MModelspectraIndependent AI Model Intelligence
Head-to-head · Updated 2026-09-08
中文
Model Comparison · Head to Head

MiniMax M3 vs Muse Spark 1.3

MiniMax M3 wins on Overall; 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.

VendorMiniMax / Meta
Data fields15+ dimensions
Updated2026-09-08
Read~9 min
01

Verdict at a glance

Bottom line: Choose MiniMax M3 when long-context reliability matter most; choose Muse Spark 1.3 when lower latency is the priority.

Choose MiniMax M3 if you…

  • Strong agent ability
  • Open source
  • Low price
  • Best for: AI agents, Open source, Chinese

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
02

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.

MiniMax M3 Higher overall
MiniMax · #16 overall
Overall70
Coding68
Multimodal70
VS
Muse Spark 1.3
Meta · #22 overall
Overall64
Coding88
Multimodal76

Aggregated from public sources and independently weighted; methodology on the Terms page. Scores within 3 points are treated as statistically tied.

03

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.

DimensionMiniMax M3Muse Spark 1.3Verdict
VendorMiniMax (CN)Meta (US)Different vendors
Released2026.062026.09Muse Spark 1.3 is newer
Overall (rank)70 · #1664 · #22MiniMax M3 +6
Coding68 · #1788 · #5Muse Spark 1.3 +20
Multimodal70 · #2276 · #17Muse Spark 1.3 +6
Context window1M256KMiniMax M3 larger
Max output64K64KTie
Effective-context9085MiniMax M3 more reliable
Input $/1M$0.6$1.25MiniMax M3 cheaper
Output $/1M$2.4$4.25MiniMax M3 cheaper
Cache discountnonenoneTie
Speed~60 tok/s~70 tok/sMuse Spark 1.3 faster
TTFT0.5s0.4sMuse Spark 1.3 snappier
Function calling8472MiniMax M3 ahead
Refusal rate~7%~4%Muse Spark 1.3 less restrictive
English7084Muse Spark 1.3
Chinese8862MiniMax M3
Modalitiestexttext, imagedifferent coverage
Open weightsYesYesBoth open
Fine-tuningYesYes
Free tierHailuo AI free; open-platform quotaOpen weights free to self-host; hosted API at $1.25/$4.25 per 1M
SOC2 / no-trainno / yesno / yes
Private deploymentYesYesBoth 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.

04

Dimension-by-dimension analysis

Reasoning & overall intelligence

MiniMax M3 leads the overall aggregate by 6 points (70 vs 64). That is a meaningful, not marginal, edge on hard, multi-step reasoning, while Muse Spark 1.3 remains a strong generalist that is not out of its depth on routine work.

Agentic coding

This is a decisive gap: Muse Spark 1.3 scores 88 against 68. On multi-file edits, SWE-style tickets and long-horizon agent loops Muse Spark 1.3 needs fewer correction turns; MiniMax M3 is still competent for scripts and assisted completion but trails as task complexity rises.

Multimodal

Muse Spark 1.3 leads multimodal 76 vs 70. 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

Muse Spark 1.3 is faster in interactive use: ~70 tok/s with 0.4s TTFT versus ~60 tok/s with 0.5s TTFT (about 1.2× 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 1M for MiniMax M3 and 256K for Muse Spark 1.3. Effective-context scores point the same way as window size — MiniMax M3 is ahead on usable recall (90 vs 85), so prefer it for long-document work where details cannot be missed.

Price & total cost

MiniMax M3 is the cheaper API at $0.6/$2.4 versus Muse Spark 1.3 at $1.25/$4.25 per 1M input/output tokens — list input is about 2.1× lower.

Chinese vs English

English: MiniMax M3 70 vs Muse Spark 1.3 84. Chinese: 88 vs 62. For Chinese-language production, MiniMax M3 is the stronger pick. Note that non-Chinese models generally require overseas network access for their APIs.

Tool use & ecosystem

Function-calling score: MiniMax M3 84 vs Muse Spark 1.3 72, so MiniMax M3 has the edge on structured tool use. Fine-tuning is available from MiniMax M3 and Muse Spark 1.3. Factor in existing SDK/plugin familiarity — switching cost often outweighs a few-point tool-use gap.

05

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 monthMiniMax M3Muse Spark 1.3Gap
List priceno cache applied$132100M in × $0.6  +  30M out × $2.4$252100M in × $1.25  +  30M out × $4.251.91×gap
With caching90% of inputs cache-hit$132no published cache discount$252no published cache discount1.91×gap

Illustrative model; your input/output mix and cache-hit ratio change the result. Prices are list rates before any enterprise agreement.

06

Decision tree

IF the workload is agentic or multi-file coding and a wrong first pass is expensive  →  choose Muse Spark 1.3 (coding 88 vs 68).
IF you serve real-time users and latency is a product KPI  →  choose Muse Spark 1.3 (~70 tok/s, 0.4s TTFT).
IF you are cost-driven at scale on routine, high-volume workloads  →  choose MiniMax M3 ($$0.6/$$2.4 per 1M in/out).
IF long-document recall has to be near-perfect  →  choose MiniMax M3 (effective context 90 vs 85).
07

Frequently asked questions

Is Muse Spark 1.3 worth the higher price over MiniMax M3?
At list the input rate is 2.1x higher, but cache discounts and output pricing narrow the effective gap. Pay the premium when Muse Spark 1.3's stronger dimensions protect revenue; for routine volume MiniMax M3 is the economical pick.
Which is better for agentic coding?
Muse Spark 1.3 is decisively stronger for coding (88 vs 68 on the aggregate). The gap shows on SWE-style multi-file tasks and long agent loops that need fewer correction turns; MiniMax M3 is fine for routine scripts.
How do costs compare at 100M tokens/month with caching?
At 100M input + 30M output with 90% of inputs cache-hit, MiniMax M3 is about $132/month and Muse Spark 1.3 about $252/month after cache discounts ($132 and $252 at list).
Does the bigger context window actually matter?
Nominal windows are MiniMax M3 (1M) and Muse Spark 1.3 (256K), but usable recall follows the effective-context score (90 vs 85). Prefer the higher effective-context model for long-document work where nothing can be missed.
How do they differ for Chinese-language and data-residency use?
MiniMax M3 is the Chinese model (Chinese score 88, domestic cloud, possible private deployment) while Muse Spark 1.3 is the global model (Chinese 62, overseas API). Pick by language quality, access path and where data must reside.
Which handles multimodal inputs better?
Multimodal scores are 70 (MiniMax M3) vs 76 (Muse Spark 1.3), with modality coverage text versus text/image. Match the model to the input types your product actually receives.