Llama 3.2 3B Instruct vs Mistral Small 3
Mistral Small 3 leads Llama 3.2 3B Instruct on the Artificial Analysis Intelligence Index, 6.7 to 5.7, and Mistral Small 3 costs 4.2x less per output token ($0.08 vs $0.3325 per million). Updated October 21, 2026; 33 benchmarks have a result for both.
Llama 3.2 3B Instruct or Mistral Small 3: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Llama 3.2 3B Instruct for a larger context window (131K against 33K tokens) and work measured by LiveCodeBench (Artificial Analysis run) or LMArena Text: coding, where it leads.
- Shared results
- 33
- Llama 3.2 3B Instruct only
- 2
- Mistral Small 3 only
- 3
- 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 Small 3
Mistral Small 3 leads, 6.7 against 5.7.
Same board, both measured
Long documents
Listed context window
Llama 3.2 3B Instruct
Llama 3.2 3B Instruct takes 131K tokens against 33K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mistral Small 3
Llama 3.2 3B Instruct $0.0002, Mistral Small 3 $0.0001 per turn.
List prices
At a glance
- Coding index
- Llama 3.2 3B Instruct not benchmarked, Mistral Small 3 not benchmarked.
- Widest category
- Knowledge, 22.0 points apart on average over 5 shared benchmarks.
- Widest single gap
- LMArena Text: Russian: Llama 3.2 3B Instruct 949 against Mistral Small 3 1216, 36.0 points apart on a 0 to 100 scale.
- Output price
- Llama 3.2 3B Instruct $0.3325 against Mistral Small 3 $0.08 per million output tokens, 4.2x apart.
- Context window
- Llama 3.2 3B Instruct takes 131K tokens, Mistral Small 3 33K.
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 3B Instruct | Bars | Mistral Small 3 | Ties |
|---|---|---|---|---|
| Coding | 0 | 3 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 1 | 4 | ||
| Math | 0 | 4 | ||
| Knowledge | 0 | 5 | ||
| General | 1 | 13 | ||
| All shared boards | 3 | 30 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
| Benchmark | Llama 3.2 3B Instruct | Mistral Small 3 | Leader |
|---|---|---|---|
| LiveCodeBench (Artificial Analysis run) | 8.3% | 25.2% | Mistral Small 3 |
| LMArena Text: coding | 1098 | 1246 | Mistral Small 3 |
| LMArena Text: software and IT | 1103 | 1248 | Mistral Small 3 |
| Neither model has a result on the other 36 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Llama 3.2 3B Instruct | Mistral Small 3 | Leader |
|---|---|---|---|
| AA Intelligence Index | 5.7 | 6.7 | Mistral Small 3 |
| Neither model has a result on the other 1 board here | |||
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Llama 3.2 3B Instruct | Mistral Small 3 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 21.1% | 19.6% | Llama 3.2 3B Instruct |
| BALROG | 10.1% | Not measured | Only Llama 3.2 3B Instruct measured |
| Berkeley Function Calling Leaderboard (BFCL) | 21.9% | Not measured | Only Llama 3.2 3B Instruct measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index14
| Benchmark | Llama 3.2 3B Instruct | Mistral Small 3 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 25.5% | 46.2% | Mistral Small 3 |
| Humanity's Last Exam (Artificial Analysis run) | 5.3% | 3.8% | Llama 3.2 3B Instruct |
| LMArena Text: hard prompts | 1095 | 1233 | Mistral Small 3 |
| LMArena Text: expert | 1090 | 1202 | Mistral Small 3 |
| LMArena Text: hard prompts in English | 1141 | 1243 | Mistral Small 3 |
| Neither model has a result on the other 17 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | Llama 3.2 3B Instruct | Mistral Small 3 | Leader |
|---|---|---|---|
| AA Math Index | 3.3 | 4.3 | Mistral Small 3 |
| AIME 2025 (Artificial Analysis run) | 3.3% | 4.3% | Mistral Small 3 |
| LMArena Text: math | 1126 | 1240 | Mistral Small 3 |
