Gemini Embedding 001 vs Llama 5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Gemini Embedding 001 by Google against Llama 5 by Meta across real-world coding, reasoning, token economics, and latency.
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Gemini Embedding 001
EmbeddingEnterprise semantic text embedding model powering retrieval, semantic search, and RAG pipelines.
$0.15 / 1M
$0.00 / 1M
2K tokens
N/A
Llama 5
Recursive Self-ImprovementMeta's flagship open model featuring native agent training, recursive self-improvement, and 5M token context.
$0.00 / 1M
$0.00 / 1M
5M tokens
74%
Key Differences at a Glance
- Llama 5 is 1.0x cheaper for API usage (input + output tokens combined).
- Llama 5 has a larger context window (5M vs 2K tokens).
- Gemini Embedding 001 is faster at 400 tok/s vs 65 tok/s.
| Feature | Gemini Embedding 001 | Llama 5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 70 | 94 |
Provider | Meta | |
Input Price Cost per million input tokens | $0.15 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.03 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 2K tokens~3 A4 pages of text | 5M tokens~7,500 A4 pages of text |
Max Output Tokens Per single request | 4K | 128K |
Throughput Speed Tokens generated per second | 400 tok/s | 65 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 74% |
GPQA Diamond Graduate-level reasoning | N/A | 84% |
Who Wins Where?
At-a-glance decision frameworkEvaluations ongoing
Empirical Head-to-Head Benchmark Scores
Official evaluation metrics verified by independent third-party research labs.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Contamination-free competitive programming problems collected continuously over time.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Tokens emitted per second during active generation streaming.
Latency & Response Time
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Real-Time Interactive Token Cost Simulator
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Gemini Embedding 001 · Strengths & Key Advantages
- High MTEB retrieval leaderboard ranking
- Low cost per million tokens
- Fast vector generation
Considerations & Trade-offs
- 2K context window limit
- Text-only — no visual embeddings
Llama 5 · Strengths & Key Advantages
- 5 Million token context with native multi-audio/video understanding
- Recursive self-improvement training loop
- Completely open weights with enterprise commercial license
Considerations & Trade-offs
- Complex MoE serving requirements
- 4B tokenizer changes require updating legacy fine-tuning harnesses
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Gemini Embedding 001 | Llama 5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.05 | $0.00 |
Output Tokens / Task Tokens generated per task | 0K | 40K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 20K |
Technical Specifications
Architecture, release timeline, capabilities, and modalities.
| Feature | Gemini Embedding 001 | Llama 5 |
|---|---|---|
| Official API Model ID (SDK) | models/gemini-embedding-001 | meta-llama/Llama-5 |
| API Protocol | Google Gemini API (generateContent) + OpenAI-Compatible SDK | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2025-05 | 2026-04 |
| Knowledge Cutoff | March 2025 | January 2026 |
| Architecture | Dense semantic text embedding transformer | Mixture-of-Experts with recursive self-improvement (~600B total, 40-80B active) |
| Parameters | Confidential | 600B+ total (~40-80B active) |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud |
| Long-Context Tier (>128K/200K) | Flat rate ($0.15/1M in) across full 2K window | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Implicit + Explicit Context Caching at $0/1M with configurable TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) |
| Computer Use / GUI Agent | Text-only completion (No GUI action loop) | Supported (Screen coordinate grounding, browser & desktop GUI action loops) |
| MCP & Parallel Tool Calling | Prompt-based JSON extraction only | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema |
| Web Search Grounding & Sandbox | Built-in Google Search Grounding + Managed Python Code Execution Sandbox | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | Vertex AI Supervised Adapter Tuning & Context Distillation | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) |
| Multimodal Token Billing | Text BPE (~4 chars/token) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec) |
| Quantization & Min GPU VRAM | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 372 GB VRAM (4-bit Quantized) · 1290 GB VRAM (Native BF16) |
| Safety & Frontier Classification | Google DeepMind Frontier Safety Framework Verified | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text | text, vision, video, audio |
| Output Modalities | embedding | text |
Gemini Embedding 001 vs Llama 5: Quick Answers
Which is more intelligent, Gemini Embedding 001 or Llama 5?
Llama 5 is more intelligent. It scores 94 compared with 70 on the composite Intelligence Index.
Which model is faster?
Gemini Embedding 001 is faster, generating 400 tokens per second compared with 65 tokens per second.
Which model is cheaper to run?
Llama 5 is cheaper at $0.00 per 1M tokens blended, compared with $0.03 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Gemini Embedding 001 has lower latency, with a time to first token of 0.15s compared with 0.85s.
Which model has a larger context window?
Llama 5 has a larger context window, supporting 5M tokens compared with 2K tokens.
Verdict
Choose Gemini Embedding 001 for enterprise vector search, document indexing, and semantic clustering, or choose Llama 5 for autonomous open-source multi-agent systems, deep software refactoring, and frontier research.
Frequently asked questions
Which is cheaper: Gemini Embedding 001 or Llama 5?+
Gemini Embedding 001 costs $0.15/1M input and $0/1M output tokens, while Llama 5 costs $0/1M input and $0/1M output tokens.
Which model has a larger context window: Gemini Embedding 001 or Llama 5?+
Gemini Embedding 001 supports 2K tokens whereas Llama 5 supports 5M tokens.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.
Sources: Google & Meta Official Documentation