Long documents
Prompts that approach the documented context limit
Muse Spark
Muse Spark has the larger documented context window.
Confidence: documented
MiniMax M2.5 vs Muse Spark
Updated October 5, 2026. Rank says Muse Spark 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
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Decision reading
Muse Spark has the higher public score estimate, 59.99 versus 50.22, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 1
- MiniMax M2.5 only
- 0
- Muse Spark only
- 26
- Like-for-like categories
- 0 / 8
Supported: MiniMax M2.5 and Muse SparkHow 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.
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
MiniMax M2.5 is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
MiniMax M2.5 is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limitedChat 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
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. MiniMax M2.5 does not fit this workload in one request. MiniMax M2.5 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-fallbackRepository 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.
35.1MiniMax M2.551.1Muse Spark
Directional only · BenchAlign v5.8
Muse Spark has the higher coding point estimate. Conditional score ranges do not establish rank confidence.
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 Shared
26 Muse Spark only
2 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign v5.8 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- MiniMax M2.5
- 28.3
- Estimated · #84/119
- Muse Spark
- 49.8
- Supported · #43/119
- Basis
- BenchAlign v5.8 lane · 0 vs 5 public rows
- Reading
- Directional only
Coding
Directional only- MiniMax M2.5
- 35.1
- Estimated · #76/144
- Muse Spark
- 51.1
- Supported · #39/144
- Basis
- BenchAlign v5.8 lane · 1 vs 5 public rows
- Reading
- Directional only
Reasoning
Not comparable- MiniMax M2.5
- Not ranked
- Muse Spark
- 53.4
- Unranked · 3 rankable rows
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- MiniMax M2.5
- Not ranked
- Muse Spark
- 78.5
- #14/49
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- MiniMax M2.5
- Not ranked
- Muse Spark
- 60.3
- Supported · #40/171
- Basis
- BenchAlign v5.8 lane · 0 vs 7 public rows
- Reading
- Not comparable
Multilingual
Not comparable- MiniMax M2.5
- Not ranked
- Muse Spark
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- MiniMax M2.5
- Not ranked
- Muse Spark
- 91.9
- #8/125
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- MiniMax M2.5
- Not ranked
- Muse Spark
- 55.1
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
| Category | MiniMax M2.5 | Muse Spark | Basis | Reading |
|---|---|---|---|---|
| Agentic | 28.3Estimated · #84/119 | 49.8Supported · #43/119 | Directional onlyBenchAlign v5.8 lane · 0 vs 5 public rows | Directional only |
| Coding | 35.1Estimated · #76/144 | 51.1Supported · #39/144 | Directional onlyBenchAlign v5.8 lane · 1 vs 5 public rows | Directional only |
| Reasoning | Not ranked | 53.4Unranked · 3 rankable rows | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Multimodal | Not ranked | 78.5#14/49 | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
| Knowledge | Not ranked | 60.3Supported · #40/171 | Not comparableBenchAlign v5.8 lane · 0 vs 7 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | 91.9#8/125 | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | 55.1Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) 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
- MiniMax M2.5
- $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 M2.5
- $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 M2.5
- $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 M2.5 does not fit this workload in one request. MiniMax M2.5 has no published cached-input rate, so cached tokens use its listed input rate. Muse Spark 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.
SpecificationMiniMax M2.5Muse Spark
Context window
Maximum documented context; output-token limits may be lower.
MiniMax M2.5
128K
Muse Spark
262K
API model ID
MiniMax M2.5
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.
MiniMax M2.5
Not published
Muse Spark
No comparable hosted API rate
Documented inputs
MiniMax M2.5
Not sourced
Muse Spark
Not sourced
Documented outputs
MiniMax M2.5
Not sourced
Muse Spark
Not sourced
Provider availability
MiniMax M2.5
Not sourced
Muse Spark
Not sourced
Reasoning profile
MiniMax M2.5
Non-Reasoning
Muse Spark
Reasoning
Weight access
MiniMax M2.5
Proprietary
Muse Spark
Proprietary
License
MiniMax M2.5
Proprietary
Muse Spark
Proprietary
Release date
MiniMax M2.5
2025-10-01
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, 59.99 versus 50.22, 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.
Questions
Which is better, MiniMax M2.5 or Muse Spark?
Muse Spark has the higher public score estimate, 59.99 versus 50.22, 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 M2.5 or Muse Spark?
Muse Spark scores higher for coding on the public lane, 51.1 to 35.1. MiniMax M2.5 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, MiniMax M2.5 or Muse Spark?
Muse Spark scores higher for agentic tasks on the public lane, 49.8 to 28.3. MiniMax M2.5 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, MiniMax M2.5 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, MiniMax M2.5 or Muse Spark?
Muse Spark has the larger documented context window: 262K, compared with 128K.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence27 rows
Agentic
Terminal-Bench 2.0
MiniMax M2.5—Muse Spark59%SourceNot directly comparable
τ²-bench results
MiniMax M2.5—Muse Spark91.5%SourceNot directly comparable
DeepSearchQA
MiniMax M2.5—Muse Spark74.8%SourceNot directly comparable
CyberGym
MiniMax M2.5—Muse Spark43.5%SourceNot directly comparable
Claw-Eval
MiniMax M2.5—Muse Spark63.8%SourceNot directly comparable
Coding
Vibe Code Bench
Shared sourceMiniMax M2.514.85%Muse Spark19.67%Muse Spark leads this result
SWE-bench Verified
MiniMax M2.5—Muse Spark77.4%SourceNot directly comparable
SWE-bench Pro
MiniMax M2.5—Muse Spark52.4%SourceNot directly comparable
LiveCodeBench Pro
MiniMax M2.5—Muse Spark80.0%SourceNot directly comparable
SWE-bench (Vals)
MiniMax M2.5—Muse Spark74.4%SourceNot directly comparable
Reasoning
ARC-AGI-2
MiniMax M2.5—Muse Spark42.5%SourceNot directly comparable
Multimodal
CharXiv
MiniMax M2.5—Muse Spark86.4%SourceNot directly comparable
MMMU-Pro
MiniMax M2.5—Muse Spark80.4%SourceNot directly comparable
ERQA
MiniMax M2.5—Muse Spark64.7%SourceNot directly comparable
SimpleVQA
MiniMax M2.5—Muse Spark71.3%SourceNot directly comparable
ScreenSpot Pro
MiniMax M2.5—Muse Spark84.1%SourceNot directly comparable
ZeroBench
MiniMax M2.5—Muse Spark33.0%SourceNot directly comparable
MedXpertQA (MM)
MiniMax M2.5—Muse Spark78.4%SourceNot directly comparable
Knowledge
GPQA-D
MiniMax M2.5—Muse Spark89.5%SourceNot directly comparable
HLE
MiniMax M2.5—Muse Spark50.4%SourceNot directly comparable
HLE w/o tools
MiniMax M2.5—Muse Spark42.8%SourceNot directly comparable
HealthBench Hard
MiniMax M2.5—Muse Spark42.8%SourceNot directly comparable
MedXpertQA (Text)
MiniMax M2.5—Muse Spark52.6%SourceNot directly comparable
GPQA Diamond (Vals)
MiniMax M2.5—Muse Spark89.6%SourceNot directly comparable
MMLU-Pro (Vals)
MiniMax M2.5—Muse Spark87.3%SourceNot directly comparable
Math
27 public results · 1 shared
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Last updated October 5, 2026