Multimodal
Like-for-like- MiMo-V2.5
- 58.5
- #29/48
- Muse Spark
- 76.8
- #14/48
- Basis
- Provisional lane · 2 vs 2 weighted rows
- Reading
- Muse Spark leads
MiMo-V2.5 vs Muse Spark
Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
Muse Spark has the higher public score estimate, 68.39 versus 60.72, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
5 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
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.
Long documents
Prompts that approach the documented context limit
MiMo-V2.5
MiMo-V2.5 has the larger documented context window.
Confidence: documented
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
MiMo-V2.5 is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
MiMo-V2.5 is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
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.
10 MiMo-V2.5 only
5 Shared
19 Muse Spark only
- Shared results
- 5
- MiMo-V2.5 only
- 10
- Muse Spark only
- 19
- Like-for-like categories
- 1 / 8
3 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Category results, on a stated basis
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.
Agentic
Directional only- MiMo-V2.5
- 51.1
- Estimated · #58/151
- Muse Spark
- 58.8
- Supported · #31/151
- Basis
- BenchAlign lane · 6 vs 5 public rows
- Reading
- Directional only
Coding
Directional only- MiMo-V2.5
- 54.8
- Estimated · #45/183
- Muse Spark
- 59.2
- Supported · #28/183
- Basis
- BenchAlign lane · 4 vs 4 public rows
- Reading
- Directional only
Knowledge
Directional only- MiMo-V2.5
- 53.8
- Estimated · #65/181
- Muse Spark
- 65.6
- Supported · #23/181
- Basis
- BenchAlign lane · 2 vs 5 public rows
- Reading
- Directional only
Reasoning
Not comparable- MiMo-V2.5
- Not ranked
- Muse Spark
- 45.9
- Unranked · 3 rankable rows
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Math
Not comparable- MiMo-V2.5
- Not ranked
- Muse Spark
- 55.3
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- MiMo-V2.5
- Not ranked
- Muse Spark
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- MiMo-V2.5
- Not ranked
- Muse Spark
- 92.9
- #8/120
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | MiMo-V2.5 | Muse Spark | Basis | Reading |
|---|---|---|---|---|
| Multimodal | 58.5#29/48 | 76.8#14/48 | Like-for-likeProvisional lane · 2 vs 2 weighted rows | Muse Spark leads |
| Agentic | 51.1Estimated · #58/151 | 58.8Supported · #31/151 | Directional onlyBenchAlign lane · 6 vs 5 public rows | Directional only |
| Coding | 54.8Estimated · #45/183 | 59.2Supported · #28/183 | Directional onlyBenchAlign lane · 4 vs 4 public rows | Directional only |
| Knowledge | 53.8Estimated · #65/181 | 65.6Supported · #23/181 | Directional onlyBenchAlign lane · 2 vs 5 public rows | Directional only |
| Reasoning | Not ranked | 45.9Unranked · 3 rankable rows | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Math | Not ranked | 55.3Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | 92.9#8/120 | 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) 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.
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.
Terminal-Bench 2.0
Agentic
CharXiv
Multimodal
SWE-bench Pro
Coding
MMMU-Pro
Multimodal
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
- MiMo-V2.5
- API rate not published
- Fits in one request
- Muse Spark
- API rate not published
- Fits in one request
MiMo-V2.5 has no comparable published API token rate. Muse Spark has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- MiMo-V2.5
- API rate not published
- Fits in one request
- Muse Spark
- API rate not published
- Fits in one request
MiMo-V2.5 has no comparable published API token rate. Muse Spark has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- MiMo-V2.5
- API rate not published
- Fits in one request
- Cached-input rate unavailable
- Muse Spark
- API rate not published
- Fits in one request
- Cached-input rate unavailable
MiMo-V2.5 has no comparable published API token rate. Muse Spark 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.
SpecificationMiMo-V2.5Muse Spark
Context window
Maximum documented context; output-token limits may be lower.
MiMo-V2.5
1M
Muse Spark
262K
API model ID
MiMo-V2.5
Not sourced
Muse Spark
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
MiMo-V2.5
No comparable hosted API rate
Muse Spark
No comparable hosted API rate
Documented inputs
MiMo-V2.5
Not sourced
Muse Spark
Not sourced
Documented outputs
MiMo-V2.5
Not sourced
Muse Spark
Not sourced
Provider availability
MiMo-V2.5
Not sourced
Muse Spark
Not sourced
Reasoning profile
MiMo-V2.5
Reasoning
Muse Spark
Reasoning
Weight access
MiMo-V2.5
Proprietary
Muse Spark
Proprietary
License
MiMo-V2.5
Proprietary
Muse Spark
Proprietary
Release date
MiMo-V2.5
2026-04-22
Muse Spark
2026-04-08
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 has the higher public score estimate, 68.39 versus 60.72, but the 90% score intervals overlap.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- MiMo-V2.5 has the larger documented window (1M).
