Updated October 15, 2026. Rank says Muse Spark Open 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
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead. 0 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
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.
Code generation, repair, and software-engineering tasks
Muse Spark Open
Muse Spark Open has the higher public coding point estimate, 56.1 to 34.4, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Prompts that approach the documented context limit
Muse Spark Open
Muse Spark Open has the larger documented context window.
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Muse Spark Open is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
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 Open has no comparable published API token rate.
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
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.
Like-for-like · BenchAlign v5.8
Muse Spark Open 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.
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
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.
| Category | MiniMax M2.7 | Muse Spark Open | Basis | Reading |
|---|---|---|---|---|
| Coding | 34.4Supported · #80/150 | 56.1Supported · #29/150 | Like-for-likeBenchAlign v5.8 lane · 12 vs 0 public rows | Muse Spark Open leads · intervals overlap |
| Knowledge | 46.0Supported · #93/184 | 64.6Supported · #38/184 | Like-for-likeBenchAlign v5.8 lane · 5 vs 0 public rows | Muse Spark Open leads · intervals overlap |
| Agentic | 32.4Supported · #89/127 | 60.2Estimated · #27/127 | Directional onlyBenchAlign v5.8 lane · 8 vs 4 public rows | Directional only |
| Reasoning | 79.6Unranked · 2 rankable rows | 79.6Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | 83.6Unranked · 4 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | 93.0#13/146 | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 70.5Unranked · 4 rankable rows | Not ranked | Not comparableProvisional lane · 2 vs 0 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.
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.
1K fresh input + 500 output tokens
Muse Spark Open has no comparable published API token rate.
50K fresh input + 3K output tokens
Muse Spark Open has no comparable published API token rate.
200K cached + 20K fresh input + 10K output tokens
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 Open has no comparable published API token rate.
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
Maximum documented context; output-token limits may be lower.
MiniMax M2.7
200K
Muse Spark Open
MiniMax M2.7
Not sourced
Muse Spark Open
Not sourced
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 Open
No comparable hosted API rate
Meta AI Research blogMiniMax M2.7
Not sourced
Muse Spark Open
text, image, video, audio
Meta Muse Spark Open model cardMiniMax M2.7
Not sourced
Muse Spark Open
MiniMax M2.7
Not sourced
Muse Spark Open
Open Weights · Hugging Face, Amazon SageMaker AI, Google Cloud Model Garden, Microsoft Foundry Models, self-hosted inference
Meta AI Research: Muse Spark OpenMiniMax M2.7
Non-Reasoning
Muse Spark Open
Reasoning
MiniMax M2.7
Open Weight
Muse Spark Open
Open Weight
MiniMax M2.7
Open Weight
Muse Spark Open
Open Weight
MiniMax M2.7
2026-03-18
Muse Spark Open
2026-10-15
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.
Muse Spark Open has the higher public coding point estimate, 56.1 to 34.4, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Muse Spark Open scores higher for agentic tasks on the public lane, 60.2 to 32.4. Muse Spark Open 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.
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Muse Spark Open has the larger documented context window: 1M, compared with 200K.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Terminal-Bench 2.0
Not directly comparable
τ³-bench results
Not directly comparable
Toolathlon
Not directly comparable
MLE-Bench Lite
Not directly comparable
MM-ClawBench
Not directly comparable
Claw-Eval
Not directly comparable
Gert Labs
Not directly comparable
Terminal-Bench 2.1 (Vals)
Not directly comparable
JobBench
Not directly comparable
MCP Atlas
Not directly comparable
HLE w/ tools
Not directly comparable
Toolathlon-Verified
Not directly comparable
SWE-bench Verified*
Not directly comparable
SWE-bench Pro
Not directly comparable
SWE-Rebench
Not directly comparable
SWE Multilingual
Not directly comparable
Multi-SWE Bench
Not directly comparable
VIBE-Pro
Not directly comparable
NL2Repo
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
Vibe Code Bench
Not directly comparable
React Native Evals
Not directly comparable
LiveCodeBench (Vals)
Not directly comparable
SWE-bench (Vals)
Not directly comparable
HLE
Not directly comparable
GPQA-D
Not directly comparable
MMLU-Pro (Arcee)
Not directly comparable
GPQA Diamond (Vals)
Not directly comparable
MMLU-Pro (Vals)
Not directly comparable
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Last updated October 15, 2026
