Agentic
Not comparable- Baichuan-Audio
- Not measured
- Muse Spark 1.1
- 80.4
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Model comparison
Baichuan-Audio vs Muse Spark 1.1
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
A complete comparable API-rate estimate is not available for both models.
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.
21 Muse Spark 1.1 only
- Shared results
- 0
- Baichuan-Audio only
- 0
- Muse Spark 1.1 only
- 21
- 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- Baichuan-Audio
- Not measured
- Muse Spark 1.1
- 61.5
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
Reasoning
Not comparable- Baichuan-Audio
- Not measured
- Muse Spark 1.1
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- Baichuan-Audio
- Not measured
- Muse Spark 1.1
- 62.1
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
Math
Not comparable- Baichuan-Audio
- Not measured
- Muse Spark 1.1
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- Baichuan-Audio
- Not measured
- Muse Spark 1.1
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- Baichuan-Audio
- Not measured
- Muse Spark 1.1
- 88.4
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
Instruction following
Not comparable- Baichuan-Audio
- Not measured
- Muse Spark 1.1
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | Baichuan-Audio | Muse Spark 1.1 | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | Not measured | 80.4 | Not comparable0 vs 2 rows | Not comparable |
| Coding | Not measured | 61.5 | Not comparable0 vs 1 rows | Not comparable |
| Reasoning | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Knowledge | Not measured | 62.1 | Not comparable0 vs 1 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 | 88.4 | Not comparable0 vs 1 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
- Baichuan-Audio
- API rate not published
- Fit state unavailable
- Muse Spark 1.1
- API rate not published
- Fits in one request
Baichuan-Audio has no comparable published API token rate. Muse Spark 1.1 has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Baichuan-Audio
- API rate not published
- Fit state unavailable
- Muse Spark 1.1
- API rate not published
- Fits in one request
Baichuan-Audio has no comparable published API token rate. Muse Spark 1.1 has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Baichuan-Audio
- API rate not published
- Fit state unavailable
- Cached-input rate unavailable
- Muse Spark 1.1
- API rate not published
- Fits in one request
- Cached-input rate unavailable
Baichuan-Audio has no comparable published API token rate. Muse Spark 1.1 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.
SpecificationBaichuan-AudioMuse Spark 1.1
Context window
Maximum documented context; output-token limits may be lower.
Baichuan-Audio
N/A
Muse Spark 1.1
1M
API model ID
Baichuan-Audio
Not sourced
Muse Spark 1.1
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Baichuan-Audio
No comparable hosted API rate
Baichuan model documentationMuse Spark 1.1
No comparable hosted API rate
Documented inputs
Baichuan-Audio
Not sourced
Muse Spark 1.1
Not sourced
Documented outputs
Baichuan-Audio
Not sourced
Muse Spark 1.1
Not sourced
Provider availability
Baichuan-Audio
Not sourced
Muse Spark 1.1
Not sourced
Reasoning profile
Baichuan-Audio
Non-Reasoning
Muse Spark 1.1
Reasoning
Weight access
Baichuan-Audio
Open Weight
Muse Spark 1.1
Proprietary
License
Baichuan-Audio
Open Weight
Muse Spark 1.1
Proprietary
Release date
Baichuan-Audio
2025-02-24
Muse Spark 1.1
2026-07-09
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.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence21 rows
Agentic
Terminal-Bench 2.0
Baichuan-Audio—Muse Spark 1.180%SourceNot directly comparable
MCP Atlas
Baichuan-Audio—Muse Spark 1.188.1%SourceNot directly comparable
Toolathlon
Baichuan-Audio—Muse Spark 1.175.6%SourceNot directly comparable
OSWorld-Verified
Baichuan-Audio—Muse Spark 1.180.8%SourceNot directly comparable
WebArena-Verified
Baichuan-Audio—Muse Spark 1.169%SourceNot directly comparable
DeepSearchQA
Baichuan-Audio—Muse Spark 1.184.9%SourceNot directly comparable
CyberGym
Baichuan-Audio—Muse Spark 1.159.0%SourceNot directly comparable
Finance Agent v2
Baichuan-Audio—Muse Spark 1.157.2%SourceNot directly comparable
deepSwe
Baichuan-Audio—Muse Spark 1.153.3%SourceNot directly comparable
OSWorld 2.0
Baichuan-Audio—Muse Spark 1.114.2%SourceNot directly comparable
JobBench
Baichuan-Audio—Muse Spark 1.154.7%SourceNot directly comparable
Cybench
Baichuan-Audio—Muse Spark 1.192.9%SourceNot directly comparable
ExploitGym
Baichuan-Audio—Muse Spark 1.10.8%SourceNot directly comparable
Coding
Reasoning
MRCR 1M
Baichuan-Audio—Muse Spark 1.154.1%SourceNot directly comparable
Knowledge
Multimodal
Frequently asked questions
Which is better, Baichuan-Audio or Muse Spark 1.1?
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, Baichuan-Audio or Muse Spark 1.1?
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, Baichuan-Audio or Muse Spark 1.1?
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, Baichuan-Audio or Muse Spark 1.1?
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, Baichuan-Audio or Muse Spark 1.1?
A complete documented context-window comparison is not available.
Related comparisons
Last updated August 4, 2026
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