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

Gemini 3.1 Pro vs Muse Spark 1.3

Gemini 3.1 Pro wins on Overall, Multimodal; Muse Spark 1.3 wins on Coding. 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 long-context reliability matter most; choose Muse Spark 1.3 when lower cost, lower latency 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 1.3 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.

Gemini 3.1 Pro Higher overall
Google · #5 overall
Overall83
Coding74
Multimodal92
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.

DimensionGemini 3.1 ProMuse Spark 1.3Verdict
VendorGoogle (US)Meta (US)Different vendors
Released2026.052026.09Muse Spark 1.3 is newer
Overall (rank)83 · #564 · #22Gemini 3.1 Pro +19
Coding74 · #1288 · #5Muse Spark 1.3 +14
Multimodal92 · #376 · #17Gemini 3.1 Pro +16
Context window2M256KGemini 3.1 Pro larger
Max output128K64KMuse Spark 1.3 longer
Effective-context9285Gemini 3.1 Pro more reliable
Input $/1M$2$1.25Muse Spark 1.3 cheaper
Output $/1M$12$4.25Muse Spark 1.3 cheaper
Cache discountnonenoneTie
Speed~60 tok/s~70 tok/sMuse Spark 1.3 faster
TTFT0.7s0.4sMuse Spark 1.3 snappier
Function calling7872Gemini 3.1 Pro ahead
Refusal rate~12%~4%Muse Spark 1.3 less restrictive
English9084Gemini 3.1 Pro
Chinese7562Gemini 3.1 Pro
Modalitiestext, image, audio, videotext, imagedifferent coverage
Open weightsNoYesMuse Spark 1.3 is open
Fine-tuningYesYes
Free tierGemini App free with limited quota; API free tier availableOpen weights free to self-host; hosted API at $1.25/$4.25 per 1M
SOC2 / no-trainyes / yesno / yes
Private deploymentNoYesMuse Spark 1.3

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

Gemini 3.1 Pro leads the overall aggregate by 19 points (83 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 clear gap: Muse Spark 1.3 scores 88 against 74. On multi-file edits, SWE-style tickets and long-horizon agent loops Muse Spark 1.3 needs fewer correction turns; Gemini 3.1 Pro is still competent for scripts and assisted completion but trails as task complexity rises.

Multimodal

Gemini 3.1 Pro leads multimodal 92 vs 76. A concrete modality difference: Gemini 3.1 Pro additionally handles audio, video.

Speed & latency

Muse Spark 1.3 is faster in interactive use: ~70 tok/s with 0.4s TTFT versus ~60 tok/s with 0.7s 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 2M for Gemini 3.1 Pro and 256K for Muse Spark 1.3. Effective-context scores point the same way as window size — Gemini 3.1 Pro is ahead on usable recall (92 vs 85), so prefer it for long-document work where details cannot be missed.

Price & total cost

Muse Spark 1.3 is the cheaper API at $1.25/$4.25 versus Gemini 3.1 Pro at $2/$12 per 1M input/output tokens — list input is about 1.6× lower.

Chinese vs English

English: Gemini 3.1 Pro 90 vs Muse Spark 1.3 84. Chinese: 75 vs 62. 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 1.3 72, so Gemini 3.1 Pro has the edge on structured tool use. Fine-tuning is available from Gemini 3.1 Pro and Muse Spark 1.3. 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 monthGemini 3.1 ProMuse Spark 1.3Gap
List priceno cache applied$560100M in × $2  +  30M out × $12$252100M in × $1.25  +  30M out × $4.252.22×gap
With caching90% of inputs cache-hit$560no published cache discount$252no published cache discount2.22×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 74).
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 Muse Spark 1.3 ($$1.25/$$4.25 per 1M in/out).
IF long-document recall has to be near-perfect  →  choose Gemini 3.1 Pro (effective context 92 vs 85).
07

Frequently asked questions

Which is better for agentic coding?
Muse Spark 1.3 is decisively stronger for coding (88 vs 74 on the aggregate). The gap shows on SWE-style multi-file tasks and long agent loops that need fewer correction turns; Gemini 3.1 Pro 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 1.3. In unattended pipelines that means more retries, fallbacks and manual review, raising effective cost and latency even when the token price is lower.
How do costs compare at 100M tokens/month with caching?
At 100M input + 30M output with 90% of inputs cache-hit, Gemini 3.1 Pro is about $560/month and Muse Spark 1.3 about $252/month after cache discounts ($560 and $252 at list).
Does the bigger context window actually matter?
Nominal windows are Gemini 3.1 Pro (2M) and Muse Spark 1.3 (256K), but usable recall follows the effective-context score (92 vs 85). Prefer the higher effective-context model for long-document work where nothing can be missed.
Can I self-host either model?
Muse Spark 1.3 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 76 (Muse Spark 1.3), with modality coverage text/image/audio/video versus text/image. Match the model to the input types your product actually receives.