MiniMax M2.1 vs Qwen3.6 27B
Qwen3.6 27B leads MiniMax M2.1 on the Artificial Analysis Intelligence Index, 21.4 to 20.9, and MiniMax M2.1 costs 2.3x less per output token ($1.20 vs $2.70 per million). Updated October 22, 2026; 11 benchmarks have a result for both.
MiniMax M2.1 or Qwen3.6 27B: which to pick
Only Qwen3.6 27B has a coding score, 36.8 on the Vibe Coding Index; MiniMax M2.1 has none, so the two cannot be ranked on coding.
Pick MiniMax M2.1 for output tokens 2.3x cheaper ($1.20 vs $2.70 per million) and a lower cost per solved task ($0.59 against $1.30).
- Shared results
- 11
- MiniMax M2.1 only
- 23
- Qwen3.6 27B only
- 14
- Like-for-like categories
- 6 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
Only Qwen3.6 27B has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
Qwen3.6 27B
Qwen3.6 27B leads, 21.4 against 20.9.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Qwen3.6 27B has a AA Agentic Index result, 18.5.
Directional only
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
No call
Only MiniMax M2.1 has a Design Arena: websites result, 1201.
Directional only
Long documents
Listed context window
Qwen3.6 27B
Qwen3.6 27B takes 262K tokens against 205K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
MiniMax M2.1
MiniMax M2.1 $0.0009, Qwen3.6 27B $0.0018 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
MiniMax M2.1
MiniMax M2.1 $0.59, Qwen3.6 27B $1.30.
Published attempt cost and pass rate
At a glance
- Coding index
- MiniMax M2.1 not benchmarked, Qwen3.6 27B 36.8 from 2 benchmarks.
- Widest category
- Agentic work, 8.8 points apart on average over 1 shared benchmark.
- Widest single gap
- Long context reasoning (AA-LCR): MiniMax M2.1 67.7% against Qwen3.6 27B 77.3%, 9.7 points apart on a 0 to 100 scale.
- Output price
- MiniMax M2.1 $1.20 against Qwen3.6 27B $2.70 per million output tokens, 2.3x apart.
- Context window
- MiniMax M2.1 takes 205K tokens, Qwen3.6 27B 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 | MiniMax M2.1 | Bars | Qwen3.6 27B | Ties |
|---|---|---|---|---|
| Coding | 1 | 1 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 1 | 1 | ||
| Knowledge | 1 | 2 | ||
| General | 1 | 1 | ||
| All shared boards | 4 | 7 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index11
| Benchmark | MiniMax M2.1 | Qwen3.6 27B | Leader |
|---|---|---|---|
| AA Coding Index | Not measured | 53.7 | Only Qwen3.6 27B measured |
| LiveCodeBench (Vals AI) | 81.8% | Not measured | Only MiniMax M2.1 measured |
| SWE-bench Verified (Vals AI) | 74.8% | 70.0% | MiniMax M2.1 |
| Vibe Code Bench (Vals AI) | Not measured | 11.9% | Only Qwen3.6 27B measured |
| LiveCodeBench (Artificial Analysis run) | 81.0% | Not measured | Only MiniMax M2.1 measured |
| SciCode | Not measured | 42.8% | Only Qwen3.6 27B measured |
| Terminal-Bench Hard (Artificial Analysis run) | 28.8% | 34.8% | Qwen3.6 27B |
| 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 34 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | MiniMax M2.1 | Qwen3.6 27B | Leader |
|---|---|---|---|
| AA Intelligence Index | 20.9 | 21.4 | Qwen3.6 27B |
| Epoch Capabilities Index | Not measured | 146.2 | Only Qwen3.6 27B measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | MiniMax M2.1 | Qwen3.6 27B | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 18.5 | Only Qwen3.6 27B measured |
| τ²-Bench Telecom | 85.4% | 94.2% | Qwen3.6 27B |
| Neither model has a result on the other 40 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | MiniMax M2.1 | Qwen3.6 27B | Leader |
|---|---|---|---|
| GPQA Diamond | 78.5% | Not measured | Only MiniMax M2.1 measured |
| GPQA Diamond (Artificial Analysis run) | 83.0% | 84.2% | Qwen3.6 27B |
| Humanity's Last Exam (Artificial Analysis run) | 23.2% | 23.1% | MiniMax M2.1 |
| GPQA Diamond (Epoch AI run) | Not measured | 85.9% | Only Qwen3.6 27B measured |