| LMArena Text: mathematical work | 1128 | 1243 | Mistral Small 3 |
| Neither model has a result on the other 9 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | Llama 3.2 3B Instruct | Mistral Small 3 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 34.7% | 65.2% | Mistral Small 3 |
| LMArena Text: science | 1106 | 1235 | Mistral Small 3 |
| LMArena Text: medicine and healthcare | 1104 | 1215 | Mistral Small 3 |
| LMArena Text: legal and government | 1115 | 1219 | Mistral Small 3 |
| LMArena Text: business and finance | 1074 | 1205 | Mistral Small 3 |
| Neither model has a result on the other 13 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Llama 3.2 3B Instruct | Mistral Small 3 | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index113
| Benchmark | Llama 3.2 3B Instruct | Mistral Small 3 | Leader |
|---|---|---|---|
| IFBench | 26.2% | 26.4% | Mistral Small 3 |
| Long context reasoning (AA-LCR) | 4.3% | 0.0% | Llama 3.2 3B Instruct |
| LMArena Text | 1110 | 1234 | Mistral Small 3 |
| LMArena Text: instruction following | 1089 | 1214 | Mistral Small 3 |
| LMArena Text: creative writing | 1094 | 1194 | Mistral Small 3 |
| LMArena Text: English | 1180 | 1256 | Mistral Small 3 |
| LMArena Text: non-English | 1019 | 1198 | Mistral Small 3 |
| LMArena Text: Chinese | 1017 | 1204 | Mistral Small 3 |
| LMArena Text: German | 1056 | 1211 | Mistral Small 3 |
| LMArena Text: Japanese | Not measured | 1111 | Only Mistral Small 3 measured |
| LMArena Text: Korean | Not measured | 1188 | Only Mistral Small 3 measured |
| LMArena Text: French | Not measured | 1204 | Only Mistral Small 3 measured |
| LMArena Text: Russian | 949 | 1216 | Mistral Small 3 |
| LMArena Text: multi-turn | 1105 | 1217 | Mistral Small 3 |
| LMArena Text: longer queries | 1100 | 1246 | Mistral Small 3 |
| LMArena Text: writing and language | 1096 | 1214 | Mistral Small 3 |
| LMArena Text: entertainment and media | 1083 | 1184 | Mistral Small 3 |
| Neither model has a result on the other 29 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Llama 3.2 3B Instruct | Mistral Small 3 | 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
Mistral Small 3 costs less
Repository review
50K input + 3K output tokens
Mistral Small 3 costs less
Agent loop
200K input + 10K output tokens
Mistral Small 3 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 3B Instruct | Mistral Small 3 |
|---|---|---|
| Maker | Meta Llama | Mistral AI |
| Weights | Open weights | Open weights |
| Released | September 25, 2024 | January 30, 2025 |
| Context window | 131K | 33K |
| Input $/Mtok | $0.0505 | $0.05 |
| Output $/Mtok | $0.3325 | $0.08 |
| 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 3B Instruct better than Mistral Small 3 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Llama 3.2 3B Instruct for a larger context window (131K against 33K tokens) and work measured by LiveCodeBench (Artificial Analysis run) or LMArena Text: coding, where it leads.
Which is cheaper, Llama 3.2 3B Instruct or Mistral Small 3?
Llama 3.2 3B Instruct costs $0.0505 per million input tokens and $0.3325 per million output tokens; Mistral Small 3 costs $0.05 and $0.08. Cost per solved task is known for neither of them.
Which has the bigger context window, Llama 3.2 3B Instruct or Mistral Small 3?
Llama 3.2 3B Instruct, with 131K tokens against 33K.
How many benchmarks is the Llama 3.2 3B Instruct vs Mistral Small 3 comparison based on?
33 benchmarks have a result for both models as of October 21, 2026. Llama 3.2 3B Instruct's coding index rests on 0 independent benchmarks and Mistral Small 3's on 0. Where only one model has a result, the page says so instead of naming a winner.