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 evidence34 rows
Agentic
Claw-Eval
Shared sourceMiMo-V2.562.3%Muse Spark63.8%Muse Spark leads this result
MM-ClawBench
MiMo-V2.523.8%SourceMuse Spark—Not directly comparable
Terminal-Bench 2.0
MiMo-V2.565.8%SourceMuse Spark59%SourceMiMo-V2.5 leads this result
Gert Labs
MiMo-V2.546.89%SourceMuse Spark—Not directly comparable
ResearchClawBench
MiMo-V2.516.9%SourceMuse Spark—Not directly comparable
Terminal-Bench 2.1 (Vals)
MiMo-V2.560.7%SourceMuse Spark—Not directly comparable
τ²-bench results
MiMo-V2.5—Muse Spark91.5%SourceNot directly comparable
DeepSearchQA
MiMo-V2.5—Muse Spark74.8%SourceNot directly comparable
CyberGym
MiMo-V2.5—Muse Spark43.5%SourceNot directly comparable
Coding
SWE-bench Pro
MiMo-V2.556.1%SourceMuse Spark52.4%SourceMiMo-V2.5 leads this result
Terminal-Bench 2.0
MiMo-V2.565.8%SourceMuse Spark—Not directly comparable
LiveCodeBench (Vals)
MiMo-V2.581.5%SourceMuse Spark—Not directly comparable
SWE-bench (Vals)
MiMo-V2.571.0%SourceMuse Spark—Not directly comparable
SWE-bench Verified
MiMo-V2.5—Muse Spark77.4%SourceNot directly comparable
LiveCodeBench Pro
MiMo-V2.5—Muse Spark80.0%SourceNot directly comparable
Vibe Code Bench
MiMo-V2.5—Muse Spark19.67%SourceNot directly comparable
Reasoning
ARC-AGI-2
MiMo-V2.5—Muse Spark42.5%SourceNot directly comparable
Knowledge
GPQA Diamond (Vals)
MiMo-V2.581.6%SourceMuse Spark—Not directly comparable
MMLU-Pro (Vals)
MiMo-V2.582.9%SourceMuse Spark—Not directly comparable
GPQA-D
MiMo-V2.5—Muse Spark89.5%SourceNot directly comparable
HLE
MiMo-V2.5—Muse Spark50.4%SourceNot directly comparable
HLE w/o tools
MiMo-V2.5—Muse Spark42.8%SourceNot directly comparable
HealthBench Hard
MiMo-V2.5—Muse Spark42.8%SourceNot directly comparable
MedXpertQA (Text)
MiMo-V2.5—Muse Spark52.6%SourceNot directly comparable
Math
Multimodal
Video-MME (with subtitle)
MiMo-V2.587.7%SourceMuse Spark—Not directly comparable
CharXiv
MiMo-V2.581%SourceMuse Spark86.4%SourceMuse Spark leads this result
MMMU-Pro
MiMo-V2.577.9%SourceMuse Spark80.4%SourceMuse Spark leads this result
ERQA
MiMo-V2.5—Muse Spark64.7%SourceNot directly comparable
SimpleVQA
MiMo-V2.5—Muse Spark71.3%SourceNot directly comparable
ScreenSpot Pro
MiMo-V2.5—Muse Spark84.1%SourceNot directly comparable
ZeroBench
MiMo-V2.5—Muse Spark33.0%SourceNot directly comparable
MedXpertQA (MM)
MiMo-V2.5—Muse Spark78.4%SourceNot directly comparable
Frequently asked questions
Which is better, MiMo-V2.5 or Muse Spark?
Muse Spark has the higher public score estimate, 68.39 versus 60.72, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, MiMo-V2.5 or Muse Spark?
Muse Spark scores higher for coding on the public lane, 59.2 to 54.8. MiMo-V2.5 is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which is better for agentic tasks, MiMo-V2.5 or Muse Spark?
Muse Spark scores higher for agentic tasks on the public lane, 58.8 to 51.1. MiMo-V2.5 is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which costs less, MiMo-V2.5 or Muse Spark?
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, MiMo-V2.5 or Muse Spark?
MiMo-V2.5 has the larger documented context window: 1M, compared with 262K.
Related comparisons
Last updated September 4, 2026
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