MiniMax M2.1 vs Nemotron 3.5 Super VL
Nemotron 3.5 Super VL leads MiniMax M2.1 on the Artificial Analysis Intelligence Index, 26.5 to 20.9, and Nemotron 3.5 Super VL costs 2.4x less per output token ($0.50 vs $1.20 per million). Updated October 23, 2026; 8 benchmarks have a result for both.
MiniMax M2.1 or Nemotron 3.5 Super VL: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick MiniMax M2.1 for work measured by AA Intelligence Index or Humanity's Last Exam (Artificial Analysis run), where it leads.
- Shared results
- 8
- MiniMax M2.1 only
- 26
- Nemotron 3.5 Super VL only
- 8
- Like-for-like categories
- 4 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
No call
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
Nemotron 3.5 Super VL
Nemotron 3.5 Super VL leads, 26.5 against 20.9.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only MiniMax M2.1 has a GPQA Diamond result, 78.5%.
Directional only
Math
AIME
No call
Only MiniMax M2.1 has a AIME result, 77.9%.
Directional only
Knowledge
MMLU Pro
No call
Only MiniMax M2.1 has a MMLU Pro result, 87.0%.
Directional only
Web design
Design Arena: websites
MiniMax M2.1
MiniMax M2.1 leads, 1201 against 1172.
Same board, both measured
Long documents
Listed context window
Nemotron 3.5 Super VL
Nemotron 3.5 Super VL takes 1.05M tokens against 205K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Nemotron 3.5 Super VL
MiniMax M2.1 $0.0009, Nemotron 3.5 Super VL $0.0004 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only MiniMax M2.1 has the number, $0.59.
Directional only
At a glance
- Coding index
- MiniMax M2.1 not benchmarked, Nemotron 3.5 Super VL not benchmarked.
- Widest category
- Reasoning, 7.5 points apart on average over 1 shared benchmark.
- Widest single gap
- Design Arena: UI components: MiniMax M2.1 1220 against Nemotron 3.5 Super VL 1148, 9.0 points apart on a 0 to 100 scale.
- Output price
- MiniMax M2.1 $1.20 against Nemotron 3.5 Super VL $0.50 per million output tokens, 2.4x apart.
- Context window
- MiniMax M2.1 takes 205K tokens, Nemotron 3.5 Super VL 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 | MiniMax M2.1 | Bars | Nemotron 3.5 Super VL | Ties |
|---|---|---|---|---|
| Intelligence | 0 | 1 | ||
| Reasoning | 0 | 1 | ||
| Web design | 5 | 0 | ||
| General | 0 | 1 | ||
| All shared boards | 5 | 3 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index8 boards, none shared
| Benchmark | MiniMax M2.1 | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| LiveBench coding | Not measured | 65.5% | Only Nemotron 3.5 Super VL measured |
| LiveCodeBench (Vals AI) | 81.8% | Not measured | Only MiniMax M2.1 measured |
| SWE-bench Verified (Vals AI) | 74.8% | Not measured | Only MiniMax M2.1 measured |
| LiveCodeBench (Artificial Analysis run) | 81.0% | Not measured | Only MiniMax M2.1 measured |
| SciCode | Not measured | 47.8% | Only Nemotron 3.5 Super VL measured |
| Terminal-Bench Hard (Artificial Analysis run) | 28.8% | Not measured | Only MiniMax M2.1 measured |
| Terminal-Bench | 36.6% | Not measured | Only MiniMax M2.1 measured |
| ALE-Bench | 623.8 | Not measured | Only MiniMax M2.1 measured |
| Neither model has a result on the other 35 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | MiniMax M2.1 | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| AA Intelligence Index | 20.9 | 26.5 | Nemotron 3.5 Super VL |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | MiniMax M2.1 | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| LiveBench research | Not measured | 70.9% | Only Nemotron 3.5 Super VL measured |
| LiveBench browser | Not measured | 4.0% | Only Nemotron 3.5 Super VL measured |
| LiveBench long agentic | Not measured | 15.8% | Only Nemotron 3.5 Super VL measured |
| τ²-Bench Telecom | 85.4% | Not measured | Only MiniMax M2.1 measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | MiniMax M2.1 | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| GPQA Diamond | 78.5% | Not measured | Only MiniMax M2.1 measured |
| LiveBench reasoning | Not measured | 78.8% | Only Nemotron 3.5 Super VL measured |
| GPQA Diamond (Artificial Analysis run) | 83.0% | Not measured | Only MiniMax M2.1 measured |
| Humanity's Last Exam (Artificial Analysis run) | 23.2% | 30.7% | Nemotron 3.5 Super VL |
