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
LFM2.5-24B-A2B vs Ternary Bonsai 2 27B
Updated October 13, 2026. Rank says Ternary Bonsai 2 27B is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
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Decision reading
Ternary Bonsai 2 27B has the higher public point estimate, 53.66 versus 37.8. Their conditional score ranges overlap. These ranges do not establish rank confidence. 6 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 6
- LFM2.5-24B-A2B only
- 7
- Ternary Bonsai 2 27B only
- 15
- Like-for-like categories
- 1 / 8
Estimated: LFM2.5-24B-A2B and Ternary Bonsai 2 27B. Conditional ranges do not establish rank confidence.How the comparison works
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.
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Ternary Bonsai 2 27B is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Ternary Bonsai 2 27B is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limitedChat 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-ratesCache-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. LFM2.5-24B-A2B does not fit this workload in one request. Ternary Bonsai 2 27B has no comparable published API token rate.
Confidence: listed-ratesRepository 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
Which one for a specific job
Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.
The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.
28.5LFM2.5-24B-A2B—Ternary Bonsai 2 27B
Not comparable · BenchAlign v5.8
The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Same basis rules as the category table below
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
7 LFM2.5-24B-A2B only
6 Shared
15 Ternary Bonsai 2 27B only
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.
GPQAKnowledge
Normalized gap 11.3LFM2.5-24B-A2B:74.4%Ternary Bonsai 2 27B:85.8%SWE-bench VerifiedCoding
Normalized gap 9.8LFM2.5-24B-A2B:51%Ternary Bonsai 2 27B:60.8%IFBenchInstruction following
Normalized gap 0.7LFM2.5-24B-A2B:74.7%Ternary Bonsai 2 27B:74%
Bars run 0–100 on each benchmark’s normalized display scale
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign v5.8 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- LFM2.5-24B-A2B
- 75.6
- #61/128
- Ternary Bonsai 2 27B
- 74.0
- #66/128
- Basis
- Provisional lane · 1 vs 1 weighted rows
- Reading
- LFM2.5-24B-A2B leads
Agentic
Not comparable- LFM2.5-24B-A2B
- 30.2
- Estimated · #88/124
- Ternary Bonsai 2 27B
- Not ranked
- Basis
- BenchAlign v5.8 lane · 5 vs 3 public rows
- Reading
- Not comparable
Coding
Not comparable- LFM2.5-24B-A2B
- 28.5
- Estimated · #98/149
- Ternary Bonsai 2 27B
- Not ranked
- Basis
- BenchAlign v5.8 lane · 2 vs 4 public rows
- Reading
- Not comparable
Reasoning
Not comparable- LFM2.5-24B-A2B
- Not ranked
- Ternary Bonsai 2 27B
- 78.8
- Unranked · 1 rankable row
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- LFM2.5-24B-A2B
- Not ranked
- Ternary Bonsai 2 27B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- LFM2.5-24B-A2B
- 40.8
- Estimated · #112/181
- Ternary Bonsai 2 27B
- Not ranked
- Basis
- BenchAlign v5.8 lane · 3 vs 3 public rows
- Reading
- Not comparable
Multilingual
Not comparable- LFM2.5-24B-A2B
- Not ranked
- Ternary Bonsai 2 27B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- LFM2.5-24B-A2B
- Not ranked
- Ternary Bonsai 2 27B
- 86.2
- Unranked · 4 rankable rows
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
| Category | LFM2.5-24B-A2B | Ternary Bonsai 2 27B | Basis | Reading |
|---|---|---|---|---|
| Instruction following | 75.6#61/128 | 74.0#66/128 | Like-for-likeProvisional lane · 1 vs 1 weighted rows | LFM2.5-24B-A2B leads |
| Agentic | 30.2Estimated · #88/124 | Not ranked | Not comparableBenchAlign v5.8 lane · 5 vs 3 public rows | Not comparable |
| Coding | 28.5Estimated · #98/149 | Not ranked | Not comparableBenchAlign v5.8 lane · 2 vs 4 public rows | Not comparable |
| Reasoning | Not ranked | 78.8Unranked · 1 rankable row | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Knowledge | 40.8Estimated · #112/181 | Not ranked | Not comparableBenchAlign v5.8 lane · 3 vs 3 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | 86.2Unranked · 4 rankable rows | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
Bars run 0–100Methodology
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
- LFM2.5-24B-A2B
- $0.0006
- Fits in one request
- Ternary Bonsai 2 27B
- Self-hosted; infrastructure cost varies
- Fits in one request
Ternary Bonsai 2 27B has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- LFM2.5-24B-A2B
- $0.008
- Fits in one request
- Ternary Bonsai 2 27B
- Self-hosted; infrastructure cost varies
- Fits in one request
Ternary Bonsai 2 27B has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- LFM2.5-24B-A2B
- $0.022
- Does not fit in one request
- Ternary Bonsai 2 27B
- Self-hosted; infrastructure cost varies
- Fits in one request
- Cached-input rate unavailable
LFM2.5-24B-A2B does not fit this workload in one request. Ternary Bonsai 2 27B has no comparable published API token rate.
Cached input falls back to the list input rate only where a cached rate is unpublished
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationLFM2.5-24B-A2BTernary Bonsai 2 27B
Context window
Maximum documented context; output-token limits may be lower.
