Gemini 3.1 Flash Lite vs Muse Spark 1.2
At a Glance
| Compare |  Gemini 3.1 Flash LiteGoogle DeepMind |  Muse Spark 1.2Meta |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.25Google AI ↗ · Aug 29, 2026 | Not reported |
| Output priceFrom · USD / 1M tokens | $1.50Google AI ↗ · Aug 29, 2026 | Not reported |
| Context windowMaximum documented tokens | 1,049K | Not reported |
| Model facts checked | Aug 29, 2026View model evidence → | Sep 3, 2026View model evidence → |
Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →
Available Benchmarks
Side-by-Side Facts
| Field | Gemini 3.1 Flash-Lite | Muse Spark 1.2 |
|---|---|---|
| Developer | Google DeepMind | Meta |
| Family | Gemini 3 | Muse Spark |
| Model | Gemini 3.1 Flash-Lite | Muse Spark 1.2 |
| Version | Gemini 3.1 Flash-Lite | Muse Spark 1.2 |
| Lifecycle | active | preview |
| Released | Unknown | 2026-08-05 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image, Video, Audio, Document | Text, Image, Video, Audio |
| Output modalities | Text | Text |
| Context window | 1,049K | Unknown |
| Total parameters | Unknown | Unknown |
| Active parameters | Unknown | Unknown |
| License | Unknown | Unknown |
| Open weights | No | No |
| API available | Yes | Yes |
| Self-hostable | No | No |
| Provider access | Google AI (Standard), Google Gemini (Standard) | Unknown |
| Capabilities | chat, generation, reasoning, tools | chat, computer-use, generation, reasoning, research, structured_outputs, tools |
Gemini 3.1 Flash Lite Capabilities
Muse Spark 1.2 Capabilities
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Primary Evidence
Sources and Freshness
Questions
Gemini 3.1 Flash Lite vs Muse Spark 1.2 FAQs
Is Gemini 3.1 Flash Lite or Muse Spark 1.2 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Gemini 3.1 Flash Lite and Muse Spark 1.2, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Gemini 3.1 Flash Lite or Muse Spark 1.2?+
Only Gemini 3.1 Flash Lite has a directly sourced input price: $0.25 per million tokens. Only Gemini 3.1 Flash Lite has a directly sourced output price: $1.50 per million tokens.
Which has a larger context window, Gemini 3.1 Flash Lite or Muse Spark 1.2?+
Neither model has a larger sourced context window in this comparison. Gemini 3.1 Flash Lite is 1,049K and Muse Spark 1.2 is —.
Which performs better in benchmarks, Gemini 3.1 Flash Lite or Muse Spark 1.2?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Gemini 3.1 Flash Lite or Muse Spark 1.2 be self-hosted?+
Both models have the same recorded self-hosting status: unsupported. Gemini 3.1 Flash Lite is not marked open weight; Muse Spark 1.2 is not marked open weight.
Can Gemini 3.1 Flash Lite and Muse Spark 1.2 understand images?+
Gemini 3.1 Flash Lite is documented with image input; Muse Spark 1.2 is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Gemini 3.1 Flash Lite or Muse Spark 1.2?+
Neither has a larger sourced maximum output. Gemini 3.1 Flash Lite is 66K and Muse Spark 1.2 is —.
Do Gemini 3.1 Flash Lite and Muse Spark 1.2 support reasoning and tool use?+
Gemini 3.1 Flash Lite: reasoning, tool calling, and image input. Muse Spark 1.2: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Gemini 3.1 Flash Lite or Muse Spark 1.2?+
Gemini 3.1 Flash Lite has 2 sourced provider routes; Muse Spark 1.2 has 0, so Gemini 3.1 Flash Lite has broader tracked availability.
Which offers better value, Gemini 3.1 Flash Lite or Muse Spark 1.2?+
There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.