 Flash 0731 | Bars | MiniMax M2.7 | Ties |
|---|---|---|---|---|
| Coding | 9 | 0 | 1 tie | |
| Intelligence | 2 | 0 | ||
| Agentic work | 5 | 0 | ||
| Reasoning | 3 | 0 | ||
| Math | 1 | 0 | ||
| Knowledge | 4 | 1 | ||
| Web design | 3 | 5 | ||
| General | 2 | 0 | ||
| All shared boards | 29 | 6 | 1 tie |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index90
| Benchmark | DeepSeek V4 Flash 0731 | MiniMax M2.7 | Leader |
|---|---|---|---|
| LMArena WebDev | Not measured | 1399 | Only MiniMax M2.7 measured |
| AA Coding Index | 69.1 | 52.6 | DeepSeek V4 Flash 0731 |
| LiveCodeBench (Vals AI) | 87.3% | 79.9% | DeepSeek V4 Flash 0731 |
| SWE-bench Verified (Vals AI) | 88.8% | 73.8% | DeepSeek V4 Flash 0731 |
| Terminal-Bench 4 (Vals AI) | 18.7% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| Vibe Code Bench (Vals AI) | 74.7% | 11.9% | DeepSeek V4 Flash 0731 |
| Code migration (Vals AI) | 38.6% | 8.7% | DeepSeek V4 Flash 0731 |
| ProgramBench (Vals AI) | 0.0% | 0.0% | Tie |
| IOI (Vals AI) | 32.7% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| Terminal-Bench 2.1 (Vals AI) | 67.0% | 48.7% | DeepSeek V4 Flash 0731 |
| SkillsBench (Vals AI) | 50.7% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| CyberBench (Vals AI) | 72.1% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| SciCode | 50.3% | 50.1% | DeepSeek V4 Flash 0731 |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 39.4% | Only MiniMax M2.7 measured |
| Terminal-Bench | Not measured | 45.1% | Only MiniMax M2.7 measured |
| FrontierCode | 18.8% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| WeirdML | 63.0% | 37.0% | DeepSeek V4 Flash 0731 |
| ALE-Bench | 1306.1 | 599.3 | DeepSeek V4 Flash 0731 |
| LMArena Text: coding | Not measured | 1454 | Only MiniMax M2.7 measured |
| LMArena Text: software and IT | Not measured | 1446 | Only MiniMax M2.7 measured |
| LMArena WebDev: React | Not measured | 1388 | Only MiniMax M2.7 measured |
| LMArena WebDev: HTML | Not measured | 1401 | Only MiniMax M2.7 measured |
| Neither model has a result on the other 17 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | DeepSeek V4 Flash 0731 | MiniMax M2.7 | Leader |
|---|---|---|---|
| AA Intelligence Index | 34.3 | 22.8 | DeepSeek V4 Flash 0731 |
| Epoch Capabilities Index | 154.8 | 145.8 | DeepSeek V4 Flash 0731 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | DeepSeek V4 Flash 0731 | MiniMax M2.7 | Leader |
|---|---|---|---|
| AA Agentic Index | 41.0 | 15.3 | DeepSeek V4 Flash 0731 |
| Finance Agent (Vals AI) | 49.5% | 27.9% | DeepSeek V4 Flash 0731 |
| Tax Agent Bench (Vals AI) | 28.1% | 6.9% | DeepSeek V4 Flash 0731 |
| Legal Research Bench (Vals AI) | 30.3% | 10.6% | DeepSeek V4 Flash 0731 |
| Excel Modeling Benchmark (Vals AI) | 57.0% | 28.4% | DeepSeek V4 Flash 0731 |
| BioMysteryBench (Vals AI) | 64.4% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| Terminal-Bench-Science (Vals AI) | 4.3% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| τ²-Bench Telecom | Not measured | 84.8% | Only MiniMax M2.7 measured |
| Neither model has a result on the other 33 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | DeepSeek V4 Flash 0731 | MiniMax M2.7 | Leader |
|---|---|---|---|
| GPQA Diamond | 89.9% | 86.6% | DeepSeek V4 Flash 0731 |
| GPQA Diamond (Artificial Analysis run) | 90.8% | 87.4% | DeepSeek V4 Flash 0731 |
| Humanity's Last Exam (Artificial Analysis run) | 38.6% | 29.6% | DeepSeek V4 Flash 0731 |
