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

Gemini 3.1 Pro vs Muse Spark 1.1

Gemini 3.1 Pro wins on Overall, Coding, Multimodal. Every numeric field is compared below, with a worked monthly-cost example and a pick rule for each use case.

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

Verdict at a glance

Bottom line: Choose Gemini 3.1 Pro when coding depth, long-context reliability matter most; choose Muse Spark 1.1 when lower cost is the priority.

Choose Gemini 3.1 Pro if you…

  • Native audio and video
  • Strong scientific computing
  • Largest 2M context
  • Search integration
  • Best for: Multimodal, Scientific reasoning, Audio & video

Choose Muse Spark 1.1 if you…

  • Completely free open weights
  • By Meta
  • Self-hostable
  • Multimodal
  • Best for: Open research, Local deployment, Experimental
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 22 tracked models.

Gemini 3.1 Pro Higher overall
Google · #3 overall
Overall83
Coding74
Multimodal92
VS
Muse Spark 1.1
Meta · #22 overall
Overall61
Coding59
Multimodal75

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.

DimensionGemini 3.1 ProMuse Spark 1.1Verdict
VendorGoogle (US)Meta (US)Different vendors
Released2026.052026.08Muse Spark 1.1 is newer
Overall (rank)83 · #361 · #22Gemini 3.1 Pro +22
Coding74 · #959 · #21Gemini 3.1 Pro +15
Multimodal92 · #275 · #15Gemini 3.1 Pro +17
Context window2M256KGemini 3.1 Pro larger
Max output128K64KMuse Spark 1.1 longer
Effective-context9280Gemini 3.1 Pro more reliable
Input $/1M$2FreeMuse Spark 1.1 cheaper
Output $/1M$12FreeMuse Spark 1.1 cheaper
Cache discountnonenoneTie
Speed~60 tok/sHardware-dependentGemini 3.1 Pro faster
TTFT0.7sHardware-dependentGemini 3.1 Pro snappier
Function calling7865Gemini 3.1 Pro ahead
Refusal rate~12%~4%Muse Spark 1.1 less restrictive
English9078Gemini 3.1 Pro
Chinese7560Gemini 3.1 Pro
Modalitiestext, image, audio, videotext, imagedifferent coverage
Open weightsNoYesMuse Spark 1.1 is open
Fine-tuningYesYes
Free tierGemini App free with limited quota; API free tier availableFully free (model weights)
SOC2 / no-trainyes / yesno / yes
Private deploymentNoYesMuse Spark 1.1

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.

04

Dimension-by-dimension analysis

Reasoning & overall intelligence

Gemini 3.1 Pro leads the overall aggregate by 22 points (83 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

This is a clear gap: Gemini 3.1 Pro scores 74 against 59. On multi-file edits, SWE-style tickets and long-horizon agent loops Gemini 3.1 Pro needs fewer correction turns; Muse Spark 1.1 is still competent for scripts and assisted completion but trails as task complexity rises.

Multimodal

Gemini 3.1 Pro leads multimodal 92 vs 75. A concrete modality difference: Gemini 3.1 Pro additionally handles audio, video.

Speed & latency

Muse Spark 1.1 is self-hosted, so its speed depends on your hardware; against a managed API like Gemini 3.1 Pro (~60 tok/s, 0.7s TTFT) compare on your own infrastructure before assuming latency.

Context: window vs usable recall

Nominal windows are 2M for Gemini 3.1 Pro and 256K for Muse Spark 1.1. Effective-context scores point the same way as window size — Gemini 3.1 Pro is ahead on usable recall (92 vs 80), so prefer it for long-document work where details cannot be missed.

Price & total cost

Muse Spark 1.1 is free open weights (you pay only for the infrastructure you run it on), while Gemini 3.1 Pro is a paid API at $2/$12 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: Gemini 3.1 Pro 90 vs Muse Spark 1.1 78. Chinese: 75 vs 60. Both are US-based models; for Chinese-first workloads also compare domestic models on the leaderboard.

Tool use & ecosystem

Function-calling score: Gemini 3.1 Pro 78 vs Muse Spark 1.1 65, so Gemini 3.1 Pro has the edge on structured tool use. Fine-tuning is available from Gemini 3.1 Pro and Muse Spark 1.1. 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.

Cost note: one of these models is free open weights, so a per-token monthly bill does not apply — budget instead for GPU and operations. The paid API counterpart works out to roughly $560/month at list for this workload.

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 Gemini 3.1 Pro (coding 74 vs 59).
IF you serve real-time users and latency is a product KPI  →  choose Gemini 3.1 Pro (~60 tok/s, 0.7s TTFT).
IF you need free, self-hostable weights and can run your own GPU/ops  →  choose Muse Spark 1.1 (free open weights).
IF long-document recall has to be near-perfect  →  choose Gemini 3.1 Pro (effective context 92 vs 80).
07

Frequently asked questions

Which is better for agentic coding?
Gemini 3.1 Pro is decisively stronger for coding (74 vs 59 on the aggregate). The gap shows on SWE-style multi-file tasks and long agent loops that need fewer correction turns; Muse Spark 1.1 is fine for routine scripts.
Does Gemini 3.1 Pro's higher refusal rate matter in production?
Gemini 3.1 Pro refuses about 12% of prompts versus 4% for Muse Spark 1.1. In unattended pipelines that means more retries, fallbacks and manual review, raising effective cost and latency even when the token price is lower.
Does the bigger context window actually matter?
Nominal windows are Gemini 3.1 Pro (2M) and Muse Spark 1.1 (256K), but usable recall follows the effective-context score (92 vs 80). Prefer the higher effective-context model for long-document work where nothing can be missed.
Can I self-host either model?
Muse Spark 1.1 ships open weights and can be self-hosted (GPU permitting) for data control; Gemini 3.1 Pro is a closed managed API with no self-hosting. Choose open weights when residency or cost-at-scale dominates, managed API for convenience.
Which handles multimodal inputs better?
Multimodal scores are 92 (Gemini 3.1 Pro) vs 75 (Muse Spark 1.1), with modality coverage text/image/audio/video versus text/image. Match the model to the input types your product actually receives.
What is the single-line recommendation?
Choose Gemini 3.1 Pro for Multimodal, Scientific reasoning; choose Muse Spark 1.1 for Open research, Local deployment.