Instruction following
Like-for-like- LFM2.5-8B-A1B
- 35.4
- #108/124
- Ternary Bonsai 2 27B
- 71.0
- #64/124
- Basis
- Provisional lane · 1 vs 1 weighted rows
- Reading
- Ternary Bonsai 2 27B leads
LFM2.5-8B-A1B vs Ternary Bonsai 2 27B
Decision reading
Ternary Bonsai 2 27B has the higher public score estimate, 50.78 versus 39.28, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
6 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Updated September 18, 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
Share or export
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
LFM2.5-8B-A1B is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
LFM2.5-8B-A1B 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. LFM2.5-8B-A1B does not fit this workload in one request. LFM2.5-8B-A1B 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.
1 LFM2.5-8B-A1B only
6 Shared
15 Ternary Bonsai 2 27B only
- Shared results
- 6
- LFM2.5-8B-A1B only
- 1
- Ternary Bonsai 2 27B only
- 15
- Like-for-like categories
- 1 / 8
2 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.
Agentic
Directional only- LFM2.5-8B-A1B
- 43.7
- Estimated · #98/154
- Ternary Bonsai 2 27B
- 49.9
- Estimated · #59/154
- Basis
- BenchAlign lane · 2 vs 3 public rows
- Reading
- Directional only
Knowledge
Directional only- LFM2.5-8B-A1B
- 34.5
- Supported · #165/184
- Ternary Bonsai 2 27B
- 50.6
- Estimated · #78/184
- Basis
- BenchAlign lane · 0 vs 3 public rows
- Reading
- Directional only
Coding
Not comparable- LFM2.5-8B-A1B
- Not ranked
- Ternary Bonsai 2 27B
- 49.9
- Estimated · #64/154
- Basis
- BenchAlign lane · 0 vs 4 public rows
- Reading
- Not comparable
Reasoning
Not comparable- LFM2.5-8B-A1B
- 21.0
- 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- LFM2.5-8B-A1B
- 17.3
- Unranked · 3 rankable rows
- Ternary Bonsai 2 27B
- 76.8
- Unranked · 4 rankable rows
- Basis
- Provisional lane · 1 vs 1 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- LFM2.5-8B-A1B
- Not ranked
- Ternary Bonsai 2 27B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- LFM2.5-8B-A1B
- Not ranked
- Ternary Bonsai 2 27B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | LFM2.5-8B-A1B | Ternary Bonsai 2 27B | Basis | Reading |
|---|---|---|---|---|
| Instruction following | 35.4#108/124 | 71.0#64/124 | Like-for-likeProvisional lane · 1 vs 1 weighted rows | Ternary Bonsai 2 27B leads |
| Agentic | 43.7Estimated · #98/154 | 49.9Estimated · #59/154 | Directional onlyBenchAlign lane · 2 vs 3 public rows | Directional only |
| Knowledge | 34.5Supported · #165/184 | 50.6Estimated · #78/184 | Directional onlyBenchAlign lane · 0 vs 3 public rows | Directional only |
| Coding | Not ranked | 49.9Estimated · #64/154 | Not comparableBenchAlign lane · 0 vs 4 public rows | Not comparable |
| Reasoning | 21.0Unranked · 2 rankable rows | 73.9Unranked · 1 rankable row | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 17.3Unranked · 3 rankable rows | 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 | Not ranked | Not ranked | Not comparableProvisional lane · 0 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.
AIME26
Math
LFM2.5-8B-A1B: 50.0%Ternary Bonsai 2 27B: 95.8%Normalized gap 45.8LFM2.5-8B-A1B source Ternary Bonsai 2 27B sourceIFBench
Instruction following
LFM2.5-8B-A1B: 56.5%Ternary Bonsai 2 27B: 74%Normalized gap 17.5LFM2.5-8B-A1B 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
- LFM2.5-8B-A1B
- Self-hosted; infrastructure cost varies
- Fits in one request
- Ternary Bonsai 2 27B
- Self-hosted; infrastructure cost varies
- Fits in one request
LFM2.5-8B-A1B 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
- LFM2.5-8B-A1B
- Self-hosted; infrastructure cost varies
- Fits in one request
- Ternary Bonsai 2 27B
- Self-hosted; infrastructure cost varies
- Fits in one request
LFM2.5-8B-A1B 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
- LFM2.5-8B-A1B
- 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
LFM2.5-8B-A1B does not fit this workload in one request. LFM2.5-8B-A1B 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.