| GPQA Diamond (Epoch AI run) | 91.0% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| ARC-AGI | 89.0% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| ARC-AGI-2 | 61.4% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| SimpleBench | 61.1% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| Chess puzzles | 32.0% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| Mystery game puzzles | 34.0% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| LMArena Text: hard prompts | Not measured | 1422 | Only MiniMax M2.7 measured |
| LMArena Text: expert | Not measured | 1443 | Only MiniMax M2.7 measured |
| LMArena Text: hard prompts in English | Not measured | 1441 | Only MiniMax M2.7 measured |
| Neither model has a result on the other 10 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V4 Flash 0731 | MiniMax M2.7 | Leader |
|---|---|---|---|
| AIME | Not measured | 91.0% | Only MiniMax M2.7 measured |
| OTIS Mock AIME | 94.4% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| FrontierMath, tiers 1 to 3 | 57.5% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| FrontierMath, tier 4 | 24.4% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| ProofBench | 56.0% | 3.0% | DeepSeek V4 Flash 0731 |
| LMArena Text: math | Not measured | 1421 | Only MiniMax M2.7 measured |
| LMArena Text: mathematical work | Not measured | 1437 | Only MiniMax M2.7 measured |
| Neither model has a result on the other 6 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index41
| Benchmark | DeepSeek V4 Flash 0731 | MiniMax M2.7 | Leader |
|---|---|---|---|
| MMLU Pro | 86.2% | 80.4% | DeepSeek V4 Flash 0731 |
| LegalBench (Vals AI) | 77.7% | 84.0% | MiniMax M2.7 |
| CaseLaw (Vals AI) | Not measured | 60.9% | Only MiniMax M2.7 measured |
| CorpFin (Vals AI) | 61.9% | 61.2% | DeepSeek V4 Flash 0731 |
| TaxEval (Vals AI) | 70.7% | 66.6% | DeepSeek V4 Flash 0731 |
| MedCode (Vals AI) | 41.4% | 34.4% | DeepSeek V4 Flash 0731 |
| SimpleQA Verified | 33.6% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| LMArena Text: science | Not measured | 1416 | Only MiniMax M2.7 measured |
| LMArena Text: medicine and healthcare | Not measured | 1409 | Only MiniMax M2.7 measured |
| LMArena Text: legal and government | Not measured | 1403 | Only MiniMax M2.7 measured |
| LMArena Text: business and finance | Not measured | 1407 | Only MiniMax M2.7 measured |
| Neither model has a result on the other 7 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index35
| Benchmark | DeepSeek V4 Flash 0731 | MiniMax M2.7 | Leader |
|---|---|---|---|
| Design Arena: websites | 1237 | 1242 | MiniMax M2.7 |
| Design Arena: UI components | 1233 | 1207 | DeepSeek V4 Flash 0731 |
| Design Arena: game dev | 1207 | 1209 | MiniMax M2.7 |
| Design Arena: data viz | 1180 | 1233 | MiniMax M2.7 |
| Design Arena: 3D | 1208 | 1194 | DeepSeek V4 Flash 0731 |
| Design Arena: all code categories | 1229 | 1233 | MiniMax M2.7 |
| Design Arena: SVG | 1183 | 1137 | DeepSeek V4 Flash 0731 |
| Design Arena: ASCII art | 1090 | 1136 | MiniMax M2.7 |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | DeepSeek V4 Flash 0731 | MiniMax M2.7 | Leader |
|---|---|---|---|
| MedScribe (Vals AI) | 80.4% | 79.9% | DeepSeek V4 Flash 0731 |
| IFBench | Not measured | 75.7% | Only MiniMax M2.7 measured |
| Long context reasoning (AA-LCR) | 79.7% | 78.3% | DeepSeek V4 Flash 0731 |
