Agentic
Like-for-like- Inkling-Small
- 36.9
- Supported · #128/152
- Muse Spark 1.1
- 59.4
- Supported · #23/152
- Basis
- BenchAlign lane · 5 vs 14 public rows
- Reading
- Muse Spark 1.1 leads
Keep up with the models you depend on. Follow price changes, retirements, and API updates.Follow the models you depend on.
Follow model changesUpdated September 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
Muse Spark 1.1 has the higher public score estimate, 70.41 versus 59.25, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
12 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Share or export
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.
Code generation, repair, and software-engineering tasks
Muse Spark 1.1
Muse Spark 1.1 leads on the public coding lane, 59.7 to 43.8, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Tool use, computer use, and multi-step task completion
Muse Spark 1.1
Muse Spark 1.1 leads on the public agentic lane, 59.4 to 36.9, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
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
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
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
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
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.
| Category | Inkling-Small | Muse Spark 1.1 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 36.9Supported · #128/152 | 59.4Supported · #23/152 | Like-for-likeBenchAlign lane · 5 vs 14 public rows | Muse Spark 1.1 leads |
| Coding | 43.8Supported · #97/151 | 59.7Supported · #23/151 | Like-for-likeBenchAlign lane · 6 vs 4 public rows | Muse Spark 1.1 leads · intervals overlap |
| Knowledge | 56.5Supported · #46/183 | 70.7Supported · #11/183 | Like-for-likeBenchAlign lane · 6 vs 5 public rows | Muse Spark 1.1 leads · intervals overlap |
| Reasoning | 42.7Unranked · 3 rankable rows | 74.4Unranked · 3 rankable rows | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Math | 76.9Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 48.8#39/48 | 77.5Unranked · 2 rankable rows | Not comparableProvisional lane · 2 vs 1 weighted rows | Not comparable |
| Instruction following | 89.6#25/123 | Not ranked | Not comparableProvisional lane · 1 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.
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.
HLE w/o tools
Knowledge
Terminal-Bench 2.0
Agentic
HLE
Knowledge
CharXiv
Multimodal
SWE-bench Pro
Coding
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.
1K fresh input + 500 output tokens
Muse Spark 1.1 has no comparable published API token rate.
50K fresh input + 3K output tokens
Muse Spark 1.1 has no comparable published API token rate.
200K cached + 20K fresh input + 10K output tokens
Muse Spark 1.1 has no comparable published API token rate.
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
Maximum documented context; output-token limits may be lower.
Inkling-Small
1M
Muse Spark 1.1
1M
Inkling-Small
Not sourced
Muse Spark 1.1
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Inkling-Small
$0.116 per 1M cached input tokens
Muse Spark 1.1
No comparable hosted API rate
Inkling-Small
Not sourced
Muse Spark 1.1
Not sourced
Inkling-Small
Not sourced
Muse Spark 1.1
Not sourced
Inkling-Small
Not sourced
Muse Spark 1.1
Not sourced
Inkling-Small
Hybrid
Muse Spark 1.1
Reasoning
Inkling-Small
Open Weight
Muse Spark 1.1
Proprietary
Inkling-Small
Open Weight
Muse Spark 1.1
Proprietary
Inkling-Small
2026-07-30
Muse Spark 1.1
2026-07-09
Run the same representative tasks against both endpoints before changing production traffic.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Terminal-Bench 2.0
Muse Spark 1.1 leads this result
BrowseComp
Not directly comparable
MCP Atlas
Muse Spark 1.1 leads this result
Toolathlon-Verified
Not directly comparable
Terminal-Bench 2.1 (Vals)
Muse Spark 1.1 leads this result
Toolathlon
Not directly comparable
OSWorld-Verified
Not directly comparable
WebArena-Verified
Not directly comparable
DeepSearchQA
Not directly comparable
CyberGym
Not directly comparable
Finance Agent v2
Not directly comparable
deepSwe
Not directly comparable
OSWorld 2.0
Not directly comparable
JobBench
Not directly comparable
Cybench
Not directly comparable
ExploitGym
Not directly comparable
SWE-bench Verified
Not directly comparable
SWE-bench Pro
Muse Spark 1.1 leads this result
Terminal-Bench 2.0
Muse Spark 1.1 leads this result
SciCode
Not directly comparable
LiveCodeBench (Vals)
Tie
SWE-bench (Vals)
Inkling-Small leads this result
GPQA
Not directly comparable
GPQA-D
Not directly comparable
HLE
Muse Spark 1.1 leads this result
HLE w/o tools
Muse Spark 1.1 leads this result
GPQA Diamond (Vals)
Muse Spark 1.1 leads this result
MMLU-Pro (Vals)
Muse Spark 1.1 leads this result
HealthBench Professional
Not directly comparable
MMMU-Pro
Not directly comparable
CharXiv
Muse Spark 1.1 leads this result
CharXiv w/o tools
Not directly comparable
BabyVision
Not directly comparable
IFBench
Not directly comparable
Muse Spark 1.1 has the higher public score estimate, 70.41 versus 59.25, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Muse Spark 1.1 leads the public coding lane, 59.7 to 43.8, with Supported evidence for both models, although the 90% intervals overlap.
Muse Spark 1.1 leads the public agentic tasks lane, 59.4 to 36.9, with Supported evidence for both models and non-overlapping 90% intervals.
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Both models list the same context window, 1M.
Last updated September 10, 2026
One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.
Read a sample issueJoin 2,000+ readers.