Muse Glimmer 30B vs Ternary Bonsai 2 27B

Decision reading

Ternary Bonsai 2 27B has the higher public score estimate, 50.78 versus 45.08, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

4 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

Muse Glimmer 30B

Which one for your work

Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.

Long documents
Prompts that approach the documented context limit
Ternary Bonsai 2 27B
Ternary Bonsai 2 27B has the larger documented context window.
Confidence: documented

Show secondary and unsupported calls

Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Muse Glimmer 30B and Ternary Bonsai 2 27B are scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Muse Glimmer 30B and Ternary Bonsai 2 27B are scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limited
Chat turn cost
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. Muse Glimmer 30B does not fit this workload in one request. Muse Glimmer 30B has no comparable published API token rate. Ternary Bonsai 2 27B has no comparable published API token rate.
Confidence: listed-rates
Repository review cost
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

10 Muse Glimmer 30B only

4 Shared

17 Ternary Bonsai 2 27B only

Shared results: 4
Muse Glimmer 30B only: 10
Ternary Bonsai 2 27B only: 17
Like-for-like categories: 1 / 8

3 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.

Instruction following

Like-for-like

Muse Glimmer 30B: 77.7; #55/124
Ternary Bonsai 2 27B: 71.0; #64/124
Basis: Provisional lane · 1 vs 1 weighted rows
Reading: Muse Glimmer 30B leads

Agentic

Directional only

Muse Glimmer 30B: 43.3; Estimated · #100/154
Ternary Bonsai 2 27B: 49.9; Estimated · #59/154
Basis: BenchAlign lane · 4 vs 3 public rows
Reading: Directional only

Coding

Directional only

Muse Glimmer 30B: 48.5; Estimated · #71/154
Ternary Bonsai 2 27B: 49.9; Estimated · #64/154
Basis: BenchAlign lane · 4 vs 4 public rows
Reading: Directional only

Knowledge

Directional only

Muse Glimmer 30B: 47.3; Estimated · #99/184
Ternary Bonsai 2 27B: 50.6; Estimated · #78/184
Basis: BenchAlign lane · 0 vs 3 public rows
Reading: Directional only

Reasoning

Not comparable

Muse Glimmer 30B: 78.3; Unranked · 2 rankable rows
Ternary Bonsai 2 27B: 73.9; Unranked · 1 rankable row
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

Muse Glimmer 30B: 75.4; Unranked · 1 rankable row
Ternary Bonsai 2 27B: 76.8; Unranked · 4 rankable rows
Basis: Provisional lane · 1 vs 1 weighted rows
Reading: Not comparable

Multilingual

Not comparable

Muse Glimmer 30B: Not ranked
Ternary Bonsai 2 27B: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

Muse Glimmer 30B: 46.3; #40/48
Ternary Bonsai 2 27B: Not ranked
Basis: Provisional lane · 2 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Muse Glimmer 30B and Ternary Bonsai 2 27B
Category	Muse Glimmer 30B	Ternary Bonsai 2 27B	Basis	Reading
Instruction following	77.7#55/124	71.0#64/124	Like-for-likeProvisional lane · 1 vs 1 weighted rows	Muse Glimmer 30B leads
Agentic	43.3Estimated · #100/154	49.9Estimated · #59/154	Directional onlyBenchAlign lane · 4 vs 3 public rows	Directional only
Coding	48.5Estimated · #71/154	49.9Estimated · #64/154	Directional onlyBenchAlign lane · 4 vs 4 public rows	Directional only
Knowledge	47.3Estimated · #99/184	50.6Estimated · #78/184	Directional onlyBenchAlign lane · 0 vs 3 public rows	Directional only
Reasoning	78.3Unranked · 2 rankable rows	73.9Unranked · 1 rankable row	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	75.4Unranked · 1 rankable row	76.8Unranked · 4 rankable rows	Not comparableProvisional lane · 1 vs 1 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Multimodal	46.3#40/48	Not ranked	Not comparableProvisional lane · 2 vs 0 weighted rows	Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

Shape of the matched evidence

Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.

Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.

SWE-bench Verified
Coding
Muse Glimmer 30B: 76%Ternary Bonsai 2 27B: 60.8%Normalized gap 15.2Muse Glimmer 30B source Ternary Bonsai 2 27B source
IFBench
Instruction following
Muse Glimmer 30B: 77%Ternary Bonsai 2 27B: 74%Normalized gap 3.0Muse Glimmer 30B source Ternary Bonsai 2 27B source
AIME26
Math
Muse Glimmer 30B: 94.7%Ternary Bonsai 2 27B: 95.8%Normalized gap 1.1Muse Glimmer 30B source Ternary Bonsai 2 27B source

What each workload costs

Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.

