Chat turn cost
1K fresh input + 500 output tokens
MiniMax M3.1 Flash
MiniMax M3.1 Flash has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
MiniMax M3.1 Flash vs Muse Spark 1.3
Updated October 19, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.
Share or export
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. 3 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 3
- MiniMax M3.1 Flash only
- 9
- Muse Spark 1.3 only
- 17
- Like-for-like categories
- 0 / 8
Estimated: MiniMax M3.1 Flash. Conditional ranges do not establish rank confidence.How 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.
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
MiniMax M3.1 Flash
MiniMax M3.1 Flash 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 M3.1 Flash
MiniMax M3.1 Flash 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: limitedLong documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
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.
61.3MiniMax M3.1 Flash—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.
9 MiniMax M3.1 Flash only
3 Shared
17 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.
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.
AutomationBenchAgentic
Normalized gap 12.8MiniMax M3.1 Flash:62.2%Muse Spark 1.3:49.4%OSWorld 2.0Agentic
Normalized gap 5.2MiniMax M3.1 Flash:61.7%Muse Spark 1.3:66.9%
Bars run 0–100 on each benchmark’s normalized display scale
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 M3.1 Flash
- 63.9
- Estimated · #19/130
- Muse Spark 1.3
- Not ranked
- Basis
- BenchAlign v5.8 lane · 6 vs 9 public rows
- Reading
- Not comparable
Coding
Not comparable- MiniMax M3.1 Flash
- 61.3
- Supported · #17/151
- Muse Spark 1.3
- Not ranked
- Basis
- BenchAlign v5.8 lane · 3 vs 6 public rows
- Reading
- Not comparable
Reasoning
Not comparable- MiniMax M3.1 Flash
- 80.6
- Unranked · 2 rankable rows
- Muse Spark 1.3
- 82.9
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- MiniMax M3.1 Flash
- 73.5
- #30/55
- Muse Spark 1.3
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- MiniMax M3.1 Flash
- 73.2
- Supported · #14/186
- Muse Spark 1.3
- Not ranked
- Basis
- BenchAlign v5.8 lane · 1 vs 0 public rows
- Reading
- Not comparable
Multilingual
Not comparable- MiniMax M3.1 Flash
- Not ranked
- Muse Spark 1.3
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- MiniMax M3.1 Flash
- Not ranked
- Muse Spark 1.3
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- MiniMax M3.1 Flash
- Not ranked
- Muse Spark 1.3
- 78.5
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
| Category | MiniMax M3.1 Flash | Muse Spark 1.3 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 63.9Estimated · #19/130 | Not ranked | Not comparableBenchAlign v5.8 lane · 6 vs 9 public rows | Not comparable |
| Coding | 61.3Supported · #17/151 | Not ranked | Not comparableBenchAlign v5.8 lane · 3 vs 6 public rows | Not comparable |
| Reasoning | 80.6Unranked · 2 rankable rows | 82.9Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 73.5#30/55 | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Knowledge | 73.2Supported · #14/186 | Not ranked | Not comparableBenchAlign v5.8 lane · 1 vs 0 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | 78.5Unranked · 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 M3.1 Flash
- $0.00045
- Fits in one request
- Muse Spark 1.3
- $0.00338
- Fits in one request
MiniMax M3.1 Flash has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- MiniMax M3.1 Flash
- $0.0093
- Fits in one request
- Muse Spark 1.3
- $0.07525
- Fits in one request
MiniMax M3.1 Flash 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 M3.1 Flash
- $0.012
- Fits in one request
- Muse Spark 1.3
- $0.0975
- Fits in one request
MiniMax M3.1 Flash has the lower modeled cost
Costs use the listed standard API rates.
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 M3.1 FlashMuse Spark 1.3
Context window
Maximum documented context; output-token limits may be lower.
