MiniMax M2.7 vs Muse Spark 1.3
Muse Spark 1.3 wins on Coding, 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 1.3 if you…
- #1 DeepSWE long-horizon coding
- Open weights, self-hostable
- Extremely low cost
- Fast
- Best for: Coding, Open-source deployment, Budget
Head-to-head aggregate scores
Scores are 0–100, aggregated from public benchmark information and independently weighted across three leaderboards. Rank is out of 26 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 1.3 | Verdict |
|---|---|---|---|
| Vendor | MiniMax (CN) | Meta (US) | Different vendors |
| Released | 2026.05 | 2026.09 | Muse Spark 1.3 is newer |
| Overall (rank) | 64 · #22 | 64 · #22 | Tie |
| Coding | 59 · #25 | 88 · #5 | Muse Spark 1.3 +29 |
| Multimodal | 62 · #26 | 76 · #17 | Muse Spark 1.3 +14 |
| Context window | 205K | 256K | Muse Spark 1.3 larger |
| Max output | 32K | 64K | Muse Spark 1.3 longer |
| Effective-context | 85 | 85 | Tie |
| Input $/1M | $0.27 | $1.25 | MiniMax M2.7 cheaper |
| Output $/1M | $1.08 | $4.25 | MiniMax M2.7 cheaper |
| Cache discount | none | none | Tie |
| Speed | ~70 tok/s | ~70 tok/s | Tie |
| TTFT | 0.4s | 0.4s | Tie |
| Function calling | 72 | 72 | Tie |
| Refusal rate | ~6% | ~4% | Muse Spark 1.3 less restrictive |
| English | 65 | 84 | Muse Spark 1.3 |
| Chinese | 82 | 62 | MiniMax M2.7 |
| Modalities | text | text, image | different coverage |
| Open weights | Yes | Yes | Both open |
| Fine-tuning | Yes | Yes | |
| Free tier | Hailuo AI free | Open weights free to self-host; hosted API at $1.25/$4.25 per 1M | |
| SOC2 / no-train | no / yes | no / yes | |
| Private deployment | Yes | Yes | Both support it |
Fields drawn from vendor public documentation and the Modelspectra 26-model dataset; speed varies with network, concurrency and prompt length. Verify current pricing before purchase.
Dimension-by-dimension analysis
Reasoning & overall intelligence
The two are level on the overall aggregate (64/100 each), a gap inside the 3-point band where rankings are statistically indistinguishable and task-specific results can swap.
Agentic coding
This is a decisive gap: Muse Spark 1.3 scores 88 against 59. On multi-file edits, SWE-style tickets and long-horizon agent loops Muse Spark 1.3 needs fewer correction turns; MiniMax M2.7 is still competent for scripts and assisted completion but trails as task complexity rises.
Multimodal
Muse Spark 1.3 leads multimodal 76 vs 62. A concrete modality difference: Muse Spark 1.3 additionally handles image. Neither emits native video, so the comparison is about parsing images and documents, not generation.
Speed & latency
MiniMax M2.7 is faster in interactive use: ~70 tok/s with 0.4s TTFT versus ~70 tok/s with 0.4s TTFT (about 1.0× the throughput). For conversational UIs where perceived responsiveness drives retention, that edge is a real product factor even when raw reasoning is lower.
Context: window vs usable recall
Nominal windows are 205K for MiniMax M2.7 and 256K for Muse Spark 1.3. Crucially, the larger nominal window does not win on usable recall: Muse Spark 1.3 advertises 256K but MiniMax M2.7 scores higher on effective-context (85 vs 85), i.e. it actually retains more of what it was given.
Price & total cost
MiniMax M2.7 is the cheaper API at $0.27/$1.08 versus Muse Spark 1.3 at $1.25/$4.25 per 1M input/output tokens — list input is about 4.6× lower.
Chinese vs English
English: MiniMax M2.7 65 vs Muse Spark 1.3 84. Chinese: 82 vs 62. 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 1.3 72, so MiniMax M2.7 has the edge on structured tool use. Fine-tuning is available from MiniMax M2.7 and Muse Spark 1.3. 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.
| Scenario · per month | MiniMax M2.7 | Muse Spark 1.3 | Gap |
|---|---|---|---|
| List priceno cache applied | $59100M in × $0.27 + 30M out × $1.08 | $252100M in × $1.25 + 30M out × $4.25 | 4.27×gap |
| With caching90% of inputs cache-hit | $59no published cache discount | $252no published cache discount | 4.27×gap |
Illustrative model; your input/output mix and cache-hit ratio change the result. Prices are list rates before any enterprise agreement.