FLUX.2 klein 4B FP8 vs Gemini 3.1 Flash Lite
At a Glance
| Compare |  FLUX.2 klein 4B FP8Black Forest Labs |  Gemini 3.1 Flash LiteGoogle DeepMind |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | Not reported | $0.25Google AI ↗ · Aug 29, 2026 |
| Output priceFrom · USD / 1M tokens | Not reported | $1.50Google AI ↗ · Aug 29, 2026 |
| Context windowMaximum documented tokens | Not reported | 1,049K |
| Model facts checked | Aug 28, 2026View model evidence → | Aug 29, 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 | FLUX.2-klein-4b-fp8 | Gemini 3.1 Flash-Lite |
|---|---|---|
| Developer | Black Forest Labs | Google DeepMind |
| Family | Flux 2 Klein 4b Fp8 | Gemini 3 |
| Model | FLUX.2-klein-4b-fp8 | Gemini 3.1 Flash-Lite |
| Version | FLUX.2-klein-4b-fp8 | Gemini 3.1 Flash-Lite |
| Lifecycle | active | active |
| Released | 2026-01-15 | Unknown |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Image | Text, Image, Video, Audio, Document |
| Output modalities | Image | Text |
| Context window | Unknown | 1,049K |
| Total parameters | 4B | Unknown |
| Active parameters | Unknown | Unknown |
| License | apache-2.0 | Unknown |
| Open weights | Yes | No |
| API available | Unknown | Yes |
| Self-hostable | Yes | No |
| Provider access | Unknown | Google AI (Standard), Google Gemini (Standard) |
| Capabilities | generation | chat, generation, reasoning, tools |
FLUX.2 klein 4B FP8 Capabilities
Gemini 3.1 Flash Lite Capabilities
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Primary Evidence
Sources and Freshness
Questions
FLUX.2 klein 4B FP8 vs Gemini 3.1 Flash Lite FAQs
Is FLUX.2 klein 4B FP8 or Gemini 3.1 Flash Lite better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both FLUX.2 klein 4B FP8 and Gemini 3.1 Flash Lite, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, FLUX.2 klein 4B FP8 or Gemini 3.1 Flash Lite?+
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, FLUX.2 klein 4B FP8 or Gemini 3.1 Flash Lite?+
Neither model has a larger sourced context window in this comparison. FLUX.2 klein 4B FP8 is — and Gemini 3.1 Flash Lite is 1,049K.
Which performs better in benchmarks, FLUX.2 klein 4B FP8 or Gemini 3.1 Flash Lite?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can FLUX.2 klein 4B FP8 or Gemini 3.1 Flash Lite be self-hosted?+
FLUX.2 klein 4B FP8 is the only model in this pair currently marked as self-hostable. FLUX.2 klein 4B FP8 is open weight; Gemini 3.1 Flash Lite is not marked open weight.
Can FLUX.2 klein 4B FP8 and Gemini 3.1 Flash Lite understand images?+
FLUX.2 klein 4B FP8 is documented with image input; Gemini 3.1 Flash Lite is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, FLUX.2 klein 4B FP8 or Gemini 3.1 Flash Lite?+
Neither has a larger sourced maximum output. FLUX.2 klein 4B FP8 is — and Gemini 3.1 Flash Lite is 66K.
Do FLUX.2 klein 4B FP8 and Gemini 3.1 Flash Lite support reasoning and tool use?+
FLUX.2 klein 4B FP8: image input. Gemini 3.1 Flash Lite: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, FLUX.2 klein 4B FP8 or Gemini 3.1 Flash Lite?+
FLUX.2 klein 4B FP8 has 0 sourced provider routes; Gemini 3.1 Flash Lite has 2, so Gemini 3.1 Flash Lite has broader tracked availability.
Which offers better value, FLUX.2 klein 4B FP8 or Gemini 3.1 Flash Lite?+
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.