Long documents
Prompts that approach the documented context limit
Muse Spark 1.3
Muse Spark 1.3 has the larger documented context window.
Confidence: documented
MiniMax M2.7 vs Muse Spark 1.3
Updated October 17, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.
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Decision reading
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality 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 M2.7 only
- 28
- Muse Spark 1.3 only
- 19
- Like-for-like categories
- 0 / 8
Supported: MiniMax M2.7How 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.
Chat turn cost
1K fresh input + 500 output tokens
MiniMax M2.7
MiniMax M2.7 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Show secondary and unsupported calls
Repository review cost
50K fresh input + 3K output tokens
MiniMax M2.7
MiniMax M2.7 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-ratesCoding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Muse Spark 1.3 is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Muse Spark 1.3 is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limitedCache-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.
Confidence: rate-fallback
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.
34.4MiniMax M2.7—Muse Spark 1.3
Not comparable · BenchAlign v5.8
The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.
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.
28 MiniMax M2.7 only
1 Shared
19 Muse Spark 1.3 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.
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
Not comparable- MiniMax M2.7
- 32.3
- Supported · #90/128
- Muse Spark 1.3
- Not ranked
- Basis
- BenchAlign v5.8 lane · 8 vs 9 public rows
- Reading
- Not comparable
Coding
Not comparable- MiniMax M2.7
- 34.4
- Supported · #80/150
- Muse Spark 1.3
- Not ranked
- Basis
- BenchAlign v5.8 lane · 12 vs 6 public rows
- Reading
- Not comparable
Reasoning
Not comparable- MiniMax M2.7
- 79.6
- Unranked · 2 rankable rows
- Muse Spark 1.3
- 82.9
- #6/28
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- MiniMax M2.7
- Not ranked
- Muse Spark 1.3
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- MiniMax M2.7
- 46.1
- Supported · #93/184
- Muse Spark 1.3
- Not ranked
- Basis
- BenchAlign v5.8 lane · 5 vs 0 public rows
- Reading
- Not comparable
Multilingual
Not comparable- MiniMax M2.7
- Not ranked
- Muse Spark 1.3
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- MiniMax M2.7
- 93.0
- #13/148
- Muse Spark 1.3
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- MiniMax M2.7
- 70.5
- Unranked · 4 rankable rows
- Muse Spark 1.3
- 78.5
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 2 vs 2 weighted rows
- Reading
- Not comparable
| Category | MiniMax M2.7 | Muse Spark 1.3 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 32.3Supported · #90/128 | Not ranked | Not comparableBenchAlign v5.8 lane · 8 vs 9 public rows | Not comparable |
| Coding | 34.4Supported · #80/150 | Not ranked | Not comparableBenchAlign v5.8 lane · 12 vs 6 public rows | Not comparable |
| Reasoning | 79.6Unranked · 2 rankable rows | 82.9#6/28 | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Knowledge | 46.1Supported · #93/184 | Not ranked | Not comparableBenchAlign v5.8 lane · 5 vs 0 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | 93.0#13/148 | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 70.5Unranked · 4 rankable rows | 78.5Unranked · 2 rankable rows | Not comparableProvisional lane · 2 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 M2.7
- $0.0009
- Fits in one request
- Muse Spark 1.3
- $0.00338
- Fits in one request
MiniMax M2.7 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- MiniMax M2.7
- $0.0186
- Fits in one request
- Muse Spark 1.3
- $0.07525
- Fits in one request
MiniMax M2.7 has the lower modeled cost
Costs use the listed standard API rates.
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 1.3
- $0.0975
- Fits 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.
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 M2.7Muse Spark 1.3
Context window
Maximum documented context; output-token limits may be lower.
MiniMax M2.7
200K
Muse Spark 1.3
API model ID
MiniMax M2.7
Not sourced
Muse Spark 1.3
muse-spark-1.3
Meta Muse Spark 1.3 model pageCached-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 1.3
$0.15 per 1M cached input tokens
Meta: Muse Spark 1.3 model pageDocumented inputs
MiniMax M2.7
Not sourced
Muse Spark 1.3
text, image, video, document
Meta Muse Spark 1.3 model pageDocumented outputs
MiniMax M2.7
Not sourced
Muse Spark 1.3
Provider availability
MiniMax M2.7
Not sourced
Muse Spark 1.3
Generally Available · Meta Model API, Muse Code
Meta AI Research Muse Spark 1.3 launchReasoning profile
MiniMax M2.7
Non-Reasoning
Muse Spark 1.3
Reasoning
Weight access
MiniMax M2.7
Open Weight
Muse Spark 1.3
Proprietary
License
MiniMax M2.7
Open Weight
Muse Spark 1.3
Proprietary
Release date
MiniMax M2.7
2026-03-18
Muse Spark 1.3
2026-09-02
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
- At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
- Workload cost
- Repository review: $0.0186 vs $0.07525. Cache-heavy agent loop: $0.078 vs $0.0975.
- Context tradeoff
- Muse Spark 1.3 has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, MiniMax M2.7 or Muse Spark 1.3?
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, MiniMax M2.7 or Muse Spark 1.3?
Muse Spark 1.3 is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, MiniMax M2.7 or Muse Spark 1.3?
Muse Spark 1.3 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, MiniMax M2.7 or Muse Spark 1.3?
For the stated presets, chat costs $0.0009 on MiniMax M2.7 and $0.00337 on Muse Spark 1.3; repository review costs $0.0186 and $0.07525; the cache-heavy agent loop costs $0.078 and $0.0975. 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.