Chat turn

1K fresh input + 500 output tokens

Muse Glimmer 30B: Self-hosted; infrastructure cost varies; Fits in one request
Ternary Bonsai 2 27B: Self-hosted; infrastructure cost varies; Fits in one request

Muse Glimmer 30B has no comparable published API token rate. Ternary Bonsai 2 27B has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Muse Glimmer 30B: Self-hosted; infrastructure cost varies; Fits in one request
Ternary Bonsai 2 27B: Self-hosted; infrastructure cost varies; Fits in one request

Muse Glimmer 30B has no comparable published API token rate. Ternary Bonsai 2 27B has no comparable published API token rate.

Cache-heavy agent loop

200K cached + 20K fresh input + 10K output tokens

Muse Glimmer 30B: Self-hosted; infrastructure cost varies; Does not fit in one request; Cached-input rate unavailable
Ternary Bonsai 2 27B: Self-hosted; infrastructure cost varies; Fits in one request; Cached-input rate unavailable

Muse Glimmer 30B does not fit this workload in one request. Muse Glimmer 30B has no comparable published API token rate. Ternary Bonsai 2 27B has no comparable published API token rate.

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

SpecificationMuse Glimmer 30BTernary Bonsai 2 27B

Context window

Maximum documented context; output-token limits may be lower.

Muse Glimmer 30B

131K

Ternary Bonsai 2 27B

262K

PrismML Bonsai 2 collection

API model ID

Muse Glimmer 30B

Not sourced

Ternary Bonsai 2 27B

prism-ml/Ternary-Bonsai-2-27B-gguf

PrismML Bonsai 2 collection

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

Muse Glimmer 30B

No comparable hosted API rate

Ternary Bonsai 2 27B

No comparable hosted API rate

PrismML Bonsai 2 collection

Documented inputs

Muse Glimmer 30B

Not sourced

Ternary Bonsai 2 27B

Not sourced

Documented outputs

Muse Glimmer 30B

Not sourced

Ternary Bonsai 2 27B

Not sourced

Provider availability

Muse Glimmer 30B

Not sourced

Ternary Bonsai 2 27B

Not sourced

Reasoning profile

Muse Glimmer 30B

Reasoning

Ternary Bonsai 2 27B

Reasoning

Weight access

Muse Glimmer 30B

Open Weight

Ternary Bonsai 2 27B

Open Weight

License

Muse Glimmer 30B

Open Weight

Ternary Bonsai 2 27B

Open Weight

Release date

Muse Glimmer 30B

2026-08-10

Ternary Bonsai 2 27B

2026-09-17

If you already use one of these models

Deployment change: The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
Quality signal: Ternary Bonsai 2 27B has the higher public score estimate, 50.78 versus 45.08, but the 90% score intervals overlap.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: Ternary Bonsai 2 27B has the larger documented window (262K).

Run the same representative tasks against both endpoints before changing production traffic.

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence31 rows

Agentic

MCP Atlas
Muse Glimmer 30B75.5%
Source
Ternary Bonsai 2 27B—
Not directly comparable
DeepSearchQA
Muse Glimmer 30B74.6%
Source
Ternary Bonsai 2 27B—
Not directly comparable
skillsBench
Muse Glimmer 30B44.3%
Source
Ternary Bonsai 2 27B—
Not directly comparable
OSWorld-Verified
Muse Glimmer 30B65.9%
Source
Ternary Bonsai 2 27B—
Not directly comparable
τ²-bench results
Muse Glimmer 30B—
Ternary Bonsai 2 27B80.2%
Source
Not directly comparable
BFCL v3
Muse Glimmer 30B—
Ternary Bonsai 2 27B74.9%
Source
Not directly comparable
Terminal-Bench 2.1
Muse Glimmer 30B—
Ternary Bonsai 2 27B52.8%
Source
Not directly comparable

Coding

SWE-bench Pro
Muse Glimmer 30B51.2%
Source
Ternary Bonsai 2 27B—
Not directly comparable
SWE-bench Verified
Muse Glimmer 30B76%
Source
Ternary Bonsai 2 27B60.8%
Source
Muse Glimmer 30B leads this result
Terminal-Bench 2.1
Muse Glimmer 30B51.7%
Source
Ternary Bonsai 2 27B52.8%
Source
Ternary Bonsai 2 27B leads this result
SciCode
Muse Glimmer 30B43.6%
Source
Ternary Bonsai 2 27B—
Not directly comparable
LiveCodeBench v6
Muse Glimmer 30B—
Ternary Bonsai 2 27B90.1%
Source
Not directly comparable
BigCodeBench
Muse Glimmer 30B—
Ternary Bonsai 2 27B58.1%
Source
Not directly comparable

