Agentic
Not comparable- 1-bit Bonsai 1.7B
- Not measured
- Ultravox v0.4.1 Llama 3.1 8B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Model comparison
1-bit Bonsai 1.7B vs Ultravox v0.4.1 Llama 3.1 8B
Updated August 4, 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
Not enough matched evidence
A complete context comparison is not sourced.
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. 1-bit Bonsai 1.7B does not fit this workload in one request. 1-bit Bonsai 1.7B has no comparable published API token rate. Ultravox v0.4.1 Llama 3.1 8B 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 1.7B does not fit this workload in one request. 1-bit Bonsai 1.7B has no comparable published API token rate. Ultravox v0.4.1 Llama 3.1 8B 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 1.7B only
- 0
- Ultravox v0.4.1 Llama 3.1 8B 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 1.7B
- Not measured
- Ultravox v0.4.1 Llama 3.1 8B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Reasoning
Not comparable- 1-bit Bonsai 1.7B
- Not measured
- Ultravox v0.4.1 Llama 3.1 8B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- 1-bit Bonsai 1.7B
- Not measured
- Ultravox v0.4.1 Llama 3.1 8B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Math
Not comparable- 1-bit Bonsai 1.7B
- Not measured
- Ultravox v0.4.1 Llama 3.1 8B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- 1-bit Bonsai 1.7B
- Not measured
- Ultravox v0.4.1 Llama 3.1 8B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- 1-bit Bonsai 1.7B
- Not measured
- Ultravox v0.4.1 Llama 3.1 8B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- 1-bit Bonsai 1.7B
- Not measured
- Ultravox v0.4.1 Llama 3.1 8B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | 1-bit Bonsai 1.7B | Ultravox v0.4.1 Llama 3.1 8B | 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 1.7B
- Self-hosted; infrastructure cost varies
- Fits in one request
- Ultravox v0.4.1 Llama 3.1 8B
- API rate not published
- Fit state unavailable
1-bit Bonsai 1.7B has no comparable published API token rate. Ultravox v0.4.1 Llama 3.1 8B has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- 1-bit Bonsai 1.7B
- Self-hosted; infrastructure cost varies
- Does not fit in one request
- Ultravox v0.4.1 Llama 3.1 8B
- API rate not published
- Fit state unavailable
1-bit Bonsai 1.7B does not fit this workload in one request. 1-bit Bonsai 1.7B has no comparable published API token rate. Ultravox v0.4.1 Llama 3.1 8B has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- 1-bit Bonsai 1.7B
- Self-hosted; infrastructure cost varies
- Does not fit in one request
- Cached-input rate unavailable
- Ultravox v0.4.1 Llama 3.1 8B
- API rate not published
- Fit state unavailable
- Cached-input rate unavailable
1-bit Bonsai 1.7B does not fit this workload in one request. 1-bit Bonsai 1.7B has no comparable published API token rate. Ultravox v0.4.1 Llama 3.1 8B 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 1.7BUltravox v0.4.1 Llama 3.1 8B
Context window
Maximum documented context; output-token limits may be lower.
1-bit Bonsai 1.7B
32K
Ultravox v0.4.1 Llama 3.1 8B
N/A
API model ID
1-bit Bonsai 1.7B
Not sourced
Ultravox v0.4.1 Llama 3.1 8B
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 1.7B
No comparable hosted API rate
Ultravox v0.4.1 Llama 3.1 8B
No comparable hosted API rate
Fixie AI model documentationDocumented inputs
1-bit Bonsai 1.7B
Not sourced
Ultravox v0.4.1 Llama 3.1 8B
Not sourced
Documented outputs
1-bit Bonsai 1.7B
Not sourced
Ultravox v0.4.1 Llama 3.1 8B
Not sourced
Provider availability
1-bit Bonsai 1.7B
Not sourced
Ultravox v0.4.1 Llama 3.1 8B
Not sourced
Reasoning profile
1-bit Bonsai 1.7B
Non-Reasoning
Ultravox v0.4.1 Llama 3.1 8B
Non-Reasoning
Weight access
1-bit Bonsai 1.7B
Open Weight
Ultravox v0.4.1 Llama 3.1 8B
Open Weight
License
1-bit Bonsai 1.7B
Open Weight
Ultravox v0.4.1 Llama 3.1 8B
Open Weight
Release date
1-bit Bonsai 1.7B
2026-03-31
Ultravox v0.4.1 Llama 3.1 8B
Not sourced
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
- 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
- A complete documented context comparison is not available.
Run the same representative tasks against both endpoints before changing production traffic.
Frequently asked questions
Which is better, 1-bit Bonsai 1.7B or Ultravox v0.4.1 Llama 3.1 8B?
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 1.7B or Ultravox v0.4.1 Llama 3.1 8B?
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 1.7B or Ultravox v0.4.1 Llama 3.1 8B?
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 1.7B or Ultravox v0.4.1 Llama 3.1 8B?
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 1.7B or Ultravox v0.4.1 Llama 3.1 8B?
A complete documented context-window comparison is not available.
Related comparisons
Last updated August 4, 2026
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