Llama 3.2 3B Instruct vs Saba
Saba leads Llama 3.2 3B Instruct on the Artificial Analysis Intelligence Index, 6.5 to 5.7, and Llama 3.2 3B Instruct costs 1.8x less per output token ($0.3325 vs $0.60 per million). Updated October 21, 2026; 4 benchmarks have a result for both.
Llama 3.2 3B Instruct or Saba: 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 output tokens 1.8x cheaper ($0.3325 vs $0.60 per million) and a larger context window (131K against 33K tokens).
- Shared results
- 4
- Llama 3.2 3B Instruct only
- 31
- Saba only
- 0
- Like-for-like categories
- 3 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
Saba
Saba leads, 6.5 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
Llama 3.2 3B Instruct
Llama 3.2 3B Instruct $0.0002, Saba $0.0005 per turn.
List prices
At a glance
- Coding index
- Llama 3.2 3B Instruct not benchmarked, Saba not benchmarked.
- Widest category
- Knowledge, 26.4 points apart on average over 1 shared benchmark.
- Widest single gap
- MMLU Pro (Artificial Analysis run): Llama 3.2 3B Instruct 34.7% against Saba 61.1%, 26.4 points apart on a 0 to 100 scale.
- Output price
- Llama 3.2 3B Instruct $0.3325 against Saba $0.60 per million output tokens, 1.8x apart.
- Context window
- Llama 3.2 3B Instruct takes 131K tokens, Saba 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 | Saba | Ties |
|---|---|---|---|---|
| Intelligence | 0 | 1 | ||
| Reasoning | 1 | 1 | ||
| Knowledge | 0 | 1 | ||
| All shared boards | 1 | 3 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index3 boards, none shared
| Benchmark | Llama 3.2 3B Instruct | Saba | Leader |
|---|---|---|---|
| LiveCodeBench (Artificial Analysis run) | 8.3% | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: coding | 1098 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: software and IT | 1103 | Not measured | Only Llama 3.2 3B Instruct measured |
| 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 | Saba | Leader |
|---|---|---|---|
| AA Intelligence Index | 5.7 | 6.5 | Saba |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | Llama 3.2 3B Instruct | Saba | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 21.1% | Not measured | Only Llama 3.2 3B Instruct measured |
| 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 index11
| Benchmark | Llama 3.2 3B Instruct | Saba | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 25.5% | 42.4% | Saba |
| Humanity's Last Exam (Artificial Analysis run) | 5.3% | 4.3% | Llama 3.2 3B Instruct |
| LMArena Text: hard prompts | 1095 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: expert | 1090 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: hard prompts in English | 1141 | Not measured | Only Llama 3.2 3B Instruct measured |
| Neither model has a result on the other 17 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | Llama 3.2 3B Instruct | Saba | Leader |
|---|---|---|---|
| AA Math Index | 3.3 | Not measured | Only Llama 3.2 3B Instruct measured |
| AIME 2025 (Artificial Analysis run) | 3.3% | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: math | 1126 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: mathematical work | 1128 | Not measured | Only Llama 3.2 3B Instruct measured |
| Neither model has a result on the other 9 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Llama 3.2 3B Instruct | Saba | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 34.7% | 61.1% | Saba |
| LMArena Text: science | 1106 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: medicine and healthcare | 1104 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: legal and government | 1115 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: business and finance | 1074 | Not measured | Only Llama 3.2 3B Instruct measured |
| 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 | Saba | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index14 boards, none shared
| Benchmark | Llama 3.2 3B Instruct | Saba | Leader |
|---|---|---|---|
| IFBench | 26.2% | Not measured | Only Llama 3.2 3B Instruct measured |
| Long context reasoning (AA-LCR) | 4.3% | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text | 1110 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: instruction following | 1089 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: creative writing | 1094 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: English | 1180 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: non-English | 1019 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: Chinese | 1017 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: German | 1056 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: Russian | 949 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: multi-turn | 1105 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: longer queries | 1100 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: writing and language | 1096 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: entertainment and media | 1083 | Not measured | Only Llama 3.2 3B Instruct measured |
| Neither model has a result on the other 32 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Llama 3.2 3B Instruct | Saba | 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 3B Instruct costs less
Repository review
50K input + 3K output tokens
Llama 3.2 3B Instruct costs less
Agent loop
200K input + 10K output tokens
Llama 3.2 3B 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 3B Instruct | Mistral Saba |
|---|---|---|
| Maker | Meta Llama | Mistral AI |
| Weights | Open weights | No public weights listed |
| Released | September 25, 2024 | February 17, 2025 |
| Context window | 131K | 33K |
| Input $/Mtok | $0.0505 | $0.20 |
| Output $/Mtok | $0.3325 | $0.60 |
| 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 Saba for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Llama 3.2 3B Instruct for output tokens 1.8x cheaper ($0.3325 vs $0.60 per million) and a larger context window (131K against 33K tokens).
Which is cheaper, Llama 3.2 3B Instruct or Saba?
Llama 3.2 3B Instruct costs $0.0505 per million input tokens and $0.3325 per million output tokens; Saba costs $0.20 and $0.60. Cost per solved task is known for neither of them.
Which has the bigger context window, Llama 3.2 3B Instruct or Saba?
Llama 3.2 3B Instruct, with 131K tokens against 33K.
How many benchmarks is the Llama 3.2 3B Instruct vs Saba comparison based on?
4 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 Saba's on 0. Where only one model has a result, the page says so instead of naming a winner.