Coding work
Code generation, repair, and software-engineering tasks
Muse Spark 1.2
Muse Spark 1.2 leads on the public coding lane, 55.2 to 48.7, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Muse Spark 1.2 vs Qwen3.8-27B
Updated September 28, 2026. Rank says Muse Spark 1.2 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
Decision reading
Muse Spark 1.2 has the higher public score estimate, 64.92 versus 55.26, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 7 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 7
- Muse Spark 1.2 only
- 1
- Qwen3.8-27B only
- 26
- Like-for-like categories
- 3 / 8
Estimated: Muse Spark 1.2 and Qwen3.8-27BHow 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.
Agentic work
Tool use, computer use, and multi-step task completion
Qwen3.8-27B
Qwen3.8-27B leads on the public agentic lane, 61.2 to 59.3, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limitedLong documents
Prompts that approach the documented context limit
Muse Spark 1.2
Muse Spark 1.2 has the larger documented context window.
Confidence: documented
Show secondary and unsupported calls
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-ratesCache-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-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.
55.2Muse Spark 1.248.7Qwen3.8-27B
Like-for-like · BenchAlign v5.7
Muse Spark 1.2 leads the like-for-like coding row, although the 90% intervals overlap.
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.
1 Muse Spark 1.2 only
7 Shared
26 Qwen3.8-27B 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.
MMLU-Pro (Vals)Knowledge
Normalized gap 4.0Muse Spark 1.2:88.3%Qwen3.8-27B:84.3%
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 v5.7 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
Like-for-like- Muse Spark 1.2
- 59.3
- Supported · #22/117
- Qwen3.8-27B
- 61.2
- Supported · #18/117
- Basis
- BenchAlign v5.7 lane · 2 vs 8 public rows
- Reading
- Qwen3.8-27B leads · intervals overlap
Coding
Like-for-like- Muse Spark 1.2
- 55.2
- Supported · #29/142
- Qwen3.8-27B
- 48.7
- Supported · #45/142
- Basis
- BenchAlign v5.7 lane · 5 vs 8 public rows
- Reading
- Muse Spark 1.2 leads · intervals overlap
Knowledge
Like-for-like- Muse Spark 1.2
- 64.9
- Supported · #24/168
- Qwen3.8-27B
- 49.2
- Supported · #63/168
- Basis
- BenchAlign v5.7 lane · 1 vs 6 public rows
- Reading
- Muse Spark 1.2 leads · intervals overlap
Reasoning
Not comparable- Muse Spark 1.2
- 76.6
- Unranked · 2 rankable rows
- Qwen3.8-27B
- 78.7
- #8/27
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Muse Spark 1.2
- Not ranked
- Qwen3.8-27B
- 80.9
- #11/50
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Muse Spark 1.2
- Not ranked
- Qwen3.8-27B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Muse Spark 1.2
- Not ranked
- Qwen3.8-27B
- 83.2
- #45/124
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Math
Not comparable- Muse Spark 1.2
- Not ranked
- Qwen3.8-27B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Muse Spark 1.2 | Qwen3.8-27B | Basis | Reading |
|---|---|---|---|---|
| Agentic | 59.3Supported · #22/117 | 61.2Supported · #18/117 | Like-for-likeBenchAlign v5.7 lane · 2 vs 8 public rows | Qwen3.8-27B leads · intervals overlap |
| Coding | 55.2Supported · #29/142 | 48.7Supported · #45/142 | Like-for-likeBenchAlign v5.7 lane · 5 vs 8 public rows | Muse Spark 1.2 leads · intervals overlap |
| Knowledge | 64.9Supported · #24/168 | 49.2Supported · #63/168 | Like-for-likeBenchAlign v5.7 lane · 1 vs 6 public rows | Muse Spark 1.2 leads · intervals overlap |
| Reasoning | 76.6Unranked · 2 rankable rows | 78.7#8/27 | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | 80.9#11/50 | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | 83.2#45/124 | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Math | 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 v5.7) 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.
Supported evidence per lane · 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
- Muse Spark 1.2
- $0.00338
- Fits in one request
- Qwen3.8-27B
- Self-hosted; infrastructure cost varies
- Fits in one request
Qwen3.8-27B has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Muse Spark 1.2
- $0.07525
- Fits in one request
- Qwen3.8-27B
- Self-hosted; infrastructure cost varies
- Fits in one request
Qwen3.8-27B has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Muse Spark 1.2
- $0.0975
- Fits in one request
- Qwen3.8-27B
- Self-hosted; infrastructure cost varies
- Fits in one request
- Cached-input rate unavailable
Qwen3.8-27B 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.
SpecificationMuse Spark 1.2Qwen3.8-27B
Context window
Maximum documented context; output-token limits may be lower.
Muse Spark 1.2
Qwen3.8-27B
API model ID
Muse Spark 1.2
muse-spark-1.2
Meta: Muse Spark 1.2 model pageQwen3.8-27B
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Muse Spark 1.2
$0.15 per 1M cached input tokens
Meta: Muse Spark 1.2 model pageQwen3.8-27B
No comparable hosted API rate
Qwen3.8-27B model cardDocumented inputs
Muse Spark 1.2
Not sourced
Qwen3.8-27B
Not sourced
Documented outputs
Muse Spark 1.2
Not sourced
Qwen3.8-27B
Not sourced
Provider availability
Muse Spark 1.2
Not sourced
Qwen3.8-27B
Not sourced
Reasoning profile
Muse Spark 1.2
Reasoning
Qwen3.8-27B
Reasoning
Weight access
Muse Spark 1.2
Proprietary
Qwen3.8-27B
Open Weight
License
Muse Spark 1.2
Proprietary
Qwen3.8-27B
Open Weight
Release date
Muse Spark 1.2
2026-08-05
Qwen3.8-27B
2026-08-05
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
- Muse Spark 1.2 has the higher public score estimate, 64.92 versus 55.26, but the 90% score intervals overlap.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- Muse Spark 1.2 has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Muse Spark 1.2 or Qwen3.8-27B?
