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Gemma 4 26B A4B (free)
- Instruction Following
Gemma 4 26B A4B (free) is a 26-billion-parameter variant in Google’s Gemma 4 family, offered with an A4B quantization profile for more efficient inference. It is accessible for free use, targeting capable reasoning and generation while reducing hardware requirements.
About the model
What is Gemma 4 26B A4B (free)?
Gemma 4 26B A4B (free) is a quantized 26B-parameter large language model from Google’s Gemma 4 series, optimized for efficient deployment. It is mainly used for general-purpose chat, code assistance, and text generation tasks where a strong medium‑sized model is suitable. It also supports applications such as prototyping AI agents, educational tools, and lightweight research workflows on constrained compute. It belongs to the Gemma model family, which follows earlier Gemma generations designed as open, efficient LLMs from Google.
Model capabilities
5 Core Capabilities
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Conversational Chat
Handles multi-turn conversations, follows instructions, and maintains context to provide coherent, helpful responses on many general topics.
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Text Understanding
Understands long-form text, summarizes content, extracts key information, and answers questions based on provided documents or prompts.
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Code Assistance
Helps write, explain, and refactor code snippets in popular programming languages, aiding debugging and implementation of common patterns.
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Language Translation
Translates between major natural languages and explains wording choices, tone, and nuances while preserving meaning and style.
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Image Understanding
Analyzes uploaded images, describing scenes and objects, reading visible text, and supporting reasoning about visual content when available.
Use cases
6 Most Valuable Use Cases
- Customer Support Chatbot
- Invoice Data Extraction
- Legal Document Search
- Contract Change Monitoring
- E-commerce Product Assistant
- Code Generation Helper
Transparent pricing
Cost Comparison
LLM API offers the lowest cost per 1M tokens and fastest Gemma 4–class inference.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | 120ms | 120 tps | 99.99% | $0.02 | $0.04 | 256K |
| Global | ~220ms | ~70 tps | 99.9% | $0.00 | $0.00 | ~128K | |
| Vertex AI (Google Cloud) | US East | ~260ms | ~60 tps | 99.9% | ~$0.20 | ~$0.40 | ~128K |
| Together AI | US West | ~240ms | ~80 tps | 99.9% | ~$0.15 | ~$0.30 | ~64K |
| Groq | US Central | ~150ms | ~100 tps | 99.9% | ~$0.10 | ~$0.20 | ~32K |
Performance benchmarks
Technical Specifications
| Metric | Gemma 4 26B A4B (free) | Gemini 2.0 Flash (Google) | GPT-4.1 Mini (OpenAI) |
|---|---|---|---|
| Avg Latency | ~220ms | ~180ms | ~200ms |
| Context Window | 128K | 128K | 128K |
| Input Price ($/1M) | $0.00 | $0.20 | $0.15 |
| Output Price ($/1M) | $0.00 | $0.60 | $0.60 |
| Max Output Tokens | 4K | 4K | 4K |
| Throughput | ~80 tps | ~100 tps | ~90 tps |
| Uptime | 99.5% | 99.9% | 99.9% |
30-day usage via LLM API
- 62.5B
- Prompt tokens processed (last 30 days)
- 9.3M
- API requests served (last 30 days)
- 74.1B
- Completion tokens generated (last 30 days)
- 99.9%
- Average uptime
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
Automatically route each request to the optimal model across providers based on latency, cost, and capability—without changing your application code or integration logic.
One endpoint, any model -
Cost-Aware Optimization
Control spend with configurable pricing policies, transparent per-request cost breakdowns, and smart routing that prefers cheaper equivalents when quality and latency requirements are met.
Lower cost, same output -
Resilient Fallbacks
Define automatic provider and model fallbacks so production traffic keeps flowing through alternative backends when a model, region, or vendor has performance or availability issues.
No single point of failure -
Deep Observability
Get end-to-end traces, metrics, and structured logs for every call, enabling fast debugging, performance tuning, and regression detection across all models and providers.
See every token, everywhere -
Task-Level Orchestration
Declare tasks like chat, tools, rerank, or embeddings once, and let LLM.API handle provider-specific quirks, payload shaping, and response normalization automatically.
One task spec, many models -
High-Throughput Batch
Run large-scale jobs with parallelized, rate-limit-aware batching, automatic retries, and progress tracking—maximizing throughput while protecting upstream providers and your application.
Ship millions of calls safely
Decision guide
When to Use — When NOT to Use
Use it if...
- You need a free, general-purpose LLM for prototypes, demos, or hackathon projects.
- You need decent quality English chat, explanations, and Q&A without strict enterprise guarantees.
- Your use case involves moderate-length content generation like emails, blogs, or marketing copy.
- Your use case involves educational assistants that explain concepts, summarize notes, or draft exercises.
- You need an inexpensive backup model for fallbacks when paid models hit limits.
- Your use case involves lightweight internal tools where occasional errors are acceptable.
- You need a model for experimentation with prompt engineering and basic reasoning tasks.
Avoid if...
- You need state-of-the-art reasoning performance comparable to the strongest proprietary frontier models.
- Your workload requires strict latency, uptime, and support SLAs for production-critical systems.
- You need robust handling of very long contexts, large documents, or complex multi-hop reasoning.
- Your workload requires strong, externally audited safety, compliance, and data-governance guarantees.
- You need advanced multimodal capabilities like high-quality image generation or complex vision reasoning.
- Your workload requires domain-optimized performance in law, medicine, or high-stakes decision-making.
- You need fine-grained control over model versions, changelogs, and long-term backward compatibility.
FAQ
Frequently Asked Questions
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What is Gemma 4 26B A4B (free)?
Gemma 4 26B A4B (free) is a 26B-parameter Google Gemma 4 language model variant accessible via LLM.API at no usage cost.
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What is Gemma 4 26B A4B (free) best suited for?
It is best for high-quality general text generation, reasoning, and coding assistance when you want a strong model without incurring API charges.
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How is Gemma 4 26B A4B (free) priced on LLM.API?
The model is offered with a $0 per-token price, subject to LLM.API’s free-tier rate limits and fair-use policies.
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What context window does Gemma 4 26B A4B (free) support?
Gemma 4 26B A4B (free) supports a 32K token context window for combined prompt and completion.
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How fast is Gemma 4 26B A4B (free) on LLM.API?
Latency is moderate, typically slower than smaller models but acceptable for interactive use, depending on request size and current platform load.
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Which modalities does Gemma 4 26B A4B (free) support?
Gemma 4 26B A4B (free) is a text-only model, accepting and producing UTF-8 text but not images, audio, or video.
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How do I call Gemma 4 26B A4B (free) through LLM.API?
Use the LLM.API chat or completion endpoint with the provider set to "google" and the model name set exactly to "Gemma 4 26B A4B (free)".
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How does Gemma 4 26B A4B (free) compare to similar-sized paid models?
It offers competitive quality for many tasks but may trail frontier paid models in complex reasoning, instruction-following robustness, and safety tuning.
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What are the main limitations of Gemma 4 26B A4B (free)?
It can hallucinate facts, lacks real-time knowledge, is text-only, and may be subject to strict rate limits due to its free status.
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Are there any usage limits for Gemma 4 26B A4B (free) on LLM.API?
Yes, LLM.API enforces per-minute and daily rate limits for the free model that may throttle or reject excessive traffic.