Agentic
Not comparable- 1-bit Bonsai 4B
- Not measured
- Ternary Bonsai 1.7B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Model comparison
1-bit Bonsai 4B vs Ternary Bonsai 1.7B
Updated July 29, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead.
0 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.
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.
No workload recommendation clears the current evidence threshold.
Use the matched evidence, workload costs, and sourced specifications below instead of treating a point score as a universal answer.
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
Confidence: limited
Long documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
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. 1-bit Bonsai 4B does not fit this workload in one request. Ternary Bonsai 1.7B does not fit this workload in one request. 1-bit Bonsai 4B has no comparable published API token rate. Ternary Bonsai 1.7B has no comparable published API token rate.
Confidence: listed-rates
Repository review cost
50K fresh input + 3K 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. 1-bit Bonsai 4B does not fit this workload in one request. Ternary Bonsai 1.7B does not fit this workload in one request. 1-bit Bonsai 4B has no comparable published API token rate. Ternary Bonsai 1.7B has no comparable published API token rate.
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.
Evidence parity totals are not available.
- Shared results
- 0
- 1-bit Bonsai 4B only
- 0
- Ternary Bonsai 1.7B only
- 0
- Like-for-like categories
- 0 / 8
Category results, on a stated basis
Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.
Coding
Not comparable- 1-bit Bonsai 4B
- Not measured
- Ternary Bonsai 1.7B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Reasoning
Not comparable- 1-bit Bonsai 4B
- Not measured
- Ternary Bonsai 1.7B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- 1-bit Bonsai 4B
- Not measured
- Ternary Bonsai 1.7B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Math
Not comparable- 1-bit Bonsai 4B
- Not measured
- Ternary Bonsai 1.7B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- 1-bit Bonsai 4B
- Not measured
- Ternary Bonsai 1.7B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- 1-bit Bonsai 4B
- Not measured
- Ternary Bonsai 1.7B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- 1-bit Bonsai 4B
- Not measured
- Ternary Bonsai 1.7B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | 1-bit Bonsai 4B | Ternary Bonsai 1.7B | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Coding | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Reasoning | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Knowledge | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Math | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Instruction following | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
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.
A shared-evidence shape is not available.
BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.
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
- 1-bit Bonsai 4B
- Self-hosted; infrastructure cost varies
- Fits in one request
- Ternary Bonsai 1.7B
- Self-hosted; infrastructure cost varies
- Fits in one request
1-bit Bonsai 4B has no comparable published API token rate. Ternary Bonsai 1.7B has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- 1-bit Bonsai 4B
- Self-hosted; infrastructure cost varies
- Does not fit in one request
- Ternary Bonsai 1.7B
- Self-hosted; infrastructure cost varies
- Does not fit in one request
1-bit Bonsai 4B does not fit this workload in one request. Ternary Bonsai 1.7B does not fit this workload in one request. 1-bit Bonsai 4B has no comparable published API token rate. Ternary Bonsai 1.7B has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- 1-bit Bonsai 4B
- Self-hosted; infrastructure cost varies
- Does not fit in one request
- Cached-input rate unavailable
- Ternary Bonsai 1.7B
- Self-hosted; infrastructure cost varies
- Does not fit in one request
- Cached-input rate unavailable
1-bit Bonsai 4B does not fit this workload in one request. Ternary Bonsai 1.7B does not fit this workload in one request. 1-bit Bonsai 4B has no comparable published API token rate. Ternary Bonsai 1.7B 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.
Specification1-bit Bonsai 4BTernary Bonsai 1.7B
Context window
Maximum documented context; output-token limits may be lower.
1-bit Bonsai 4B
32K
Ternary Bonsai 1.7B
32K
API model ID
1-bit Bonsai 4B
Not sourced
Ternary Bonsai 1.7B
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
1-bit Bonsai 4B
No comparable hosted API rate
Ternary Bonsai 1.7B
No comparable hosted API rate
Documented inputs
1-bit Bonsai 4B
Not sourced
Ternary Bonsai 1.7B
Not sourced
Documented outputs
1-bit Bonsai 4B
Not sourced
Ternary Bonsai 1.7B
Not sourced
Provider availability
1-bit Bonsai 4B
Not sourced
Ternary Bonsai 1.7B
Not sourced
Reasoning profile
1-bit Bonsai 4B
Non-Reasoning
Ternary Bonsai 1.7B
Non-Reasoning
Weight access
1-bit Bonsai 4B
Open Weight
Ternary Bonsai 1.7B
Open Weight
License
1-bit Bonsai 4B
Open Weight
Ternary Bonsai 1.7B
Open Weight
Release date
1-bit Bonsai 4B
2026-03-31
Ternary Bonsai 1.7B
2026-04-16
If you already use one of these models
- Deployment change
- Both entries list Prism ML as the provider. Confirm endpoint, model ID, limits, and feature support before switching.
- Quality signal
- The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- Both models list 32K.
Run the same representative tasks against both endpoints before changing production traffic.
Frequently asked questions
Which is better, 1-bit Bonsai 4B or Ternary Bonsai 1.7B?
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, 1-bit Bonsai 4B or Ternary Bonsai 1.7B?
The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.
Which is better for agentic tasks, 1-bit Bonsai 4B or Ternary Bonsai 1.7B?
The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.
Which costs less, 1-bit Bonsai 4B or Ternary Bonsai 1.7B?
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, 1-bit Bonsai 4B or Ternary Bonsai 1.7B?
Both models list the same context window, 32K.
Related comparisons
Last updated July 29, 2026
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