Agentic
Like-for-like- MiniMax M2.7
- 57.0
- Muse Spark
- 59.0
- Weighted basis
- 1 vs 1 rows
- Reading
- Muse Spark leads
MiniMax M2.7 vs Muse Spark
Updated August 21, 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, 70.6 versus 62.85, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
5 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.
Agentic work
Tool use, computer use, and multi-step task completion
Muse Spark
Muse Spark leads on the same 1 weighted benchmark row.
Confidence: limited
Long documents
Prompts that approach the documented context limit
Muse Spark
Muse Spark 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
The category averages use different weighted benchmark sets, so they are 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
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. MiniMax M2.7 does not fit this workload in one request. MiniMax M2.7 has no published cached-input rate, so cached tokens use its listed input rate. Muse Spark has no comparable published API token rate.
Confidence: rate-fallback
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.
13 MiniMax M2.7 only
5 Shared
19 Muse Spark only
- Shared results
- 5
- MiniMax M2.7 only
- 13
- Muse Spark only
- 19
- Like-for-like categories
- 1 / 8
1 category uses different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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
Directional only- MiniMax M2.7
- 53.3
- Muse Spark
- 67.8
- Weighted basis
- 2 vs 2 rows
- Reading
- Directional only
Reasoning
Not comparable- MiniMax M2.7
- Not measured
- Muse Spark
- 42.5
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
Knowledge
Not comparable- MiniMax M2.7
- Not measured
- Muse Spark
- 50.4
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
Math
Not comparable- MiniMax M2.7
- Not measured
- Muse Spark
- 32.9
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Multilingual
Not comparable- MiniMax M2.7
- Not measured
- Muse Spark
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- MiniMax M2.7
- Not measured
- Muse Spark
- 82.5
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Instruction following
Not comparable- MiniMax M2.7
- Not measured
- Muse Spark
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | MiniMax M2.7 | Muse Spark | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | 57.0 | 59.0 | Like-for-like1 vs 1 rows | Muse Spark leads |
| Coding | 53.3 | 67.8 | Directional only2 vs 2 rows | Directional only |
| Reasoning | Not measured | 42.5 | Not comparable0 vs 1 rows | Not comparable |
| Knowledge | Not measured | 50.4 | Not comparable0 vs 1 rows | Not comparable |
| Math | Not measured | 32.9 | Not comparable0 vs 2 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | Not measured | 82.5 | Not comparable0 vs 2 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.
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.
SWE-bench Pro
Coding
Terminal-Bench 2.0
Agentic
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
- MiniMax M2.7
- $0.0009
- Fits in one request
- Muse Spark
- API rate not published
- Fits in one request
Muse Spark has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- MiniMax M2.7
- $0.0186
- Fits in one request
- Muse Spark
- API rate not published
- Fits in one request
Muse Spark has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- MiniMax M2.7
- $0.078
- Does not fit in one request
- Cached input priced at the published list-input rate
- Muse Spark
- API rate not published
- Fits in one request
- Cached-input rate unavailable
MiniMax M2.7 does not fit this workload in one request. MiniMax M2.7 has no published cached-input rate, so cached tokens use its listed input 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.
SpecificationMiniMax M2.7Muse Spark
Context window
Maximum documented context; output-token limits may be lower.
MiniMax M2.7
200K
Muse Spark
262K
API model ID
MiniMax M2.7
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.
MiniMax M2.7
Not published
Muse Spark
No comparable hosted API rate
Documented inputs
MiniMax M2.7
Not sourced
Muse Spark
Not sourced
Documented outputs
MiniMax M2.7
Not sourced
Muse Spark
Not sourced
Provider availability
MiniMax M2.7
Not sourced
Muse Spark
Not sourced
Reasoning profile
MiniMax M2.7
Non-Reasoning
Muse Spark
Reasoning
Weight access
MiniMax M2.7
Open Weight
Muse Spark
Proprietary
License
MiniMax M2.7
Open Weight
Muse Spark
Proprietary
Release date
MiniMax M2.7
2026-03-18
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, 70.6 versus 62.85, but the 90% score intervals overlap.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- Muse Spark has the larger documented window (262K).
