Llama 3.2 1B Instruct vs Mistral Medium 3.1
Mistral Medium 3.1 leads Llama 3.2 1B Instruct on the Artificial Analysis Intelligence Index, 9.2 to 4.8, and Llama 3.2 1B Instruct costs 10.0x less per output token ($0.201 vs $2.00 per million). Updated October 21, 2026; 34 benchmarks have a result for both.
Llama 3.2 1B Instruct or Mistral Medium 3.1: 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 10.0x cheaper ($0.201 vs $2.00 per million) and work measured by LiveCodeBench (Artificial Analysis run) or Terminal-Bench Hard (Artificial Analysis run), where it leads.
- Shared results
- 34
- Llama 3.2 1B Instruct only
- 4
- Mistral Medium 3.1 only
- 40
- 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
Mistral Medium 3.1
Mistral Medium 3.1 leads, 9.2 against 4.8.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Mistral Medium 3.1 has a AA Agentic Index result, 1.9.
Directional only
Math
AIME
No call
Only Mistral Medium 3.1 has a AIME result, 42.3%.
Directional only
Knowledge
MMLU Pro
No call
Only Mistral Medium 3.1 has a MMLU Pro result, 75.3%.
Directional only
Web design
Design Arena: websites
No call
Only Mistral Medium 3.1 has a Design Arena: websites result, 1133.
Directional only
Long documents
Listed context window
Mistral Medium 3.1
Mistral Medium 3.1 takes 131K 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, Mistral Medium 3.1 $0.0014 per turn.
List prices
At a glance
- Coding index
- Llama 3.2 1B Instruct not benchmarked, Mistral Medium 3.1 not benchmarked.
- Widest category
- Knowledge, 63.5 points apart on average over 5 shared benchmarks.
- Widest single gap
- LMArena Text: Chinese: Llama 3.2 1B Instruct 959 against Mistral Medium 3.1 1447, 74.5 points apart on a 0 to 100 scale.
- Output price
- Llama 3.2 1B Instruct $0.201 against Mistral Medium 3.1 $2.00 per million output tokens, 10.0x apart.
- Context window
- Llama 3.2 1B Instruct takes 60K tokens, Mistral Medium 3.1 131K.
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 | Mistral Medium 3.1 | Ties |
|---|---|---|---|---|
| Coding | 0 | 4 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 1 | 4 | ||
| Math | 0 | 4 | ||
| Knowledge | 0 | 5 | ||
| General | 0 | 14 | ||
| All shared boards | 1 | 33 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index04
| Benchmark | Llama 3.2 1B Instruct | Mistral Medium 3.1 | Leader |
|---|---|---|---|
| AA Coding Index | Not measured | 20.5 | Only Mistral Medium 3.1 measured |
| LiveCodeBench (Vals AI) | Not measured | 44.8% | Only Mistral Medium 3.1 measured |
| LiveCodeBench (Artificial Analysis run) | 1.9% | 40.6% | Mistral Medium 3.1 |
| SciCode | Not measured | 32.4% | Only Mistral Medium 3.1 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 0.0% | 10.6% | Mistral Medium 3.1 |
| ALE-Bench | Not measured | 210.2 | Only Mistral Medium 3.1 measured |
| LMArena Text: coding | 1070 | 1433 | Mistral Medium 3.1 |
| LMArena Text: software and IT | 1056 | 1436 | Mistral Medium 3.1 |
| 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 | Mistral Medium 3.1 | Leader |
|---|---|---|---|
| AA Intelligence Index | 4.8 | 9.2 | Mistral Medium 3.1 |
| 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 | Mistral Medium 3.1 | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 1.9 | Only Mistral Medium 3.1 measured |
| τ²-Bench Telecom | 0.0% | 40.6% | Mistral Medium 3.1 |
| 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 37 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index14
| Benchmark | Llama 3.2 1B Instruct | Mistral Medium 3.1 | Leader |