| Neither model has a result on the other 19 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | MiniMax M2.1 | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| AIME | 77.9% | Not measured | Only MiniMax M2.1 measured |
| MATH 500 (Vals AI) | 89.0% | Not measured | Only MiniMax M2.1 measured |
| MGSM (Vals AI) | 87.8% | Not measured | Only MiniMax M2.1 measured |
| AA Math Index | 82.7 | Not measured | Only MiniMax M2.1 measured |
| AIME 2025 (Artificial Analysis run) | 82.7% | Not measured | Only MiniMax M2.1 measured |
| Neither model has a result on the other 9 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | MiniMax M2.1 | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| MMLU Pro | 87.0% | Not measured | Only MiniMax M2.1 measured |
| LegalBench (Vals AI) | 75.5% | Not measured | Only MiniMax M2.1 measured |
| CaseLaw (Vals AI) | 55.8% | Not measured | Only MiniMax M2.1 measured |
| CorpFin (Vals AI) | 58.9% | Not measured | Only MiniMax M2.1 measured |
| TaxEval (Vals AI) | 66.3% | Not measured | Only MiniMax M2.1 measured |
| MedQA (Vals AI) | 91.2% | Not measured | Only MiniMax M2.1 measured |
| MedCode (Vals AI) | 34.1% | Not measured | Only MiniMax M2.1 measured |
| MMLU Pro (Artificial Analysis run) | 87.5% | Not measured | Only MiniMax M2.1 measured |
| Neither model has a result on the other 12 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | MiniMax M2.1 | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| Design Arena: websites | 1201 | 1172 | MiniMax M2.1 |
| Design Arena: UI components | 1220 | 1148 | MiniMax M2.1 |
| Design Arena: game dev | 1134 | 1110 | MiniMax M2.1 |
| Design Arena: data viz | 1207 | Not measured | Only MiniMax M2.1 measured |
| Design Arena: 3D | 1166 | 1127 | MiniMax M2.1 |
| Design Arena: all code categories | 1190 | 1154 | MiniMax M2.1 |
| Design Arena: SVG | 1131 | Not measured | Only MiniMax M2.1 measured |
| LiveBench design | Not measured | 60.7% | Only Nemotron 3.5 Super VL measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | MiniMax M2.1 | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| LiveBench data analysis | Not measured | 64.6% | Only Nemotron 3.5 Super VL measured |
| MedScribe (Vals AI) | 80.8% | Not measured | Only MiniMax M2.1 measured |
| IFBench | 69.9% | Not measured | Only MiniMax M2.1 measured |
| Long context reasoning (AA-LCR) | 67.7% | 69.0% | Nemotron 3.5 Super VL |
| Neither model has a result on the other 44 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | MiniMax M2.1 | Nemotron 3.5 Super VL | 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
Nemotron 3.5 Super VL costs less
Repository review
50K input + 3K output tokens
Nemotron 3.5 Super VL costs less
Agent loop
200K input + 10K output tokens
Nemotron 3.5 Super VL 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 | MiniMax M2.1 | NVIDIA Nemotron 3.5 Super VL |
|---|---|---|
| Maker | MiniMax | NVIDIA |
| Weights | Open weights | Open weights |
| Released | December 23, 2025 | October 15, 2026 |
| Context window | 205K | 1.05M |
| Input $/Mtok | $0.30 | $0.15 |
| Output $/Mtok | $1.20 | $0.50 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 1 | 1 |
| Cost per solved task | $0.59 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is MiniMax M2.1 better than Nemotron 3.5 Super VL for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick MiniMax M2.1 for work measured by AA Intelligence Index or Humanity's Last Exam (Artificial Analysis run), where it leads.
Which is cheaper, MiniMax M2.1 or Nemotron 3.5 Super VL?
MiniMax M2.1 costs $0.30 per million input tokens and $1.20 per million output tokens; Nemotron 3.5 Super VL costs $0.15 and $0.50. Cost per solved task is known for MiniMax M2.1 only.
Which has the bigger context window, MiniMax M2.1 or Nemotron 3.5 Super VL?
Nemotron 3.5 Super VL, with 1.05M tokens against 205K.
How many benchmarks is the MiniMax M2.1 vs Nemotron 3.5 Super VL comparison based on?
8 benchmarks have a result for both models as of October 23, 2026. MiniMax M2.1's coding index rests on 1 independent benchmark and Nemotron 3.5 Super VL's on 1. Where only one model has a result, the page says so instead of naming a winner.