Muse Spark 1.2 has the higher public score estimate, 64.92 versus 55.26, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, Muse Spark 1.2 or Qwen3.8-27B?
Muse Spark 1.2 leads the public coding lane, 55.2 to 48.7, with Supported evidence for both models, although the 90% intervals overlap.
Which is better for agentic tasks, Muse Spark 1.2 or Qwen3.8-27B?
Qwen3.8-27B leads the public agentic tasks lane, 61.2 to 59.3, with Supported evidence for both models, although the 90% intervals overlap.
Which costs less, Muse Spark 1.2 or Qwen3.8-27B?
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, Muse Spark 1.2 or Qwen3.8-27B?
Muse Spark 1.2 has the larger documented context window: 1M, compared with 262K.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence34 rows
Agentic
Terminal-Bench 2.1
Muse Spark 1.282.9%SourceQwen3.8-27B73.0%SourceMuse Spark 1.2 leads this result
Terminal-Bench 2.1 (Vals)
Muse Spark 1.269.7%SourceQwen3.8-27B58.4%SourceMuse Spark 1.2 leads this result
CoWorkBench
Muse Spark 1.2—Qwen3.8-27B70.7%SourceNot directly comparable
JobBench
Muse Spark 1.2—Qwen3.8-27B33.4%SourceNot directly comparable
Agents' Last Exam
Muse Spark 1.2—Qwen3.8-27B42.9%SourceNot directly comparable
OSWorld-Verified
Muse Spark 1.2—Qwen3.8-27B84.3%SourceNot directly comparable
WebArena-Verified
Muse Spark 1.2—Qwen3.8-27B64.8%SourceNot directly comparable
AndroidWorld
Muse Spark 1.2—Qwen3.8-27B81.9%SourceNot directly comparable
Coding
Terminal-Bench 2.1
Muse Spark 1.282.9%SourceQwen3.8-27B73.0%SourceMuse Spark 1.2 leads this result
DeepSWE
Muse Spark 1.259.3%SourceQwen3.8-27B42.2%SourceMuse Spark 1.2 leads this result
VulcanBench v3
Muse Spark 1.287.0%SourceQwen3.8-27B82.6%SourceMuse Spark 1.2 leads this result
FrontierSWE v2
Muse Spark 1.212.0%SourceQwen3.8-27B—Not directly comparable
SWE-bench (Vals)
Muse Spark 1.286.6%SourceQwen3.8-27B86.0%SourceMuse Spark 1.2 leads this result
SWE-bench Pro
Muse Spark 1.2—Qwen3.8-27B61.7%SourceNot directly comparable
NL2Repo
Muse Spark 1.2—Qwen3.8-27B42.3%SourceNot directly comparable
LiveCodeBench v6
Muse Spark 1.2—Qwen3.8-27B90.3%SourceNot directly comparable
LiveCodeBench (Vals)
Muse Spark 1.2—Qwen3.8-27B84.0%SourceNot directly comparable
Multimodal
MathVision
Muse Spark 1.2—Qwen3.8-27B90.0%SourceNot directly comparable
MathVision w/ Python
Muse Spark 1.2—Qwen3.8-27B94.6%SourceNot directly comparable
BabyVision
Muse Spark 1.2—Qwen3.8-27B65.7%SourceNot directly comparable
BabyVision w/ Python
Muse Spark 1.2—Qwen3.8-27B85.6%SourceNot directly comparable
Vision2Web
Muse Spark 1.2—Qwen3.8-27B62.9%SourceNot directly comparable
CharXiv w/o tools
Muse Spark 1.2—Qwen3.8-27B83.7%SourceNot directly comparable
CharXiv
Muse Spark 1.2—Qwen3.8-27B90.2%SourceNot directly comparable
OmniDocBench 1.5
Muse Spark 1.2—Qwen3.8-27B91.1%SourceNot directly comparable
RealWorldQA
Muse Spark 1.2—Qwen3.8-27B85.9%SourceNot directly comparable
ERQA
Muse Spark 1.2—Qwen3.8-27B65.5%SourceNot directly comparable
Knowledge
MMLU-Pro (Vals)
Muse Spark 1.288.3%SourceQwen3.8-27B84.3%SourceMuse Spark 1.2 leads this result
GPQA
Muse Spark 1.2—Qwen3.8-27B89.2%SourceNot directly comparable
GPQA-D
Muse Spark 1.2—Qwen3.8-27B89.2%SourceNot directly comparable
HLE
Muse Spark 1.2—Qwen3.8-27B30.8%SourceNot directly comparable
HLE w/o tools
Muse Spark 1.2—Qwen3.8-27B30.8%SourceNot directly comparable
GPQA Diamond (Vals)
Muse Spark 1.2—Qwen3.8-27B88.9%SourceNot directly comparable
Instruction following
IFBench
Muse Spark 1.2—Qwen3.8-27B79.5%SourceNot directly comparable
34 public results · 7 shared
Watch Muse Spark 1.2 vs Qwen3.8-27B
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Last updated September 28, 2026