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 evidence37 rows
Agentic
Terminal-Bench 2.0
MiniMax M2.757%SourceMuse Spark59%SourceMuse Spark leads this result
Toolathlon
MiniMax M2.746.3%SourceMuse Spark—Not directly comparable
MLE-Bench Lite
MiniMax M2.766.6%SourceMuse Spark—Not directly comparable
MM-ClawBench
MiniMax M2.762.7%SourceMuse Spark—Not directly comparable
Claw-Eval
Shared sourceMiniMax M2.748.7%Muse Spark63.8%Muse Spark leads this result
Gert Labs
MiniMax M2.740.40%SourceMuse Spark—Not directly comparable
τ²-bench results
MiniMax M2.7—Muse Spark91.5%SourceNot directly comparable
DeepSearchQA
MiniMax M2.7—Muse Spark74.8%SourceNot directly comparable
CyberGym
MiniMax M2.7—Muse Spark43.5%SourceNot directly comparable
Coding
SWE-bench Verified*
MiniMax M2.775.4%SourceMuse Spark—Not directly comparable
SWE-bench Pro
MiniMax M2.756.2%SourceMuse Spark52.4%SourceMiniMax M2.7 leads this result
SWE-Rebench
MiniMax M2.751.9%SourceMuse Spark—Not directly comparable
SWE Multilingual
MiniMax M2.776.5%SourceMuse Spark—Not directly comparable
Multi-SWE Bench
MiniMax M2.752.7%SourceMuse Spark—Not directly comparable
VIBE-Pro
MiniMax M2.755.6%SourceMuse Spark—Not directly comparable
NL2Repo
MiniMax M2.739.8%SourceMuse Spark—Not directly comparable
Vibe Code Bench
Shared sourceMiniMax M2.727.04%Muse Spark19.67%MiniMax M2.7 leads this result
React Native Evals
MiniMax M2.771.4%SourceMuse Spark—Not directly comparable
SWE-bench Verified
MiniMax M2.7—Muse Spark77.4%SourceNot directly comparable
LiveCodeBench Pro
MiniMax M2.7—Muse Spark80.0%SourceNot directly comparable
Reasoning
ARC-AGI-2
MiniMax M2.7—Muse Spark42.5%SourceNot directly comparable
Knowledge
GPQA-D
MiniMax M2.787.0%SourceMuse Spark89.5%SourceMuse Spark leads this result
MMLU-Pro (Arcee)
MiniMax M2.780.8%SourceMuse Spark—Not directly comparable
HLE
MiniMax M2.7—Muse Spark50.4%SourceNot directly comparable
HLE w/o tools
MiniMax M2.7—Muse Spark42.8%SourceNot directly comparable
HealthBench Hard
MiniMax M2.7—Muse Spark42.8%SourceNot directly comparable
MedXpertQA (Text)
MiniMax M2.7—Muse Spark52.6%SourceNot directly comparable
Math
Multimodal
CharXiv
MiniMax M2.7—Muse Spark86.4%SourceNot directly comparable
MMMU-Pro
MiniMax M2.7—Muse Spark80.4%SourceNot directly comparable
ERQA
MiniMax M2.7—Muse Spark64.7%SourceNot directly comparable
SimpleVQA
MiniMax M2.7—Muse Spark71.3%SourceNot directly comparable
ScreenSpot Pro
MiniMax M2.7—Muse Spark84.1%SourceNot directly comparable
ZeroBench
MiniMax M2.7—Muse Spark33.0%SourceNot directly comparable
MedXpertQA (MM)
MiniMax M2.7—Muse Spark78.4%SourceNot directly comparable
Frequently asked questions
Which is better, MiniMax M2.7 or Muse Spark?
Muse Spark has the higher public score estimate, 70.6 versus 62.85, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, MiniMax M2.7 or Muse Spark?
The current coding averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.
Which is better for agentic tasks, MiniMax M2.7 or Muse Spark?
Muse Spark leads the like-for-like agentic tasks comparison across 1 shared weighted benchmark row.
Which costs less, MiniMax M2.7 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, MiniMax M2.7 or Muse Spark?
Muse Spark has the larger documented context window: 262K, compared with 200K.
Related comparisons
Last updated August 21, 2026
Watch MiniMax M2.7 vs Muse Spark
One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.
Read a sample issueJoin 2,000+ readers.