MiniMax M3.1 Flash
Muse Spark 1.3
API model ID
MiniMax M3.1 Flash
MiniMax-M3.1-Flash
MiniMax: M3.1 Flash launch postMuse 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 M3.1 Flash
$0.015 per 1M cached input tokens
MiniMax: M3.1 Flash launch postMuse Spark 1.3
$0.15 per 1M cached input tokens
Meta: Muse Spark 1.3 model pageDocumented inputs
MiniMax M3.1 Flash
Not sourced
Muse Spark 1.3
text, image, video, document
Meta Muse Spark 1.3 model pageDocumented outputs
MiniMax M3.1 Flash
Not sourced
Muse Spark 1.3
Provider availability
MiniMax M3.1 Flash
Not sourced
Muse Spark 1.3
Generally Available · Meta Model API, Muse Code
Meta AI Research Muse Spark 1.3 launchReasoning profile
MiniMax M3.1 Flash
Reasoning
Muse Spark 1.3
Reasoning
Weight access
MiniMax M3.1 Flash
Proprietary
Muse Spark 1.3
Proprietary
License
MiniMax M3.1 Flash
Proprietary
Muse Spark 1.3
Proprietary
Release date
MiniMax M3.1 Flash
2026-10-19
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: $0.0093 vs $0.07525. Cache-heavy agent loop: $0.012 vs $0.0975.
- Context tradeoff
- Both models list 1M.
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, MiniMax M3.1 Flash 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 M3.1 Flash 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 M3.1 Flash 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 M3.1 Flash or Muse Spark 1.3?
For the stated presets, chat costs $0.00045 on MiniMax M3.1 Flash and $0.00337 on Muse Spark 1.3; repository review costs $0.0093 and $0.07525; the cache-heavy agent loop costs $0.012 and $0.0975. Costs use the listed standard API rates.
Which has the larger context window, MiniMax M3.1 Flash or Muse Spark 1.3?
Both models list the same context window, 1M.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence29 rows
Agentic
AutomationBench
MiniMax M3.1 Flash62.2%SourceMuse Spark 1.349.4%SourceMiniMax M3.1 Flash leads this result
BankerToolBench
MiniMax M3.1 Flash76.2%SourceMuse Spark 1.3—Not directly comparable
Terminal-Bench 4.0
MiniMax M3.1 Flash38.10%SourceMuse Spark 1.3—Not directly comparable
Agents' Last Exam
MiniMax M3.1 Flash29.6%SourceMuse Spark 1.3—Not directly comparable
OSWorld 2.0
MiniMax M3.1 Flash61.7%SourceMuse Spark 1.366.9%SourceMuse Spark 1.3 leads this result
CyberGym
MiniMax M3.1 Flash82.2%SourceMuse Spark 1.3—Not directly comparable
Terminal-Bench 2.1
MiniMax M3.1 Flash—Muse Spark 1.388.8%SourceNot directly comparable
JobBench
MiniMax M3.1 Flash—Muse Spark 1.364.9%SourceNot directly comparable
DeepSearchQA
MiniMax M3.1 Flash—Muse Spark 1.389.4%SourceNot directly comparable
ApprenticeBench
MiniMax M3.1 Flash—Muse Spark 1.319%SourceNot directly comparable
Terminal-Bench 2.1 (Vals)
MiniMax M3.1 Flash—Muse Spark 1.372.3%SourceNot directly comparable
CWE-bench v1
MiniMax M3.1 Flash—Muse Spark 1.355.0%SourceNot directly comparable
MazeBench
MiniMax M3.1 Flash—Muse Spark 1.30%SourceNot directly comparable
Coding
PostTrainBench v1.1
MiniMax M3.1 Flash39.3%SourceMuse Spark 1.3—Not directly comparable
DeepSWE
MiniMax M3.1 Flash73.7%SourceMuse Spark 1.375.4%SourceMuse Spark 1.3 leads this result
NL2Repo
MiniMax M3.1 Flash61.2%SourceMuse Spark 1.3—Not directly comparable
Terminal-Bench 2.1
MiniMax M3.1 Flash—Muse Spark 1.388.8%SourceNot directly comparable
SWE-Atlas Codebase QnA
MiniMax M3.1 Flash—Muse Spark 1.359.4%SourceNot directly comparable
CursorBench 4.0
MiniMax M3.1 Flash—Muse Spark 1.341.6%SourceNot directly comparable
Bug Hunt Bench
MiniMax M3.1 Flash—Muse Spark 1.332.2 fixesSourceNot directly comparable
FrontierSWE v2
MiniMax M3.1 Flash—Muse Spark 1.325.8%SourceNot directly comparable
Reasoning
Multimodal
Knowledge
HLE w/o tools
MiniMax M3.1 Flash43.4%SourceMuse Spark 1.3—Not directly comparable
Instruction following
Gray Swan IPI (15 attempts)
MiniMax M3.1 Flash—Muse Spark 1.315.9%SourceNot directly comparable
Math
29 public results · 3 shared
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Last updated October 19, 2026