| Mystery game puzzles | Not measured | 7.0% | Only Qwen3.6 27B measured |
| Neither model has a result on the other 18 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | MiniMax M2.1 | Qwen3.6 27B | 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 |
| OTIS Mock AIME | Not measured | 91.1% | Only Qwen3.6 27B measured |
| FrontierMath, tiers 1 to 3 | Not measured | 35.1% | Only Qwen3.6 27B measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index12
| Benchmark | MiniMax M2.1 | Qwen3.6 27B | 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% | 53.2% | MiniMax M2.1 |
| CorpFin (Vals AI) | 58.9% | 62.3% | Qwen3.6 27B |
| TaxEval (Vals AI) | 66.3% | 71.3% | Qwen3.6 27B |
| MortgageTax (Vals AI) | Not measured | 68.3% | Only Qwen3.6 27B 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 11 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | MiniMax M2.1 | Qwen3.6 27B | Leader |
|---|---|---|---|
| Design Arena: websites | 1201 | Not measured | Only MiniMax M2.1 measured |
| Design Arena: UI components | 1220 | Not measured | Only MiniMax M2.1 measured |
| Design Arena: game dev | 1134 | Not measured | Only MiniMax M2.1 measured |
| Design Arena: data viz | 1206 | Not measured | Only MiniMax M2.1 measured |
| Design Arena: 3D | 1164 | Not measured | Only MiniMax M2.1 measured |
| Design Arena: all code categories | 1190 | Not measured | Only MiniMax M2.1 measured |
| Design Arena: SVG | 1131 | Not measured | Only MiniMax M2.1 measured |
| Neither model has a result on the other 2 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | MiniMax M2.1 | Qwen3.6 27B | Leader |
|---|---|---|---|
| SAGE (Vals AI) | Not measured | 45.6% | Only Qwen3.6 27B measured |
| MedScribe (Vals AI) | 80.8% | Not measured | Only MiniMax M2.1 measured |
| IFBench | 69.9% | 67.6% | MiniMax M2.1 |
| Long context reasoning (AA-LCR) | 67.7% | 77.3% | Qwen3.6 27B |
| DTBench | Not measured | 78.1% | Only Qwen3.6 27B measured |
| LMCA | Not measured | 34.5% | Only Qwen3.6 27B measured |
| EQ-Bench 4 | Not measured | 1026 | Only Qwen3.6 27B measured |
| Neither model has a result on the other 41 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | MiniMax M2.1 | Qwen3.6 27B | 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
MiniMax M2.1 costs less
Repository review
50K input + 3K output tokens
MiniMax M2.1 costs less
Agent loop
200K input + 10K output tokens
MiniMax M2.1 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 | Qwen3.6 27B |
|---|---|---|
| Maker | MiniMax | Qwen |
| Weights | Open weights | Open weights |
| Released | December 23, 2025 | April 27, 2026 |
| Context window | 205K | 262K |
| Input $/Mtok | $0.30 | $0.45 |
| Output $/Mtok | $1.20 | $2.70 |
| Coding index | Not benchmarked | 36.8 |
| Independent measurements behind it | 1 | 2 |
| Cost per solved task | $0.59 | $1.30 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is MiniMax M2.1 better than Qwen3.6 27B for coding?
Only Qwen3.6 27B has a coding score, 36.8 on the Vibe Coding Index; MiniMax M2.1 has none, so the two cannot be ranked on coding. Pick MiniMax M2.1 for output tokens 2.3x cheaper ($1.20 vs $2.70 per million) and a lower cost per solved task ($0.59 against $1.30).
Which is cheaper, MiniMax M2.1 or Qwen3.6 27B?
MiniMax M2.1 costs $0.30 per million input tokens and $1.20 per million output tokens; Qwen3.6 27B costs $0.45 and $2.70. Per solved SWE-bench Verified task, MiniMax M2.1 costs $0.59 and Qwen3.6 27B $1.30.
Which has the bigger context window, MiniMax M2.1 or Qwen3.6 27B?
Qwen3.6 27B, with 262K tokens against 205K.
How many benchmarks is the MiniMax M2.1 vs Qwen3.6 27B comparison based on?
11 benchmarks have a result for both models as of October 22, 2026. MiniMax M2.1's coding index rests on 1 independent benchmark and Qwen3.6 27B's on 2. Where only one model has a result, the page says so instead of naming a winner.