MiniMax M2.1 vs Qwen3 14B
MiniMax M2.1 leads Qwen3 14B on the Artificial Analysis Intelligence Index, 20.9 to 6.7, and Qwen3 14B costs 5.2x less per output token ($0.23 vs $1.20 per million). Updated October 22, 2026; 11 benchmarks have a result for both.
MiniMax M2.1 or Qwen3 14B: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick MiniMax M2.1 for a larger context window (205K against 41K tokens).
- Shared results
- 11
- MiniMax M2.1 only
- 23
- Qwen3 14B only
- 9
- Like-for-like categories
- 7 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
MiniMax M2.1
MiniMax M2.1 leads, 20.9 against 6.7.
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
No call
Only MiniMax M2.1 has a Design Arena: websites result, 1201.
Directional only
Long documents
Listed context window
MiniMax M2.1
MiniMax M2.1 takes 205K tokens against 41K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3 14B
MiniMax M2.1 $0.0009, Qwen3 14B $0.0002 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, Qwen3 14B not benchmarked.
- Widest category
- Agentic work, 50.9 points apart on average over 1 shared benchmark.
- Widest single gap
- Long context reasoning (AA-LCR): MiniMax M2.1 67.7% against Qwen3 14B 0.0%, 67.7 points apart on a 0 to 100 scale.
- Output price
- MiniMax M2.1 $1.20 against Qwen3 14B $0.23 per million output tokens, 5.2x apart.
- Context window
- MiniMax M2.1 takes 205K tokens, Qwen3 14B 41K.
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 14B | Ties |
|---|---|---|---|---|
| Coding | 2 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 2 | 0 | ||
| Math | 2 | 0 | ||
| Knowledge | 1 | 0 | ||
| General | 2 | 0 | ||
| All shared boards | 11 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index20
| Benchmark | MiniMax M2.1 | Qwen3 14B | Leader |
|---|---|---|---|
| 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% | 52.3% | MiniMax M2.1 |
| SciCode | Not measured | 30.7% | Only Qwen3 14B measured |
| Terminal-Bench Hard (Artificial Analysis run) | 28.8% | 5.3% | MiniMax M2.1 |
| 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 36 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | MiniMax M2.1 | Qwen3 14B | Leader |
|---|---|---|---|
| AA Intelligence Index | 20.9 | 6.7 | MiniMax M2.1 |
| Epoch Capabilities Index | Not measured | 138.2 | Only Qwen3 14B measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | MiniMax M2.1 | Qwen3 14B | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 85.4% | 34.5% | MiniMax M2.1 |
| Berkeley Function Calling Leaderboard (BFCL) | Not measured | 41.0% | Only Qwen3 14B measured |
| Neither model has a result on the other 40 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | MiniMax M2.1 | Qwen3 14B | Leader |
|---|---|---|---|
| GPQA Diamond | 78.5% | Not measured | Only MiniMax M2.1 measured |
| GPQA Diamond (Artificial Analysis run) | 83.0% | 60.4% | MiniMax M2.1 |
| Humanity's Last Exam (Artificial Analysis run) | 23.2% | 4.5% | MiniMax M2.1 |
| GPQA Diamond (Epoch AI run) | Not measured | 63.8% | Only Qwen3 14B measured |
| Kagi LLM Benchmark | Not measured | 49.1% | Only Qwen3 14B measured |
| Neither model has a result on the other 18 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | MiniMax M2.1 | Qwen3 14B | 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 | 58.0 | MiniMax M2.1 |
| AIME 2025 (Artificial Analysis run) | 82.7% | 58.0% | MiniMax M2.1 |
| OTIS Mock AIME | Not measured | 66.4% | Only Qwen3 14B measured |
| Neither model has a result on the other 8 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | MiniMax M2.1 | Qwen3 14B | 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% | 77.4% | MiniMax M2.1 |
| Neither model has a result on the other 12 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | MiniMax M2.1 | Qwen3 14B | 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 index20
| Benchmark | MiniMax M2.1 | Qwen3 14B | Leader |
|---|---|---|---|
| MedScribe (Vals AI) | 80.8% | Not measured | Only MiniMax M2.1 measured |
| IFBench | 69.9% | 40.5% | MiniMax M2.1 |
| Long context reasoning (AA-LCR) | 67.7% | 0.0% | MiniMax M2.1 |
| DTBench | Not measured | 64.0% | Only Qwen3 14B measured |
| LMCA | Not measured | 18.2% | Only Qwen3 14B measured |
| Fiction.LiveBench | Not measured | 62.5% | Only Qwen3 14B measured |
| Neither model has a result on the other 42 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | MiniMax M2.1 | Qwen3 14B | 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
Qwen3 14B costs less
Repository review
50K input + 3K output tokens
Qwen3 14B costs less
Agent loop
200K input + 10K output tokens
Qwen3 14B 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 14B |
|---|---|---|
| Maker | MiniMax | Qwen |
| Weights | Open weights | Open weights |
| Released | December 23, 2025 | April 28, 2025 |
| Context window | 205K | 41K |
| Input $/Mtok | $0.30 | $0.11 |
| Output $/Mtok | $1.20 | $0.23 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 1 | 0 |
| Cost per solved task | $0.59 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is MiniMax M2.1 better than Qwen3 14B for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick MiniMax M2.1 for a larger context window (205K against 41K tokens).
Which is cheaper, MiniMax M2.1 or Qwen3 14B?
MiniMax M2.1 costs $0.30 per million input tokens and $1.20 per million output tokens; Qwen3 14B costs $0.11 and $0.23. Cost per solved task is known for MiniMax M2.1 only.
Which has the bigger context window, MiniMax M2.1 or Qwen3 14B?
MiniMax M2.1, with 205K tokens against 41K.
How many benchmarks is the MiniMax M2.1 vs Qwen3 14B 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 14B's on 0. Where only one model has a result, the page says so instead of naming a winner.