| DTBench | 90.9% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| LMCA | 41.7% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| ExploitBench | Not measured | 13.3% | Only MiniMax M2.7 measured |
| LMArena Text | Not measured | 1405 | Only MiniMax M2.7 measured |
| LMArena Text: instruction following | Not measured | 1405 | Only MiniMax M2.7 measured |
| LMArena Text: creative writing | Not measured | 1354 | Only MiniMax M2.7 measured |
| LMArena Text: English | Not measured | 1426 | Only MiniMax M2.7 measured |
| LMArena Text: non-English | Not measured | 1382 | Only MiniMax M2.7 measured |
| LMArena Text: Chinese | Not measured | 1442 | Only MiniMax M2.7 measured |
| LMArena Text: German | Not measured | 1398 | Only MiniMax M2.7 measured |
| LMArena Text: Japanese | Not measured | 1260 | Only MiniMax M2.7 measured |
| LMArena Text: Korean | Not measured | 1314 | Only MiniMax M2.7 measured |
| LMArena Text: French | Not measured | 1424 | Only MiniMax M2.7 measured |
| LMArena Text: Spanish | Not measured | 1402 | Only MiniMax M2.7 measured |
| LMArena Text: Russian | Not measured | 1383 | Only MiniMax M2.7 measured |
| LMArena Text: Polish | Not measured | 1387 | Only MiniMax M2.7 measured |
| LMArena Text: multi-turn | Not measured | 1412 | Only MiniMax M2.7 measured |
| LMArena Text: longer queries | Not measured | 1418 | Only MiniMax M2.7 measured |
| LMArena Text: writing and language | Not measured | 1376 | Only MiniMax M2.7 measured |
| LMArena Text: entertainment and media | Not measured | 1360 | Only MiniMax M2.7 measured |
| Neither model has a result on the other 23 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V4 Flash 0731 | MiniMax M2.7 | 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 Flash 0731 costs less
Repository review
50K input + 3K output tokens
DeepSeek V4 Flash 0731 costs less
Agent loop
200K input + 10K output tokens
DeepSeek V4 Flash 0731 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 Flash 0731 | MiniMax M2.7 |
|---|---|---|
| Maker | DeepSeek | MiniMax |
| Weights | Open weights | Open weights |
| Released | July 31, 2026 | March 18, 2026 |
| Context window | 1.05M | 205K |
| Input $/Mtok | $0.14 | $0.30 |
| Output $/Mtok | $0.28 | $1.20 |
| Coding index | 51.8 | 36.2 |
| Independent measurements behind it | 4 | 3 |
| Cost per solved task | $0.01 | $0.64 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V4 Flash 0731 better than MiniMax M2.7 for coding?
Pick DeepSeek V4 Flash 0731 for coding: it scores 51.8 on the Vibe Coding Index against 36.2 for MiniMax M2.7. Pick MiniMax M2.7 for work measured by LegalBench (Vals AI) or Design Arena: websites, where it leads.
Which is cheaper, DeepSeek V4 Flash 0731 or MiniMax M2.7?
DeepSeek V4 Flash 0731 costs $0.14 per million input tokens and $0.28 per million output tokens; MiniMax M2.7 costs $0.30 and $1.20. Per solved SWE-bench Verified task, DeepSeek V4 Flash 0731 costs $0.01 and MiniMax M2.7 $0.64.
Which has the bigger context window, DeepSeek V4 Flash 0731 or MiniMax M2.7?
DeepSeek V4 Flash 0731, with 1.05M tokens against 205K.
How many benchmarks is the DeepSeek V4 Flash 0731 vs MiniMax M2.7 comparison based on?
36 benchmarks have a result for both models as of October 22, 2026. DeepSeek V4 Flash 0731's coding index rests on 4 independent benchmarks and MiniMax M2.7's on 3. Where only one model has a result, the page says so instead of naming a winner.