Kimi K2 0905 vs Muse Glimmer 30B
Muse Glimmer 30B leads Kimi K2 0905 on the Artificial Analysis Intelligence Index, 17.5 to 15.3, and Muse Glimmer 30B costs 1.9x less per output token ($1.35 vs $2.50 per million). Updated October 22, 2026; 2 benchmarks have a result for both.
Kimi K2 0905 or Muse Glimmer 30B: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Kimi K2 0905 for a larger context window (262K against 131K tokens) and work measured by ALE-Bench or AA Intelligence Index, where it leads.
- Shared results
- 2
- Kimi K2 0905 only
- 15
- Muse Glimmer 30B only
- 3
- Like-for-like categories
- 2 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 15.3.
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
Web design
Design Arena: websites
No call
Only Kimi K2 0905 has a Design Arena: websites result, 1088.
Directional only
Long documents
Listed context window
Kimi K2 0905
Kimi K2 0905 takes 262K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Muse Glimmer 30B
Kimi K2 0905 $0.0019, Muse Glimmer 30B $0.0010 per turn.
List prices
At a glance
- Coding index
- Kimi K2 0905 not benchmarked, Muse Glimmer 30B not benchmarked.
- Widest category
- Coding, 23.2 points apart on average over 1 shared benchmark.
- Widest single gap
- ALE-Bench: Kimi K2 0905 267.1 against Muse Glimmer 30B 962.5, 23.2 points apart on a 0 to 100 scale.
- Output price
- Kimi K2 0905 $2.50 against Muse Glimmer 30B $1.35 per million output tokens, 1.9x apart.
- Context window
- Kimi K2 0905 takes 262K tokens, Muse Glimmer 30B 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 | Kimi K2 0905 | Bars | Muse Glimmer 30B | Ties |
|---|---|---|---|---|
| Coding | 0 | 1 | ||
| Intelligence | 0 | 1 | ||
| All shared boards | 0 | 2 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index01
| Benchmark | Kimi K2 0905 | Muse Glimmer 30B | Leader |
|---|---|---|---|
| AA Coding Index | Not measured | 49.0 | Only Muse Glimmer 30B measured |
| LiveCodeBench (Artificial Analysis run) | 61.0% | Not measured | Only Kimi K2 0905 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 23.5% | Not measured | Only Kimi K2 0905 measured |
| WeirdML | 36.7% | Not measured | Only Kimi K2 0905 measured |
| ALE-Bench | 267.1 | 962.5 | Muse Glimmer 30B |
| Neither model has a result on the other 34 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Kimi K2 0905 | Muse Glimmer 30B | Leader |
|---|---|---|---|
| AA Intelligence Index | 15.3 | 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 index2 boards, none shared
| Benchmark | Kimi K2 0905 | Muse Glimmer 30B | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 8.5 | Only Muse Glimmer 30B measured |
| τ²-Bench Telecom | 73.4% | Not measured | Only Kimi K2 0905 measured |
| Neither model has a result on the other 39 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | Kimi K2 0905 | Muse Glimmer 30B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 76.7% | Not measured | Only Kimi K2 0905 measured |
| Humanity's Last Exam (Artificial Analysis run) | 6.4% | Not measured | Only Kimi K2 0905 measured |
| Kagi LLM Benchmark | 52.2% | Not measured | Only Kimi K2 0905 measured |
| Neither model has a result on the other 19 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Kimi K2 0905 | Muse Glimmer 30B | Leader |
|---|---|---|---|
| AA Math Index | 57.3 | Not measured | Only Kimi K2 0905 measured |
| AIME 2025 (Artificial Analysis run) | 57.3% | Not measured | Only Kimi K2 0905 measured |
| Neither model has a result on the other 11 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Kimi K2 0905 | Muse Glimmer 30B | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 81.9% | Not measured | Only Kimi K2 0905 measured |
| Neither model has a result on the other 17 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | Kimi K2 0905 | Muse Glimmer 30B | Leader |
|---|---|---|---|
| Design Arena: websites | 1088 | Not measured | Only Kimi K2 0905 measured |
| Design Arena: game dev | Not measured | 1144 | Only Muse Glimmer 30B measured |
| Design Arena: all code categories | 1098 | Not measured | Only Kimi K2 0905 measured |
| Neither model has a result on the other 6 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | Kimi K2 0905 | Muse Glimmer 30B | Leader |
|---|---|---|---|
| IFBench | 41.7% | Not measured | Only Kimi K2 0905 measured |
| Long context reasoning (AA-LCR) | 53.0% | Not measured | Only Kimi K2 0905 measured |
| Fiction.LiveBench | 66.7% | Not measured | Only Kimi K2 0905 measured |
| Neither model has a result on the other 43 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Kimi K2 0905 | 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
Muse Glimmer 30B costs less
Repository review
50K input + 3K output tokens
Muse Glimmer 30B costs less
Agent loop
200K input + 10K output tokens
Muse Glimmer 30B 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 | MoonshotAI Kimi K2 0905 | Meta Muse Glimmer 30B |
|---|---|---|
| Maker | Moonshot AI | Meta |
| Weights | Open weights | Open weights |
| Released | September 4, 2025 | August 9, 2026 |
| Context window | 262K | 131K |
| Input $/Mtok | $0.60 | $0.325 |
| Output $/Mtok | $2.50 | $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 Kimi K2 0905 better than Muse Glimmer 30B for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Kimi K2 0905 for a larger context window (262K against 131K tokens) and work measured by ALE-Bench or AA Intelligence Index, where it leads.
Which is cheaper, Kimi K2 0905 or Muse Glimmer 30B?
Kimi K2 0905 costs $0.60 per million input tokens and $2.50 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, Kimi K2 0905 or Muse Glimmer 30B?
Kimi K2 0905, with 262K tokens against 131K.
How many benchmarks is the Kimi K2 0905 vs Muse Glimmer 30B comparison based on?
2 benchmarks have a result for both models as of October 22, 2026. Kimi K2 0905'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.