LFM2.5-24B-A2B
Ternary Bonsai 2 27B
API model ID
LFM2.5-24B-A2B
Not sourced
Ternary Bonsai 2 27B
prism-ml/Ternary-Bonsai-2-27B-gguf
PrismML Bonsai 2 collectionCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
LFM2.5-24B-A2B
$0.05 per 1M cached input tokens
Liquid AI launch postTernary Bonsai 2 27B
No comparable hosted API rate
PrismML Bonsai 2 collectionDocumented inputs
LFM2.5-24B-A2B
Not sourced
Ternary Bonsai 2 27B
Not sourced
Documented outputs
LFM2.5-24B-A2B
Not sourced
Ternary Bonsai 2 27B
Not sourced
Provider availability
LFM2.5-24B-A2B
Not sourced
Ternary Bonsai 2 27B
Not sourced
Reasoning profile
LFM2.5-24B-A2B
Reasoning
Ternary Bonsai 2 27B
Reasoning
Weight access
LFM2.5-24B-A2B
Open Weight
Ternary Bonsai 2 27B
Open Weight
License
LFM2.5-24B-A2B
Open Weight
Ternary Bonsai 2 27B
Open Weight
Release date
LFM2.5-24B-A2B
2026-10-13
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 point estimate, 53.66 versus 37.8. Their conditional score ranges overlap. These ranges do not establish rank confidence.
- 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.
Questions
Which is better, LFM2.5-24B-A2B or Ternary Bonsai 2 27B?
Ternary Bonsai 2 27B has the higher public point estimate, 53.66 versus 37.8. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, LFM2.5-24B-A2B or Ternary Bonsai 2 27B?
Ternary Bonsai 2 27B is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, LFM2.5-24B-A2B or Ternary Bonsai 2 27B?
Ternary Bonsai 2 27B is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, LFM2.5-24B-A2B 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, LFM2.5-24B-A2B or Ternary Bonsai 2 27B?
Ternary Bonsai 2 27B has the larger documented context window: 262K, compared with 131K.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence28 rows
Agentic
AutomationBench
LFM2.5-24B-A2B27.7%SourceTernary Bonsai 2 27B—Not directly comparable
BFCL v4
LFM2.5-24B-A2B65.0%SourceTernary Bonsai 2 27B—Not directly comparable
τ³-bench results
LFM2.5-24B-A2B8.0%SourceTernary Bonsai 2 27B—Not directly comparable
PinchBench
LFM2.5-24B-A2B83.2%SourceTernary Bonsai 2 27B—Not directly comparable
WebArena-Verified
LFM2.5-24B-A2B26.8%SourceTernary Bonsai 2 27B—Not directly comparable
τ²-bench results
LFM2.5-24B-A2B—Ternary Bonsai 2 27B80.2%SourceNot directly comparable
BFCL v3
LFM2.5-24B-A2B—Ternary Bonsai 2 27B74.9%SourceNot directly comparable
Terminal-Bench 2.1
LFM2.5-24B-A2B—Ternary Bonsai 2 27B52.8%SourceNot directly comparable
Coding
SciCode
LFM2.5-24B-A2B32.7%SourceTernary Bonsai 2 27B—Not directly comparable
SWE-bench Verified
LFM2.5-24B-A2B51%SourceTernary Bonsai 2 27B60.8%SourceTernary Bonsai 2 27B leads this result
LiveCodeBench v6
LFM2.5-24B-A2B—Ternary Bonsai 2 27B90.1%SourceNot directly comparable
BigCodeBench
LFM2.5-24B-A2B—Ternary Bonsai 2 27B58.1%SourceNot directly comparable
Terminal-Bench 2.1
LFM2.5-24B-A2B—Ternary Bonsai 2 27B52.8%SourceNot directly comparable
Multimodal
CharXiv (overall)
LFM2.5-24B-A2B—Ternary Bonsai 2 27B80.0%SourceNot directly comparable
A-OKVQA
LFM2.5-24B-A2B—Ternary Bonsai 2 27B86.8%SourceNot directly comparable
OmniDocBench 1.6
LFM2.5-24B-A2B—Ternary Bonsai 2 27B89.1%SourceNot directly comparable
RealWorldQA
LFM2.5-24B-A2B—Ternary Bonsai 2 27B80.1%SourceNot directly comparable
OCRBench V2
LFM2.5-24B-A2B—Ternary Bonsai 2 27B56.9%SourceNot directly comparable
Knowledge
GPQA
LFM2.5-24B-A2B74.4%SourceTernary Bonsai 2 27B85.8%SourceTernary Bonsai 2 27B leads this result
GPQA-D
LFM2.5-24B-A2B74.4%SourceTernary Bonsai 2 27B85.8%SourceTernary Bonsai 2 27B leads this result
HLE
LFM2.5-24B-A2B7.4%SourceTernary Bonsai 2 27B—Not directly comparable
MMLU-Redux
LFM2.5-24B-A2B—Ternary Bonsai 2 27B89.1%SourceNot directly comparable
Instruction following
Math
MATH-500
LFM2.5-24B-A2B96.0%SourceTernary Bonsai 2 27B98.8%SourceTernary Bonsai 2 27B leads this result
GSM8K
LFM2.5-24B-A2B—Ternary Bonsai 2 27B96.7%SourceNot directly comparable
AIME 2025
LFM2.5-24B-A2B—Ternary Bonsai 2 27B95%SourceNot directly comparable
AIME26
LFM2.5-24B-A2B—Ternary Bonsai 2 27B95.8%SourceNot directly comparable
28 public results · 6 shared
Watch LFM2.5-24B-A2B vs Ternary Bonsai 2 27B
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Last updated October 13, 2026