SpecificationLFM2.5-8B-A1BTernary Bonsai 2 27B
Context window
Maximum documented context; output-token limits may be lower.
LFM2.5-8B-A1B
128K
Ternary Bonsai 2 27B
API model ID
LFM2.5-8B-A1B
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.
LFM2.5-8B-A1B
No comparable hosted API rate
Ternary Bonsai 2 27B
No comparable hosted API rate
PrismML Bonsai 2 collectionDocumented inputs
LFM2.5-8B-A1B
Not sourced
Ternary Bonsai 2 27B
Not sourced
Documented outputs
LFM2.5-8B-A1B
Not sourced
Ternary Bonsai 2 27B
Not sourced
Provider availability
LFM2.5-8B-A1B
Not sourced
Ternary Bonsai 2 27B
Not sourced
Reasoning profile
LFM2.5-8B-A1B
Reasoning
Ternary Bonsai 2 27B
Reasoning
Weight access
LFM2.5-8B-A1B
Open Weight
Ternary Bonsai 2 27B
Open Weight
License
LFM2.5-8B-A1B
Open Weight
Ternary Bonsai 2 27B
Open Weight
Release date
LFM2.5-8B-A1B
2026-05-28
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 39.28, 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 evidence22 rows
Agentic
BFCL v4
LFM2.5-8B-A1B49.7%SourceTernary Bonsai 2 27B—Not directly comparable
τ²-bench results
LFM2.5-8B-A1B88.1%SourceTernary Bonsai 2 27B80.2%SourceLFM2.5-8B-A1B leads this result
BFCL v3
LFM2.5-8B-A1B—Ternary Bonsai 2 27B74.9%SourceNot directly comparable
Terminal-Bench 2.1
LFM2.5-8B-A1B—Ternary Bonsai 2 27B52.8%SourceNot directly comparable
Coding
LiveCodeBench v6
LFM2.5-8B-A1B—Ternary Bonsai 2 27B90.1%SourceNot directly comparable
BigCodeBench
LFM2.5-8B-A1B—Ternary Bonsai 2 27B58.1%SourceNot directly comparable
SWE-bench Verified
LFM2.5-8B-A1B—Ternary Bonsai 2 27B60.8%SourceNot directly comparable
Terminal-Bench 2.1
LFM2.5-8B-A1B—Ternary Bonsai 2 27B52.8%SourceNot directly comparable
Knowledge
Math
MATH-500
LFM2.5-8B-A1B88.8%SourceTernary Bonsai 2 27B98.8%SourceTernary Bonsai 2 27B leads this result
AIME 2025
LFM2.5-8B-A1B42.5%SourceTernary Bonsai 2 27B95%SourceTernary Bonsai 2 27B leads this result
AIME26
LFM2.5-8B-A1B50.0%SourceTernary Bonsai 2 27B95.8%SourceTernary Bonsai 2 27B leads this result
GSM8K
LFM2.5-8B-A1B—Ternary Bonsai 2 27B96.7%SourceNot directly comparable
Multimodal
CharXiv (overall)
LFM2.5-8B-A1B—Ternary Bonsai 2 27B80.0%SourceNot directly comparable
A-OKVQA
LFM2.5-8B-A1B—Ternary Bonsai 2 27B86.8%SourceNot directly comparable
OmniDocBench 1.6
LFM2.5-8B-A1B—Ternary Bonsai 2 27B89.1%SourceNot directly comparable
RealWorldQA
LFM2.5-8B-A1B—Ternary Bonsai 2 27B80.1%SourceNot directly comparable
OCRBench V2
LFM2.5-8B-A1B—Ternary Bonsai 2 27B56.9%SourceNot directly comparable
Instruction following
Questions
Which is better, LFM2.5-8B-A1B or Ternary Bonsai 2 27B?
Ternary Bonsai 2 27B has the higher public score estimate, 50.78 versus 39.28, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, LFM2.5-8B-A1B or Ternary Bonsai 2 27B?
LFM2.5-8B-A1B is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, LFM2.5-8B-A1B or Ternary Bonsai 2 27B?
Ternary Bonsai 2 27B scores higher for agentic tasks on the public lane, 49.9 to 43.7. LFM2.5-8B-A1B 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, LFM2.5-8B-A1B 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, LFM2.5-8B-A1B or Ternary Bonsai 2 27B?
Ternary Bonsai 2 27B has the larger documented context window: 262K, compared with 128K.
Related comparisons
Last updated September 18, 2026
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