Gemini 3.8 Flash vs Granite Embedding 278m Multilingual
At a Glance
| Compare |  Gemini 3.8 FlashGoogle DeepMind | |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | #8 of 4682.0 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 54.7–88.0 | UnrankedNot in the 46-model eligible cohort |
| CostLower is better · Published-token output estimate | #26 of 44$0.160 per LiveBench case | UnrankedNot in the 44-model eligible cohort |
| EfficiencyHigher is better · MM Efficiency v1.5 | #8 of 3860.4 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 46.7–63.4 | UnrankedNot in the 38-model eligible cohort |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.75Google AI ↗ · Sep 2, 2026 | $0.106IBM watsonx.ai ↗ · Aug 29, 2026 |
| Output priceFrom · USD / 1M tokens | $3.75Google AI ↗ · Sep 2, 2026 | Not reported |
| Context windowMaximum documented tokens | 1,049K | 1K |
| Model facts checked | Sep 2, 2026View model evidence → | Aug 28, 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 3.8 Flash | granite-embedding-278m-multilingual |
|---|---|---|
| Developer | Google DeepMind | IBM |
| Family | Gemini 3 | Granite Embedding 278m Multilingual |
| Model | Gemini 3.8 Flash | granite-embedding-278m-multilingual |
| Version | Gemini 3.8 Flash | granite-embedding-278m-multilingual |
| Lifecycle | active | active |
| Released | 2026-09-02 | 2024-12-18 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image, Video, Audio, Document | Text |
| Output modalities | Text | Embedding |
| Context window | 1,049K | 1K |
| Total parameters | Unknown | 278M |
| Active parameters | Unknown | Unknown |
| License | Unknown | apache-2.0 |
| Open weights | No | Yes |
| API available | Yes | Yes |
| Self-hostable | No | Yes |
| Provider access | Google AI (Standard), Google Gemini (Standard) | Hugging Face (Standard), IBM watsonx.ai (Pay as you go) |
| Capabilities | chat, code_execution, computer_use, generation, reasoning, structured_outputs, tools | embeddings |
Gemini 3.8 Flash Capabilities
Granite Embedding 278m Multilingual Capabilities
Related Comparisons
| A | Pair | B | Context |
|---|---|---|---|
Gemini 3.7 FlashGoogle DeepMind | vs | Gemini 3.8 FlashGoogle DeepMind | family variantstext |
Gemini 3.8 FlashGoogle DeepMind | vs |  GPT-6 AstraOpenAI | cross-developer peerstext |
Claude Fable 5.1Anthropic | vs | Gemini 3.8 FlashGoogle DeepMind | cross-developer peerstext |
Gemini 3.1 ProGoogle DeepMind | vs | Gemini 3.8 FlashGoogle DeepMind | family variantstext |
 DeepSeek-V4.1-FlashDeepSeek | vs | Gemini 3.8 FlashGoogle DeepMind | cross-developer peerstext |
Gemini 3.8 FlashGoogle DeepMind | vs | Grok 4.6xAI | cross-developer peerstext |
Gemini 3.8 FlashGoogle DeepMind | vs |  Qwen3.8-MaxQwen | cross-developer peerstext |
Gemini 3.8 FlashGoogle DeepMind | vs |  Kimi-K3Moonshot AI | cross-developer peerstext |
 MiniMax-M3MiniMax | vs | Gemini 3.8 FlashGoogle DeepMind | cross-developer peerstext |
Gemini 3.8 FlashGoogle DeepMind | vs |  GLM-5.3Z.ai | cross-developer peerstext |
Gemini 3.8 FlashGoogle DeepMind | vs |  Hy4 previewTencent | cross-developer peerstext |
Seed 2.1 ProByteDance Seed | vs | Gemini 3.8 FlashGoogle DeepMind | cross-developer peerstext |
Primary Evidence
Sources and Freshness
Questions
Gemini 3.8 Flash vs Granite Embedding 278m Multilingual FAQs
Is Gemini 3.8 Flash or Granite Embedding 278m Multilingual better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Gemini 3.8 Flash and Granite Embedding 278m Multilingual, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Gemini 3.8 Flash or Granite Embedding 278m Multilingual?+
Gemini 3.8 Flash is $0.75 and Granite Embedding 278m Multilingual is $0.106 per million tokens, so Granite Embedding 278m Multilingual is cheaper on this metric. Only Gemini 3.8 Flash has a directly sourced output price: $3.75 per million tokens.
Which has a larger context window, Gemini 3.8 Flash or Granite Embedding 278m Multilingual?+
Gemini 3.8 Flash has the larger sourced context window. Gemini 3.8 Flash supports 1,049K and Granite Embedding 278m Multilingual supports 1K.
Which performs better in benchmarks, Gemini 3.8 Flash or Granite Embedding 278m Multilingual?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Gemini 3.8 Flash or Granite Embedding 278m Multilingual be self-hosted?+
Granite Embedding 278m Multilingual is the only model in this pair currently marked as self-hostable. Gemini 3.8 Flash is not marked open weight; Granite Embedding 278m Multilingual is open weight.
Can Gemini 3.8 Flash and Granite Embedding 278m Multilingual understand images?+
Gemini 3.8 Flash is documented with image input; Granite Embedding 278m Multilingual is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Gemini 3.8 Flash or Granite Embedding 278m Multilingual?+
Neither has a larger sourced maximum output. Gemini 3.8 Flash is 66K and Granite Embedding 278m Multilingual is —.
Do Gemini 3.8 Flash and Granite Embedding 278m Multilingual support reasoning and tool use?+
Gemini 3.8 Flash: reasoning, tool calling, and image input. Granite Embedding 278m Multilingual: none of these features are definitively sourced. Feature support does not establish relative quality.
Which is available from more inference providers, Gemini 3.8 Flash or Granite Embedding 278m Multilingual?+
Gemini 3.8 Flash has 2 sourced provider routes; Granite Embedding 278m Multilingual has 2, a tie.
Which offers better value, Gemini 3.8 Flash or Granite Embedding 278m Multilingual?+
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.