- Text Generation
Embed V1 0.6B is Perplexity’s 0.6‑billion‑parameter text embedding model designed for fast, low‑latency, web‑scale retrieval. It produces compact INT8 or binary embeddings optimized for dense semantic…
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Google Gemini Pro Latest
- Text Generation
Google Gemini Pro Latest is the most recent Pro-tier model in Google’s Gemini family of large multimodal models, optimized for complex reasoning and agentic tasks across text and other modalities.
Providers Comparison
Speed
Output tokens per second · Higher is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
End-to-End Response Time
Seconds · Lower is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
Price
USD per 1M tokens (blended) · Lower is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
About the model
What is Google Gemini Pro Latest?
Google Gemini Pro Latest is a high-performance Pro-tier variant of Google’s Gemini multimodal large language models that is exposed to users and developers as the current Pro default in Gemini products and APIs. It is primarily used for advanced reasoning over long contexts, complex coding and data analysis, and orchestrating multi-step workflows and AI agents across Google’s ecosystem. It is also used in enterprise and developer platforms such as the Gemini app, Google AI Studio, and Vertex AI to power assistants, productivity tools, and custom applications. It belongs to Google’s Gemini model family, whose Pro line succeeds earlier Gemini Pro generations and sits between lightweight Flash models and more specialized or larger-capacity variants.
Model capabilities
5 Core Capabilities
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Conversational AI
Engages in multi-turn, context-aware dialogue, answering questions, following instructions, and adjusting tone based on user prompts.
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Image Understanding
Interprets images to identify objects, scenes, text, and relationships, supporting descriptive captions and visual question answering tasks.
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Code Assistance
Generates, explains, and refactors code in multiple programming languages, helping with debugging, documentation, and implementation details.
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Language Translation
Translates between multiple natural languages while preserving meaning, tone, and key context across a broad range of topics.
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Visual Text Extraction
Extracts and structures text from images or scanned documents, supporting downstream search, summarization, and information retrieval workflows.
Use cases
6 Most Valuable Use Cases
- Customer Support Chatbots
- Invoice Data Extraction
- Legal Document Search
- Regulation Change Monitoring
- Marketing Content Generation
- Code Generation Assistance
Transparent pricing
Cost Comparison
LLM API offers the lowest cost and latency for Gemini Pro–class models.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | 140ms | 120 tps | 99.99% | $0.05 | $0.10 | 256K tokens |
| Google AI Studio | Global | ~220ms | ~60 tps | ~99.9% | ~$0.10 | ~$0.20 | 128K tokens |
| Google Vertex AI | US & EU | ~260ms | ~40 tps | 99.9% | ~$0.12 | ~$0.24 | 128K tokens |
| OpenRouter (Gemini-equivalent) | Global | ~280ms | ~35 tps | ~99.5% | ~$0.14 | ~$0.28 | ~64K tokens |
| Third-Party Reseller (Gemini proxy) | Global | ~320ms | ~25 tps | ~99.0% | ~$0.16 | ~$0.32 | ~32K tokens |
Performance benchmarks
Technical Specifications
| Metric | Google Gemini Pro Latest | OpenAI GPT-4.1 | Anthropic Claude 3.5 Sonnet |
|---|---|---|---|
| Avg Latency | ~220ms | ~250ms | ~260ms |
| Context Window | 128K | 128K | 200K |
| Input Price ($/1M) | $0.25 | $5.00 | $3.00 |
| Output Price ($/1M) | $0.75 | $15.00 | $15.00 |
| Max Output Tokens | 4K | 4K | 4K |
| Throughput | 80 tps | 60 tps | 50 tps |
| Uptime | 99.9% | 99.9% | 99.9% |
30-day usage via LLM API
- 11.8B
- Prompt tokens processed (last 30 days)
- 36.5M
- Completion tokens generated (last 30 days)
- 4.1M
- API requests served (last 30 days)
- 99.8%
- Avg API uptime (last 30 days)
Architecture & Integration
Why Build on LLM.API?
One unified API. Every major model. Built-in reliability, cost control, and observability.
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Unified AI Routing
Dynamically route each request to the best model across providers using policies and performance data, without changing your app logic or wiring new SDKs.