|---|---|---|---|
| MMMU Pro (Vals AI) | Not measured | 63.0% | Only Mistral Medium 3.1 measured |
| GPQA Diamond (Artificial Analysis run) | 19.6% | 58.8% | Mistral Medium 3.1 |
| Humanity's Last Exam (Artificial Analysis run) | 5.5% | 4.7% | Llama 3.2 1B Instruct |
| GPQA Diamond (Epoch AI run) | 23.9% | Not measured | Only Llama 3.2 1B Instruct measured |
| LMArena Text: hard prompts | 1044 | 1426 | Mistral Medium 3.1 |
| LMArena Text: expert | 1007 | 1408 | Mistral Medium 3.1 |
| LMArena Text: hard prompts in English | 1087 | 1435 | Mistral Medium 3.1 |
| LMArena Vision: diagrams | Not measured | 1175 | Only Mistral Medium 3.1 measured |
| LMArena Vision: homework | Not measured | 1165 | Only Mistral Medium 3.1 measured |
| Neither model has a result on the other 13 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | Llama 3.2 1B Instruct | Mistral Medium 3.1 | Leader |
|---|---|---|---|
| AIME | Not measured | 42.3% | Only Mistral Medium 3.1 measured |
| MATH 500 (Vals AI) | Not measured | 87.0% | Only Mistral Medium 3.1 measured |
| MGSM (Vals AI) | Not measured | 90.9% | Only Mistral Medium 3.1 measured |
| AA Math Index | 0.0 | 38.3 | Mistral Medium 3.1 |
| AIME 2025 (Artificial Analysis run) | 0.0% | 38.3% | Mistral Medium 3.1 |
| OTIS Mock AIME | 0.6% | Not measured | Only Llama 3.2 1B Instruct measured |
| LMArena Text: math | 1086 | 1408 | Mistral Medium 3.1 |
| LMArena Text: mathematical work | 1098 | 1412 | Mistral Medium 3.1 |
| Neither model has a result on the other 5 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | Llama 3.2 1B Instruct | Mistral Medium 3.1 | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 75.3% | Only Mistral Medium 3.1 measured |
| LegalBench (Vals AI) | Not measured | 62.0% | Only Mistral Medium 3.1 measured |
| CorpFin (Vals AI) | Not measured | 50.7% | Only Mistral Medium 3.1 measured |
| TaxEval (Vals AI) | Not measured | 70.3% | Only Mistral Medium 3.1 measured |
| MortgageTax (Vals AI) | Not measured | 36.5% | Only Mistral Medium 3.1 measured |
| MedQA (Vals AI) | Not measured | 78.2% | Only Mistral Medium 3.1 measured |
| MMLU Pro (Artificial Analysis run) | 20.0% | 68.3% | Mistral Medium 3.1 |
| LMArena Text: science | 1055 | 1444 | Mistral Medium 3.1 |
| LMArena Text: medicine and healthcare | 1009 | 1449 | Mistral Medium 3.1 |
| LMArena Text: legal and government | 1071 | 1439 | Mistral Medium 3.1 |
| LMArena Text: business and finance | 1000 | 1418 | Mistral Medium 3.1 |
| Neither model has a result on the other 7 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | Llama 3.2 1B Instruct | Mistral Medium 3.1 | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1133 | Only Mistral Medium 3.1 measured |
| Design Arena: UI components | Not measured | 1100 | Only Mistral Medium 3.1 measured |
| Design Arena: game dev | Not measured | 1074 | Only Mistral Medium 3.1 measured |
| Design Arena: data viz | Not measured | 1144 | Only Mistral Medium 3.1 measured |
| Design Arena: 3D | Not measured | 1081 | Only Mistral Medium 3.1 measured |
| Design Arena: all code categories | Not measured | 1120 | Only Mistral Medium 3.1 measured |
| Design Arena: SVG | Not measured | 993 | Only Mistral Medium 3.1 measured |
| Design Arena: ASCII art | Not measured | 997 | Only Mistral Medium 3.1 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index014
| Benchmark | Llama 3.2 1B Instruct | Mistral Medium 3.1 | Leader |
|---|---|---|---|
| IFBench | 22.8% | 39.8% | Mistral Medium 3.1 |
