MiniMax M2.7 vs Muse Spark 1.1
MiniMax M2.7 wins on Overall; Muse Spark 1.1 wins on Multimodal. Every numeric field is compared below, with a worked monthly-cost example and a pick rule for each use case.
Verdict at a glance
Choose MiniMax M2.7 if you…
- Light and fast
- Open source
- Low price
- Best for: Light tasks, Open source, Low cost
Choose Muse Spark 1.1 if you…
- Completely free open weights
- By Meta
- Self-hostable
- Multimodal
- Best for: Open research, Local deployment, Experimental
Head-to-head aggregate scores
Scores are 0–100, aggregated from public benchmark information and independently weighted across three leaderboards. Rank is out of 22 tracked models.
Aggregated from public sources and independently weighted; methodology on the Terms page. Scores within 3 points are treated as statistically tied.
Specs & pricing — every field side by side
List API prices in USD per 1M tokens. The highlighted cell is the stronger value/capability on that row.
| Dimension | MiniMax M2.7 | Muse Spark 1.1 | Verdict |
|---|---|---|---|
| Vendor | MiniMax (CN) | Meta (US) | Different vendors |
| Released | 2026.05 | 2026.08 | Muse Spark 1.1 is newer |
| Overall (rank) | 64 · #19 | 61 · #22 | MiniMax M2.7 +3 |
| Coding | 59 · #21 | 59 · #21 | Tie |
| Multimodal | 62 · #22 | 75 · #15 | Muse Spark 1.1 +13 |
| Context window | 205K | 256K | Muse Spark 1.1 larger |
| Max output | 32K | 64K | Muse Spark 1.1 longer |
| Effective-context | 85 | 80 | MiniMax M2.7 more reliable |
| Input $/1M | $0.27 | Free | Muse Spark 1.1 cheaper |
| Output $/1M | $1.08 | Free | Muse Spark 1.1 cheaper |
| Cache discount | none | none | Tie |
| Speed | ~70 tok/s | Hardware-dependent | MiniMax M2.7 faster |
| TTFT | 0.4s | Hardware-dependent | MiniMax M2.7 snappier |
| Function calling | 72 | 65 | MiniMax M2.7 ahead |
| Refusal rate | ~6% | ~4% | Muse Spark 1.1 less restrictive |
| English | 65 | 78 | Muse Spark 1.1 |
| Chinese | 82 | 60 | MiniMax M2.7 |
| Modalities | text | text, image | different coverage |
| Open weights | Yes | Yes | Both open |
| Fine-tuning | Yes | Yes | |
| Free tier | Hailuo AI free | Fully free (model weights) | |
| SOC2 / no-train | no / yes | no / yes | |
| Private deployment | Yes | Yes | Both support it |
Fields drawn from vendor public documentation and the Modelspectra 22-model dataset; speed varies with network, concurrency and prompt length. Verify current pricing before purchase.
Dimension-by-dimension analysis
Reasoning & overall intelligence
MiniMax M2.7 leads the overall aggregate by 3 points (64 vs 61). That is a meaningful, not marginal, edge on hard, multi-step reasoning, while Muse Spark 1.1 remains a strong generalist that is not out of its depth on routine work.
Agentic coding
Both score 59/100 on the coding aggregate, so expect parity on most day-to-day engineering tasks.
Multimodal
Muse Spark 1.1 leads multimodal 75 vs 62. A concrete modality difference: Muse Spark 1.1 additionally handles image. Neither emits native video, so the comparison is about parsing images and documents, not generation.
Speed & latency
Muse Spark 1.1 is self-hosted, so its speed depends on your hardware; against a managed API like MiniMax M2.7 (~70 tok/s, 0.4s TTFT) compare on your own infrastructure before assuming latency.
Context: window vs usable recall
Nominal windows are 205K for MiniMax M2.7 and 256K for Muse Spark 1.1. Crucially, the larger nominal window does not win on usable recall: Muse Spark 1.1 advertises 256K but MiniMax M2.7 scores higher on effective-context (85 vs 80), i.e. it actually retains more of what it was given.
Price & total cost
Muse Spark 1.1 is free open weights (you pay only for the infrastructure you run it on), while MiniMax M2.7 is a paid API at $0.27/$1.08 per 1M tokens. The real comparison is total cost of ownership — GPU/ops against a managed bill — not list price alone.
Chinese vs English
English: MiniMax M2.7 65 vs Muse Spark 1.1 78. Chinese: 82 vs 60. For Chinese-language production, MiniMax M2.7 is the stronger pick. Note that non-Chinese models generally require overseas network access for their APIs.
Tool use & ecosystem
Function-calling score: MiniMax M2.7 72 vs Muse Spark 1.1 65, so MiniMax M2.7 has the edge on structured tool use. Fine-tuning is available from MiniMax M2.7 and Muse Spark 1.1. Factor in existing SDK/plugin familiarity — switching cost often outweighs a few-point tool-use gap.
Cost worked example — same workload, real token math
Assume a production workload of 100M input + 30M output tokens per month, with 90% of input tokens served from cache. Figures use public list prices.
Illustrative model; your input/output mix and cache-hit ratio change the result. Prices are list rates before any enterprise agreement.