Gemini Embedding 2 vs Llama 4 Scout
Compare Google and Meta (via Together AI) AI models
Cost Comparison (1000 input + 500 output tokens, 100 requests/day)
Gemini Embedding 2
Llama 4 Scout
Cost Differences
Llama 4 Scout costs less than Gemini Embedding 2
Feature Comparison
| Feature | Gemini Embedding 2 | Llama 4 Scout |
|---|---|---|
| Provider | Meta (via Together AI) | |
| Input Price | $0.20/1M tokens | $0.08/1M tokens |
| Output Price | $0.20/1M tokens | $0.30/1M tokens |
| Context Window | 8,192 tokens | 10,000,000 tokens |
| Max Output | 3,072 tokens | 32,768 tokens |
| Category | embedding | efficient |
| Capabilities | textvisionaudiovideoembeddings | textvisioncode |
| Release Date | 3/10/2026 | 4/5/2025 |
Gemini Embedding 2 vs Llama 4 Scout: Which Should You Choose?
Choosing between Gemini Embedding 2 and Llama 4 Scout depends on your priorities: cost efficiency, context length, or raw capability. Llama 4 Scout is the more affordable option at $0.08/1M input tokens — 60% cheaper than Gemini Embedding 2. Meanwhile, Llama 4 Scout offers a significantly larger context window at 10,000,000 tokens vs 8,192 for Gemini Embedding 2.
These models come from different providers — Google and Meta (via Together AI) — which means different API ecosystems, SDKs, rate limits, and terms of service. If you're already integrated with Google, switching to Meta (via Together AI)involves migration effort beyond just pricing. Factor in your existing infrastructure when deciding.
These models target different tiers: Gemini Embedding 2 is a embedding model while Llama 4 Scout is efficient. This means they're optimized for different workloads. Llama 4 Scout targets more demanding workloads, while Gemini Embedding 2 provides a cost-effective option for everyday tasks.
Output costs matter too. Gemini Embedding 2 charges $0.20/1M output tokens vs $0.30 for Llama 4 Scout. For generation-heavy workloads (content creation, code generation, summarization), output pricing often dominates your bill. Llama 4 Scout has the edge here at $0.30/1M output tokens.
Multimodal capabilities: Both models support vision (image understanding), so you can send images alongside text prompts with either option.
Best Use Cases
Choose Gemini Embedding 2 when:
- • You need more capabilities (audio, video, embeddings)
- • You're already using Google's API ecosystem
Choose Llama 4 Scout when:
- • Budget is a primary concern
- • You need a larger context window (10,000,000 tokens)
- • You need longer outputs (up to 32,768 tokens)
- • You're already using Meta (via Together AI)'s API ecosystem
- • You're running high-volume, latency-sensitive workloads
Try Different Scenarios
Use the calculator below to see how costs change with different usage patterns
Gemini Embedding 2 (Google)
Llama 4 Scout (Meta (via Together AI))
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