| Long context reasoning (AA-LCR) | 6.7% | 42.7% | Mistral Medium 3.1 |
| DTBench | Not measured | 67.5% | Only Mistral Medium 3.1 measured |
| LMCA | Not measured | 19.1% | Only Mistral Medium 3.1 measured |
| LMArena Text | 1055 | 1424 | Mistral Medium 3.1 |
| LMArena Text: instruction following | 1031 | 1398 | Mistral Medium 3.1 |
| LMArena Text: creative writing | 1033 | 1391 | Mistral Medium 3.1 |
| LMArena Vision | Not measured | 1172 | Only Mistral Medium 3.1 measured |
| LMArena Text: English | 1117 | 1438 | Mistral Medium 3.1 |
| LMArena Text: non-English | 973 | 1408 | Mistral Medium 3.1 |
| LMArena Text: Chinese | 959 | 1447 | Mistral Medium 3.1 |
| LMArena Text: German | 1014 | 1432 | Mistral Medium 3.1 |
| LMArena Text: Japanese | Not measured | 1379 | Only Mistral Medium 3.1 measured |
| LMArena Text: Korean | Not measured | 1380 | Only Mistral Medium 3.1 measured |
| LMArena Text: French | Not measured | 1460 | Only Mistral Medium 3.1 measured |
| LMArena Text: Spanish | Not measured | 1433 | Only Mistral Medium 3.1 measured |
| LMArena Text: Russian | 941 | 1411 | Mistral Medium 3.1 |
| LMArena Text: Polish | Not measured | 1430 | Only Mistral Medium 3.1 measured |
| LMArena Text: multi-turn | 1030 | 1418 | Mistral Medium 3.1 |
| LMArena Text: longer queries | 1050 | 1406 | Mistral Medium 3.1 |
| LMArena Text: writing and language | 1030 | 1395 | Mistral Medium 3.1 |
| LMArena Text: entertainment and media | 1025 | 1390 | Mistral Medium 3.1 |
| LMArena Vision: English | Not measured | 1179 | Only Mistral Medium 3.1 measured |
| LMArena Vision: Chinese | Not measured | 1190 | Only Mistral Medium 3.1 measured |
| LMArena Vision: reading text | Not measured | 1175 | Only Mistral Medium 3.1 measured |
| LMArena Vision: humor | Not measured | 1168 | Only Mistral Medium 3.1 measured |
| LMArena Vision: captioning | Not measured | 1145 | Only Mistral Medium 3.1 measured |
| LMArena Vision: recognizing things | Not measured | 1147 | Only Mistral Medium 3.1 measured |
| LMArena Vision: creative writing | Not measured | 1200 | Only Mistral Medium 3.1 measured |
| Neither model has a result on the other 17 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Llama 3.2 1B Instruct | Mistral Medium 3.1 | 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 | Mistral Medium 3.1 |
|---|---|---|
| Maker | Meta Llama | Mistral AI |
| Weights | Open weights | No public weights listed |
| Released | September 25, 2024 | August 13, 2025 |
| Context window | 60K | 131K |
| Input $/Mtok | $0.027 | $0.40 |
| Output $/Mtok | $0.201 | $2.00 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Llama 3.2 1B Instruct better than Mistral Medium 3.1 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 10.0x cheaper ($0.201 vs $2.00 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 Mistral Medium 3.1?
Llama 3.2 1B Instruct costs $0.027 per million input tokens and $0.201 per million output tokens; Mistral Medium 3.1 costs $0.40 and $2.00. Cost per solved task is known for neither of them.
Which has the bigger context window, Llama 3.2 1B Instruct or Mistral Medium 3.1?
Mistral Medium 3.1, with 131K tokens against 60K.
How many benchmarks is the Llama 3.2 1B Instruct vs Mistral Medium 3.1 comparison based on?
34 benchmarks have a result for both models as of October 21, 2026. Llama 3.2 1B Instruct's coding index rests on 0 independent benchmarks and Mistral Medium 3.1's on 0. Where only one model has a result, the page says so instead of naming a winner.