Llama 3.2 1B Instruct vs MiniMax M2.1
MiniMax M2.1 leads Llama 3.2 1B Instruct on the Artificial Analysis Intelligence Index, 20.9 to 4.8, and Llama 3.2 1B Instruct costs 6.0x less per output token ($0.201 vs $1.20 per million). Updated October 22, 2026; 11 benchmarks have a result for both.
Llama 3.2 1B Instruct or MiniMax M2.1: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Llama 3.2 1B Instruct for output tokens 6.0x cheaper ($0.201 vs $1.20 per million) and work measured by LiveCodeBench (Artificial Analysis run) or Terminal-Bench Hard (Artificial Analysis run), where it leads.
- Shared results
- 11
- Llama 3.2 1B Instruct only
- 27
- MiniMax M2.1 only
- 23
- 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 4.8.
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 60K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Llama 3.2 1B Instruct
Llama 3.2 1B Instruct $0.0001, MiniMax M2.1 $0.0009 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
- Llama 3.2 1B Instruct not benchmarked, MiniMax M2.1 not benchmarked.
- Widest category
- Agentic work, 85.4 points apart on average over 1 shared benchmark.
- Widest single gap
- τ²-Bench Telecom: Llama 3.2 1B Instruct 0.0% against MiniMax M2.1 85.4%, 85.4 points apart on a 0 to 100 scale.
- Output price
- Llama 3.2 1B Instruct $0.201 against MiniMax M2.1 $1.20 per million output tokens, 6.0x apart.
- Context window
- Llama 3.2 1B Instruct takes 60K tokens, MiniMax M2.1 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 | Llama 3.2 1B Instruct | Bars | MiniMax M2.1 | Ties |
|---|---|---|---|---|
| Coding | 0 | 2 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 2 | ||
| Math | 0 | 2 | ||
| Knowledge | 0 | 1 | ||
| General | 0 | 2 | ||
| All shared boards | 0 | 11 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
| Benchmark | Llama 3.2 1B Instruct | MiniMax M2.1 | Leader |
|---|---|---|---|
| LiveCodeBench (Vals AI) | Not measured | 81.8% | Only MiniMax M2.1 measured |
| SWE-bench Verified (Vals AI) | Not measured | 74.8% | Only MiniMax M2.1 measured |
| LiveCodeBench (Artificial Analysis run) | 1.9% | 81.0% | MiniMax M2.1 |
| Terminal-Bench Hard (Artificial Analysis run) | 0.0% | 28.8% | MiniMax M2.1 |
| Terminal-Bench | Not measured | 36.6% | Only MiniMax M2.1 measured |
| ALE-Bench | Not measured | 623.8 | Only MiniMax M2.1 measured |
| LMArena Text: coding | 1070 | Not measured | Only Llama 3.2 1B Instruct measured |
| LMArena Text: software and IT | 1056 | Not measured | Only Llama 3.2 1B Instruct measured |
| Neither model has a result on the other 31 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Llama 3.2 1B Instruct | MiniMax M2.1 | Leader |
|---|---|---|---|
| AA Intelligence Index | 4.8 | 20.9 | MiniMax M2.1 |
| Neither model has a result on the other 1 board here | |||
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Llama 3.2 1B Instruct | MiniMax M2.1 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 0.0% | 85.4% | MiniMax M2.1 |
| BALROG | 6.6% | Not measured | Only Llama 3.2 1B Instruct measured |
| Berkeley Function Calling Leaderboard (BFCL) | 10.8% | Not measured | Only Llama 3.2 1B Instruct measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Llama 3.2 1B Instruct | MiniMax M2.1 | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 78.5% | Only MiniMax M2.1 measured |
| GPQA Diamond (Artificial Analysis run) | 19.6% | 83.0% | MiniMax M2.1 |
| Humanity's Last Exam (Artificial Analysis run) | 5.5% | 23.2% | MiniMax M2.1 |
| GPQA Diamond (Epoch AI run) | 23.9% | Not measured | Only Llama 3.2 1B Instruct measured |
| LMArena Text: hard prompts | 1044 | Not measured | Only Llama 3.2 1B Instruct measured |
| LMArena Text: expert | 1007 | Not measured | Only Llama 3.2 1B Instruct measured |
| LMArena Text: hard prompts in English | 1087 | Not measured | Only Llama 3.2 1B Instruct measured |
| Neither model has a result on the other 15 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Llama 3.2 1B Instruct | MiniMax M2.1 | Leader |
|---|---|---|---|
| AIME | Not measured | 77.9% | Only MiniMax M2.1 measured |
| MATH 500 (Vals AI) | Not measured | 89.0% | Only MiniMax M2.1 measured |
| MGSM (Vals AI) | Not measured | 87.8% | Only MiniMax M2.1 measured |
| AA Math Index | 0.0 | 82.7 | MiniMax M2.1 |
| AIME 2025 (Artificial Analysis run) | 0.0% | 82.7% | MiniMax M2.1 |
| OTIS Mock AIME | 0.6% | Not measured | Only Llama 3.2 1B Instruct measured |
| LMArena Text: math | 1086 | Not measured | Only Llama 3.2 1B Instruct measured |
| LMArena Text: mathematical work | 1098 | Not measured | Only Llama 3.2 1B Instruct measured |
| Neither model has a result on the other 5 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Llama 3.2 1B Instruct | MiniMax M2.1 | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 87.0% | Only MiniMax M2.1 measured |
