- Instruction Following
MiniMax M2.5 (free) is a third-generation, open-source agentic large language model from MiniMax, offered via multiple providers with free usage tiers. It is notable for its…
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Google Gemini Flash Latest
- Instruction Following
Google Gemini Flash Latest is a fast, cost‑optimized variant of Google’s Gemini family, designed to deliver high-throughput, low-latency multimodal reasoning for everyday and agent-style workloads. It emphasizes speed and efficiency over maximum raw capability while retaining strong text, code, and media understanding.
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 Flash Latest?
Google Gemini Flash Latest is a frontier multimodal large language model variant from Google’s Gemini lineup, tuned for very low latency and high request volume. It is mainly used for real-time applications such as chatbots, assistants, and AI agents that must respond quickly while handling complex text and code tasks. It is also used for scalable workloads like bulk content generation, summarization, and lightweight multimodal understanding where cost per token is critical. It belongs to the Gemini model family developed by Google DeepMind, which includes Pro, Flash, Flash-Lite, image, audio, and other specialized variants across multiple generations.
Model capabilities
5 Core Capabilities
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Multimodal Reasoning
Processes and reasons over mixed inputs like text and images, supporting tasks such as explanation, classification, and grounded question answering.
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Conversational Chat
Engages in multi-turn dialogue, following instructions, maintaining context, and generating coherent, natural language responses across diverse topics.
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Image Understanding
Interprets images by identifying objects, reading charts, and explaining visual scenes, useful for analysis, descriptions, and Q&A.
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Text Translation
Translates between multiple languages, preserving meaning and tone, suitable for everyday communication and content localization tasks.
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Visual Text Extraction
Performs optical character recognition on images or documents, extracting machine-readable text for search, editing, or downstream processing.
Use cases
6 Most Valuable Use Cases
- Customer Support Chatbots
- Invoice Data Extraction
- Legal Document Search
- Regulation Change Monitoring
- E-commerce Product Insights
- Low-latency AI Inference
Transparent pricing
Cost Comparison
LLM API offers the lowest cost and latency for Gemini Flash–class models across providers.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | 80ms | 120 tps | 99.99% | $0.05 | $0.10 | 256K |
| Global | ~150ms | ~60 tps | 99.9% | ~$0.10 | ~$0.20 | 128K | |
| OpenRouter | Global | ~220ms | ~40 tps | ~99.5% | ~$0.14 | ~$0.28 | ~128K |
| Fireworks AI | US East | ~180ms | ~70 tps | ~99.9% | ~$0.11 | ~$0.22 | ~128K |
Performance benchmarks
Technical Specifications
| Metric | Google Gemini Flash Latest | OpenAI GPT-4o-mini | Anthropic Claude 3 Haiku |
|---|---|---|---|
| Avg Latency | ~180ms | ~200ms | ~220ms |
| Context Window | 128K | 128K | 200K |
| Input Price ($/1M) | $0.05 | $0.15 | $0.25 |
| Output Price ($/1M) | $0.15 | $0.60 | $1.25 |
| Max Output Tokens | 8K | 4K | 4K |
| Throughput | 80 tps | 60 tps | 50 tps |
| Uptime | 99.9% | 99.9% | 99.9% |
30-day usage via LLM API
- 11.5B
- Prompt tokens processed (30 days)
- 2.8B
- Completion tokens generated (30 days)
- 24.3M
- API requests served (30 days)
- 99.9%
- Average API uptime (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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Intelligent Model Routing
Dynamically route each request to the optimal model and provider using rules or performance data, so you keep shipping features instead of maintaining glue code.
One endpoint, any model -
Cost-Aware Orchestration
Automatically balance cost and quality across providers with per-route policies and real-time price data, keeping your LLM bill predictable as usage scales.
Control spend, not output -
Resilient Fallback Flows
Define provider and model failover chains so requests survive outages and rate limits, without custom retry logic scattered across services.
No single point of failure -
End-to-End Observability
Trace every call across providers with unified logs, latency and error metrics, plus payload sampling, so you can debug and tune LLM workloads in one place.
See every token hop -
Task-Level Abstractions
Describe tasks like chat, tools, RAG or classification once and let LLM.API handle prompts, schemas and providers, keeping your app logic clean and portable.
Code to tasks, not models -
High-Throughput Batch APIs
Send massive batches of prompts or embeddings through a single job with automatic chunking, retries and aggregation to maximize throughput and minimize overhead.
Ship thousands at once
Decision guide
When to Use — When NOT to Use
Use it if...
- You need fast, low-cost responses for high-volume chatbots or simple assistants.
- Your use case involves lightweight data extraction or classification from short texts at scale.
- You need quick iterative prompting during development where latency and cost matter most.
- Your use case involves simple image understanding, like describing images or detecting basic elements.
- You need to fan out many parallel calls for A/B testing or tool orchestration.
- Your use case involves summarizing short documents, emails, or tickets in bulk.
Avoid if...
- You need the very strongest reasoning capabilities for complex multi-step planning or coding.
- Your workload requires handling very long contexts with high fidelity and deep analysis.
- You need best-in-class performance on nuanced coding tasks across large, complex repositories.
- Your workload requires highly reliable, expert-level answers on specialized legal or medical topics.
- You need state-of-the-art multimodal reasoning over complex documents, diagrams, or technical images.
- Your workload requires maximizing output quality where cost and latency are less constrained.
FAQ
Frequently Asked Questions
-
What is Google Gemini Flash Latest?
Google Gemini Flash Latest is a lightweight, production-oriented Gemini model from ~Google optimized for fast, low-cost multimodal inference.
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What is Google Gemini Flash Latest best suited for?
Google Gemini Flash Latest is best for latency-sensitive, high-throughput workloads like chatbots, streaming agents, and simple vision or document understanding tasks.
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What is the context window of Google Gemini Flash Latest?
Google Gemini Flash Latest supports a context window of up to 1 million tokens, enabling very long conversations and large document inputs.
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How fast is Google Gemini Flash Latest on LLM.API?
On LLM.API, Google Gemini Flash Latest is tuned for low latency, usually returning first tokens within a few hundred milliseconds depending on load.
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What modalities does Google Gemini Flash Latest support via LLM.API?
Through LLM.API, Google Gemini Flash Latest supports text input and output plus vision inputs such as images and document snapshots.
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How is Google Gemini Flash Latest priced on LLM.API?
LLM.API applies its own per-token pricing for Google Gemini Flash Latest, typically cheaper than heavier Gemini Pro models; check the LLM.API pricing page.
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How do I call Google Gemini Flash Latest through the LLM.API gateway?
Select the provider '~Google' and model name 'Google Gemini Flash Latest' in your LLM.API request, then send standard OpenAI-compatible chat completion payloads.
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How does Google Gemini Flash Latest compare to larger Gemini models?
Compared to larger Gemini models, Gemini Flash Latest trades some reasoning depth and accuracy for significantly lower latency and cost.
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What are the main limitations of Google Gemini Flash Latest?
Google Gemini Flash Latest can struggle with complex multi-step reasoning, highly specialized domain knowledge, and tasks requiring the highest factual accuracy.
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Can I use Google Gemini Flash Latest for image understanding and captioning?
Yes, Google Gemini Flash Latest supports image inputs and can generate descriptions, classifications, and basic reasoning about visual content.
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