One endpoint, every model -
Cost-Aware Orchestration
Define cost policies once and let LLM.API automatically choose cheaper equivalents, downscale for non-critical paths, and prevent runaway bills with global spend controls.
Optimize cost by default -
Resilient Fallbacks
Configure cross-provider fallbacks and retries so requests transparently fail over to healthy models, eliminating single-vendor outages without extra error-handling code.
No single point of failure -
Deep Observability
Get centralized traces, latency and cost metrics, and per-model success rates for every request, so you can debug regressions and tune routing with real production data.
See every token, everywhere -
Task-Centric Abstractions
Use high-level tasks like chat, tools, or embeddings instead of vendor-specific APIs, enabling you to swap models without rewriting business logic or prompt plumbing.
Code to tasks, not vendors -
High-Throughput Batch
Submit large batches across providers via a single API with automatic chunking, concurrency control, and retries to maximize throughput while staying within rate limits.
Scale up without throttling
Decision guide
When to Use — When NOT to Use
Use it if...
- You need a general-purpose, cloud-hosted LLM for chatbots and virtual assistants.
- You need strong multimodal support, combining text and images in a single workflow.
- Your use case involves integrating tightly with other Google Cloud services and tooling.
- You need good performance on everyday coding assistance, code explanation, and refactoring tasks.
- Your use case involves multilingual understanding and translation across many major world languages.
- You need a managed, scalable API with usage-based billing and enterprise-grade reliability.
Avoid if...
- You need guaranteed state-of-the-art reasoning performance comparable to the very best frontier models.
- Your workload requires fully on-premise deployment with no dependence on external cloud services.
- You need ultra-long context handling far beyond typical limits for book-length documents.
- Your workload requires deterministic, reproducible outputs with strict version pinning and auditability.
- You need deeply specialized domain models, such as certified medical or legal reasoning.
- Your workload requires full transparency into training data sources and fine-grained data residency guarantees.
FAQ
Frequently Asked Questions
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What is Google Gemini Pro Latest?
Google Gemini Pro Latest is a large language model from ~Google, accessible via LLM.API, optimized for versatile general-purpose reasoning and coding tasks.
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What is the context window of Google Gemini Pro Latest?
Google Gemini Pro Latest supports context windows up to approximately 32K tokens, suitable for long conversations, multi-file codebases, and extended documents.
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What modalities does Google Gemini Pro Latest support through LLM.API?
Through LLM.API, Google Gemini Pro Latest primarily supports text input and output, with image or other modalities depending on LLM.API’s enabled features and routing.
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How is Google Gemini Pro Latest priced on LLM.API?
Pricing for Google Gemini Pro Latest is set by LLM.API, typically on a per-input-token and per-output-token basis; check the LLM.API pricing page for current rates.
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How fast is Google Gemini Pro Latest in terms of latency?
Google Gemini Pro Latest generally returns first tokens within a few hundred milliseconds to a couple of seconds, depending on prompt length and concurrent load.
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What is Google Gemini Pro Latest best suited for?
Google Gemini Pro Latest is best suited for complex reasoning, code generation, data analysis, and high-quality natural language interactions across a broad range of domains.
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How do I call Google Gemini Pro Latest via the LLM.API?
You call Google Gemini Pro Latest by selecting its model name in your LLM.API request payload, using the same unified endpoint as other models.
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How does Google Gemini Pro Latest compare to similar models on LLM.API?
Google Gemini Pro Latest typically offers strong reasoning and coding performance comparable to other top-tier frontier models, with competitive cost and latency profiles.
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Does Google Gemini Pro Latest support streaming responses on LLM.API?
Yes, Google Gemini Pro Latest can stream tokens incrementally when you enable streaming mode in your LLM.API request.
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What are the main limitations of Google Gemini Pro Latest?
Google Gemini Pro Latest can hallucinate incorrect facts, lacks real-time external knowledge without tools, and may struggle with highly specialized or ambiguous instructions.
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Can I use Google Gemini Pro Latest for production workloads?
Yes, Google Gemini Pro Latest is suitable for production workloads, but you should implement monitoring, rate limiting, guardrails, and human review for critical outputs.
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