| LegalBench (Vals AI) | Not measured | 75.5% | Only MiniMax M2.1 measured |
| CaseLaw (Vals AI) | Not measured | 55.8% | Only MiniMax M2.1 measured |
| CorpFin (Vals AI) | Not measured | 58.9% | Only MiniMax M2.1 measured |
| TaxEval (Vals AI) | Not measured | 66.3% | Only MiniMax M2.1 measured |
| MedQA (Vals AI) | Not measured | 91.2% | Only MiniMax M2.1 measured |
| MedCode (Vals AI) | Not measured | 34.1% | Only MiniMax M2.1 measured |
| MMLU Pro (Artificial Analysis run) | 20.0% | 87.5% | MiniMax M2.1 |
| LMArena Text: science | 1055 | Not measured | Only Llama 3.2 1B Instruct measured |
| LMArena Text: medicine and healthcare | 1009 | Not measured | Only Llama 3.2 1B Instruct measured |
| LMArena Text: legal and government | 1071 | Not measured | Only Llama 3.2 1B Instruct measured |
| LMArena Text: business and finance | 1000 | Not measured | Only Llama 3.2 1B Instruct measured |
| Neither model has a result on the other 6 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | Llama 3.2 1B Instruct | MiniMax M2.1 | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1201 | Only MiniMax M2.1 measured |
| Design Arena: UI components | Not measured | 1220 | Only MiniMax M2.1 measured |
| Design Arena: game dev | Not measured | 1134 | Only MiniMax M2.1 measured |
| Design Arena: data viz | Not measured | 1206 | Only MiniMax M2.1 measured |
| Design Arena: 3D | Not measured | 1164 | Only MiniMax M2.1 measured |
| Design Arena: all code categories | Not measured | 1190 | Only MiniMax M2.1 measured |
| Design Arena: SVG | Not measured | 1131 | 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 index02
| Benchmark | Llama 3.2 1B Instruct | MiniMax M2.1 | Leader |
|---|---|---|---|
| MedScribe (Vals AI) | Not measured | 80.8% | Only MiniMax M2.1 measured |
| IFBench | 22.8% | 69.9% | MiniMax M2.1 |
| Long context reasoning (AA-LCR) | 6.7% | 67.7% | MiniMax M2.1 |
| LMArena Text | 1055 | Not measured | Only Llama 3.2 1B Instruct measured |
| LMArena Text: instruction following | 1031 | Not measured | Only Llama 3.2 1B Instruct measured |
| LMArena Text: creative writing | 1033 | Not measured | Only Llama 3.2 1B Instruct measured |
| LMArena Text: English | 1117 | Not measured | Only Llama 3.2 1B Instruct measured |
| LMArena Text: non-English | 973 | Not measured | Only Llama 3.2 1B Instruct measured |
| LMArena Text: Chinese | 959 | Not measured | Only Llama 3.2 1B Instruct measured |
| LMArena Text: German | 1014 | Not measured | Only Llama 3.2 1B Instruct measured |
| LMArena Text: Russian | 941 | Not measured | Only Llama 3.2 1B Instruct measured |
| LMArena Text: multi-turn | 1030 | Not measured | Only Llama 3.2 1B Instruct measured |
| LMArena Text: longer queries | 1050 | Not measured | Only Llama 3.2 1B Instruct measured |
| LMArena Text: writing and language | 1030 | Not measured | Only Llama 3.2 1B Instruct measured |
| LMArena Text: entertainment and media | 1025 | Not measured | Only Llama 3.2 1B Instruct measured |
| Neither model has a result on the other 31 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Llama 3.2 1B Instruct | MiniMax M2.1 | 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
Llama 3.2 1B Instruct costs less
Repository review
50K input + 3K output tokens
Llama 3.2 1B Instruct costs less
Agent loop
200K input + 10K output tokens
Llama 3.2 1B Instruct 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 | Meta Llama 3.2 1B Instruct | MiniMax M2.1 |
|---|---|---|
| Maker | Meta Llama | MiniMax |
| Weights | Open weights | Open weights |
| Released | September 25, 2024 | December 23, 2025 |
| Context window | 60K | 205K |
| Input $/Mtok | $0.027 | $0.30 |
| Output $/Mtok | $0.201 | $1.20 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 1 |
| Cost per solved task | Not computable | $0.59 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Llama 3.2 1B Instruct better than MiniMax M2.1 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Llama 3.2 1B Instruct for output tokens 6.0x cheaper ($0.201 vs $1.20 per million) and work measured by LiveCodeBench (Artificial Analysis run) or Terminal-Bench Hard (Artificial Analysis run), where it leads.
Which is cheaper, Llama 3.2 1B Instruct or MiniMax M2.1?
Llama 3.2 1B Instruct costs $0.027 per million input tokens and $0.201 per million output tokens; MiniMax M2.1 costs $0.30 and $1.20. Cost per solved task is known for MiniMax M2.1 only.
Which has the bigger context window, Llama 3.2 1B Instruct or MiniMax M2.1?
MiniMax M2.1, with 205K tokens against 60K.
How many benchmarks is the Llama 3.2 1B Instruct vs MiniMax M2.1 comparison based on?
11 benchmarks have a result for both models as of October 22, 2026. Llama 3.2 1B Instruct's coding index rests on 0 independent benchmarks and MiniMax M2.1's on 1. Where only one model has a result, the page says so instead of naming a winner.