Which has the larger context window, MiniMax M2.7 or Muse Spark 1.3?
Muse Spark 1.3 has the larger documented context window: 1M, compared with 200K.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence48 rows
Agentic
Terminal-Bench 2.0
MiniMax M2.757%SourceMuse Spark 1.3—Not directly comparable
τ³-bench results
MiniMax M2.767.6%SourceMuse Spark 1.3—Not directly comparable
Toolathlon
MiniMax M2.746.3%SourceMuse Spark 1.3—Not directly comparable
MLE-Bench Lite
MiniMax M2.766.6%SourceMuse Spark 1.3—Not directly comparable
MM-ClawBench
MiniMax M2.762.7%SourceMuse Spark 1.3—Not directly comparable
Claw-Eval
MiniMax M2.748.7%SourceMuse Spark 1.3—Not directly comparable
Gert Labs
MiniMax M2.740.40%SourceMuse Spark 1.3—Not directly comparable
Terminal-Bench 2.1 (Vals)
MiniMax M2.748.7%SourceMuse Spark 1.372.3%SourceMuse Spark 1.3 leads this result
Terminal-Bench 2.1
MiniMax M2.7—Muse Spark 1.388.8%SourceNot directly comparable
JobBench
MiniMax M2.7—Muse Spark 1.364.9%SourceNot directly comparable
OSWorld 2.0
MiniMax M2.7—Muse Spark 1.366.9%SourceNot directly comparable
DeepSearchQA
MiniMax M2.7—Muse Spark 1.389.4%SourceNot directly comparable
AutomationBench
MiniMax M2.7—Muse Spark 1.349.4%SourceNot directly comparable
ApprenticeBench
MiniMax M2.7—Muse Spark 1.319%SourceNot directly comparable
CWE-bench v1
MiniMax M2.7—Muse Spark 1.355.0%SourceNot directly comparable
MazeBench
MiniMax M2.7—Muse Spark 1.30%SourceNot directly comparable
Coding
SWE-bench Verified*
MiniMax M2.775.4%SourceMuse Spark 1.3—Not directly comparable
SWE-bench Pro
MiniMax M2.756.2%SourceMuse Spark 1.3—Not directly comparable
SWE-Rebench
MiniMax M2.751.9%SourceMuse Spark 1.3—Not directly comparable
SWE Multilingual
MiniMax M2.776.5%SourceMuse Spark 1.3—Not directly comparable
Multi-SWE Bench
MiniMax M2.752.7%SourceMuse Spark 1.3—Not directly comparable
VIBE-Pro
MiniMax M2.755.6%SourceMuse Spark 1.3—Not directly comparable
NL2Repo
MiniMax M2.739.8%SourceMuse Spark 1.3—Not directly comparable
Terminal-Bench 2.0
MiniMax M2.757.0%SourceMuse Spark 1.3—Not directly comparable
Vibe Code Bench
MiniMax M2.727.04%SourceMuse Spark 1.3—Not directly comparable
React Native Evals
MiniMax M2.771.4%SourceMuse Spark 1.3—Not directly comparable
LiveCodeBench (Vals)
MiniMax M2.779.9%SourceMuse Spark 1.3—Not directly comparable
SWE-bench (Vals)
MiniMax M2.773.8%SourceMuse Spark 1.3—Not directly comparable
Terminal-Bench 2.1
MiniMax M2.7—Muse Spark 1.388.8%SourceNot directly comparable
DeepSWE
MiniMax M2.7—Muse Spark 1.375.4%SourceNot directly comparable
SWE-Atlas Codebase QnA
MiniMax M2.7—Muse Spark 1.359.4%SourceNot directly comparable
CursorBench 4.0
MiniMax M2.7—Muse Spark 1.341.6%SourceNot directly comparable
Bug Hunt Bench
MiniMax M2.7—Muse Spark 1.332.2 fixesSourceNot directly comparable
FrontierSWE v2
MiniMax M2.7—Muse Spark 1.325.8%SourceNot directly comparable
Reasoning
Knowledge
HLE
MiniMax M2.728%SourceMuse Spark 1.3—Not directly comparable
GPQA-D
MiniMax M2.787.0%SourceMuse Spark 1.3—Not directly comparable
MMLU-Pro (Arcee)
MiniMax M2.780.8%SourceMuse Spark 1.3—Not directly comparable
GPQA Diamond (Vals)
MiniMax M2.786.6%SourceMuse Spark 1.3—Not directly comparable
MMLU-Pro (Vals)
MiniMax M2.780.4%SourceMuse Spark 1.3—Not directly comparable
Instruction following
Gray Swan IPI (15 attempts)
MiniMax M2.7—Muse Spark 1.315.9%SourceNot directly comparable
Math
AIME25 (Arcee)
MiniMax M2.780.0%SourceMuse Spark 1.3—Not directly comparable
AIME26
MiniMax M2.789.8%SourceMuse Spark 1.3—Not directly comparable
HMMT Nov 2025
MiniMax M2.781.0%SourceMuse Spark 1.3—Not directly comparable
HMMT Feb 2026
MiniMax M2.772.7%SourceMuse Spark 1.3—Not directly comparable
FrontierMath v2 (Tiers 1-3)
MiniMax M2.7—Muse Spark 1.374.035%SourceNot directly comparable
FrontierMath v2 (Tier 4)
MiniMax M2.7—Muse Spark 1.346.341%SourceNot directly comparable
48 public results · 1 shared
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Last updated October 17, 2026