Knowledge

MMLU-Redux
Muse Glimmer 30B—
Ternary Bonsai 2 27B89.1%
Source
Not directly comparable
GPQA
Muse Glimmer 30B—
Ternary Bonsai 2 27B85.8%
Source
Not directly comparable
GPQA-D
Muse Glimmer 30B—
Ternary Bonsai 2 27B85.8%
Source
Not directly comparable

Math

AIME26
Muse Glimmer 30B94.7%
Source
Ternary Bonsai 2 27B95.8%
Source
Ternary Bonsai 2 27B leads this result
GSM8K
Muse Glimmer 30B—
Ternary Bonsai 2 27B96.7%
Source
Not directly comparable
MATH-500
Muse Glimmer 30B—
Ternary Bonsai 2 27B98.8%
Source
Not directly comparable
AIME 2025
Muse Glimmer 30B—
Ternary Bonsai 2 27B95%
Source
Not directly comparable

Multimodal

CharXiv
Muse Glimmer 30B78.8%
Source
Ternary Bonsai 2 27B—
Not directly comparable
ScreenSpot Pro
Muse Glimmer 30B75.4%
Source
Ternary Bonsai 2 27B—
Not directly comparable
OmniDocBench 1.5
Muse Glimmer 30B75.8%
Source
Ternary Bonsai 2 27B—
Not directly comparable
MMMU-Pro
Muse Glimmer 30B74%
Source
Ternary Bonsai 2 27B—
Not directly comparable
CharXiv (overall)
Muse Glimmer 30B—
Ternary Bonsai 2 27B80.0%
Source
Not directly comparable
A-OKVQA
Muse Glimmer 30B—
Ternary Bonsai 2 27B86.8%
Source
Not directly comparable
OmniDocBench 1.6
Muse Glimmer 30B—
Ternary Bonsai 2 27B89.1%
Source
Not directly comparable
RealWorldQA
Muse Glimmer 30B—
Ternary Bonsai 2 27B80.1%
Source
Not directly comparable
OCRBench V2
Muse Glimmer 30B—
Ternary Bonsai 2 27B56.9%
Source
Not directly comparable

Instruction following

IFBench
Muse Glimmer 30B77%
Source
Ternary Bonsai 2 27B74%
Source
Muse Glimmer 30B leads this result
IFEval
Muse Glimmer 30B—
Ternary Bonsai 2 27B91.3%
Source
Not directly comparable

Questions

Which is better, Muse Glimmer 30B or Ternary Bonsai 2 27B?

Ternary Bonsai 2 27B has the higher public score estimate, 50.78 versus 45.08, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, Muse Glimmer 30B or Ternary Bonsai 2 27B?

Ternary Bonsai 2 27B scores higher for coding on the public lane, 49.9 to 48.5. Muse Glimmer 30B and Ternary Bonsai 2 27B are scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which is better for agentic tasks, Muse Glimmer 30B or Ternary Bonsai 2 27B?

Ternary Bonsai 2 27B scores higher for agentic tasks on the public lane, 49.9 to 43.3. Muse Glimmer 30B and Ternary Bonsai 2 27B are scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which costs less, Muse Glimmer 30B or Ternary Bonsai 2 27B?

Both models do not have comparable published API token rates, so this page does not name a universal price winner.

Which has the larger context window, Muse Glimmer 30B or Ternary Bonsai 2 27B?

Ternary Bonsai 2 27B has the larger documented context window: 262K, compared with 131K.

Related comparisons

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Last updated September 18, 2026

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Muse Glimmer 30B vs Ternary Bonsai 2 27B

Which one for your work

Long documents

Coding work

Agentic work

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

What is actually comparable

Category results, on a stated basis

Instruction following

Agentic

Coding

Knowledge

Reasoning

Math

Multilingual

Multimodal

Shape of the matched evidence

What each workload costs

Chat turn

Repository review

Cache-heavy agent loop

Specification differences

Context window

API model ID

Cached-input rate

Documented inputs

Documented outputs

Provider availability

Reasoning profile

Weight access

License

Release date

Benchmark evidence

Agentic

Coding

Knowledge

Math

Multimodal

Instruction following

Questions

Which is better, Muse Glimmer 30B or Ternary Bonsai 2 27B?

Which is better for coding, Muse Glimmer 30B or Ternary Bonsai 2 27B?

Which is better for agentic tasks, Muse Glimmer 30B or Ternary Bonsai 2 27B?

Which costs less, Muse Glimmer 30B or Ternary Bonsai 2 27B?

Which has the larger context window, Muse Glimmer 30B or Ternary Bonsai 2 27B?

Related comparisons

Watch Muse Glimmer 30B vs Ternary Bonsai 2 27B