Llama 3.2 3B Instruct vs Muse Glimmer 30B
Muse Glimmer 30B leads Llama 3.2 3B Instruct on the Artificial Analysis Intelligence Index, 17.5 to 5.7, and Llama 3.2 3B Instruct costs 4.1x less per output token ($0.3325 vs $1.35 per million). Updated October 22, 2026; 1 benchmarks have a result for both.
Llama 3.2 3B Instruct or Muse Glimmer 30B: 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 4.1x cheaper ($0.3325 vs $1.35 per million) and work measured by AA Intelligence Index, where it leads.
- Shared results
- 1
- Llama 3.2 3B Instruct only
- 34
- Muse Glimmer 30B only
- 4
- Like-for-like categories
- 1 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
Muse Glimmer 30B
Muse Glimmer 30B leads, 17.5 against 5.7.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Muse Glimmer 30B has a AA Agentic Index result, 8.5.
Directional only
Long documents
Listed context window
No call
Both take 131K tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Llama 3.2 3B Instruct
Llama 3.2 3B Instruct $0.0002, Muse Glimmer 30B $0.0010 per turn.
List prices
At a glance
- Coding index
- Llama 3.2 3B Instruct not benchmarked, Muse Glimmer 30B not benchmarked.
- Widest category
- Intelligence, 11.8 points apart on average over 1 shared benchmark.
- Widest single gap
- AA Intelligence Index: Llama 3.2 3B Instruct 5.7 against Muse Glimmer 30B 17.5, 11.8 points apart on a 0 to 100 scale.
- Output price
- Llama 3.2 3B Instruct $0.3325 against Muse Glimmer 30B $1.35 per million output tokens, 4.1x apart.
- Context window
- Both take 131K tokens.
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 | Muse Glimmer 30B | Ties |
|---|---|---|---|---|
| Intelligence | 0 | 1 | ||
| All shared boards | 0 | 1 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index5 boards, none shared
| Benchmark | Llama 3.2 3B Instruct | Muse Glimmer 30B | Leader |
|---|---|---|---|
| AA Coding Index | Not measured | 49.0 | Only Muse Glimmer 30B measured |
| LiveCodeBench (Artificial Analysis run) | 8.3% | Not measured | Only Llama 3.2 3B Instruct measured |
| ALE-Bench | Not measured | 962.5 | Only Muse Glimmer 30B 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 34 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Llama 3.2 3B Instruct | Muse Glimmer 30B | Leader |
|---|---|---|---|
| AA Intelligence Index | 5.7 | 17.5 | Muse Glimmer 30B |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | Llama 3.2 3B Instruct | Muse Glimmer 30B | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 8.5 | Only Muse Glimmer 30B measured |
| 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 37 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | Llama 3.2 3B Instruct | Muse Glimmer 30B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 25.5% | Not measured | Only Llama 3.2 3B Instruct measured |
| Humanity's Last Exam (Artificial Analysis run) | 5.3% | Not measured | Only Llama 3.2 3B Instruct measured |
| 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 | Muse Glimmer 30B | 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 | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | Llama 3.2 3B Instruct | Muse Glimmer 30B | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 34.7% | Not measured | Only Llama 3.2 3B Instruct measured |
| 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 designDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Llama 3.2 3B Instruct | Muse Glimmer 30B | Leader |
|---|---|---|---|
| Design Arena: game dev | Not measured | 1144 | Only Muse Glimmer 30B measured |
| Neither model has a result on the other 8 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index14 boards, none shared
| Benchmark | Llama 3.2 3B Instruct | Muse Glimmer 30B | 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 | Muse Glimmer 30B | 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 | Meta Muse Glimmer 30B |
|---|---|---|
| Maker | Meta Llama | Meta |
| Weights | Open weights | Open weights |
| Released | September 25, 2024 | August 9, 2026 |
| Context window | 131K | 131K |
| Input $/Mtok | $0.0505 | $0.325 |
| Output $/Mtok | $0.3325 | $1.35 |
| 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 Muse Glimmer 30B 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 4.1x cheaper ($0.3325 vs $1.35 per million) and work measured by AA Intelligence Index, where it leads.
Which is cheaper, Llama 3.2 3B Instruct or Muse Glimmer 30B?
Llama 3.2 3B Instruct costs $0.0505 per million input tokens and $0.3325 per million output tokens; Muse Glimmer 30B costs $0.325 and $1.35. Cost per solved task is known for neither of them.
Which has the bigger context window, Llama 3.2 3B Instruct or Muse Glimmer 30B?
Neither: both take 131K tokens.
How many benchmarks is the Llama 3.2 3B Instruct vs Muse Glimmer 30B comparison based on?
1 benchmarks have a result for both models as of October 22, 2026. Llama 3.2 3B Instruct's coding index rests on 0 independent benchmarks and Muse Glimmer 30B's on 0. Where only one model has a result, the page says so instead of naming a winner.