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Gemma 4 26B A4B

  • Text Generation

Gemma 4 26B A4B is a 26-billion-parameter multimodal Mixture-of-Experts model from Google’s Gemma 4 family, optimized for high-throughput reasoning with long context windows. It supports text and image inputs and is designed to run efficiently on modern GPUs and cloud platforms.

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About the model

What is Gemma 4 26B A4B?

Gemma 4 26B A4B is a Google DeepMind Mixture-of-Experts language model with around 26B parameters (about 3.8B active) that supports long-context multimodal understanding. It is mainly used for text and image (and in some deployments video) analysis and generation in applications such as agents, coding assistants, and knowledge-intensive chat. It is also used for enterprise workloads that need high token throughput, long-context processing (around a 256K token window), and cost-efficient inference on commodity hardware. It belongs to the Gemma 4 open-weight model family, alongside smaller E2B/E4B variants and larger 12B and 31B models.

Model capabilities

5 Core Capabilities

  • Conversational Chat

    Engages in multi-turn, instruction-following dialogue, answering questions and following user directions while maintaining context and coherence.

  • Code Assistance

    Helps write, read, and reason about source code, suggesting corrections, explaining logic, and supporting common programming languages.

  • Image Understanding

    Interprets uploaded images, identifying objects, text, and visual relationships to support question answering and description tasks.

  • Language Translation

    Translates between major natural languages, preserving meaning and tone for general-purpose, non-specialized text content.

  • Visual Text Extraction

    Extracts readable text from images, enabling downstream processing like search, summarization, or translation of visual documents.

Use cases

6 Most Valuable Use Cases

  • Customer Support Chatbots
  • Financial Document Summarization
  • Legal Knowledge Retrieval
  • Compliance Case Monitoring
  • E-commerce Product Assistance
  • Code Generation and Review

Transparent pricing

Cost Comparison

LLM API offers the lowest cost and latency for Gemma 4–class 26B models

Provider Region Latency Throughput Uptime Input ($/1M) Output ($/1M) Context
LLM API BEST Global 120ms 220 tps 99.99% $0.15 $0.15 256K
Google Global ~220ms ~150 tps 99.9% ~$0.25 ~$0.25 128K
AWS Bedrock US East ~260ms ~140 tps 99.9% ~$0.28 ~$0.28 128K
Azure AI EU West ~250ms ~130 tps 99.9% ~$0.30 ~$0.30 128K
Anthropic Partner API Global ~240ms ~160 tps 99.95% ~$0.32 ~$0.32 200K

Performance benchmarks

Technical Specifications

Metric Gemma 4 26B A4B (Google) Llama 3.1 70B (Meta) GPT-4.1 (OpenAI)
Avg Latency ~180ms ~220ms ~200ms
Context Window 128K 128K 128K
Input Price ($/1M) ~$0.30 ~$0.50 ~$5.00
Output Price ($/1M) ~$0.60 ~$0.80 ~$15.00
Max Output Tokens 4K 4K 4K
Throughput ~80 tps ~60 tps ~70 tps
Uptime 99.9% 99.9% 99.9%

30-day usage via LLM API

62B
Prompt tokens processed (last 30 days)
51B
Completion tokens generated (last 30 days)
3.6M
API requests served (last 30 days)
99.8%
Avg uptime (last 30 days)
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Architecture & Integration

Why Build on LLM.API?

One unified API. Every major model. Built-in reliability, cost control, and observability.

  • Unified AI Routing

    Automatically route each request to the best model across providers based on cost, latency, or quality—without changing your integration.

    One endpoint, every model

  • Cost-Aware Control

    Set explicit cost policies, caps, and model tiers so you never exceed budget while still unlocking premium models when they matter most.

    Predictable AI spend

  • Resilient Fallbacks

    Define automatic cross-provider fallbacks so outages or quota limits never take your AI features down—no extra client logic required.

    No single point of failure

  • Deep Observability

    Track latency, errors, tokens, and provider performance per route and project, with logs you can query directly from your existing monitoring stack.

    See every token

  • Task-Native Abstractions

    Call high-level tasks like chat, embed, rerank, and tools via a single schema while LLM.API handles provider-specific quirks under the hood.

    One schema, any task

  • High-Throughput Batching

    Batch thousands of requests across models and tasks in a single call to maximize throughput, minimize overhead, and cut per-request costs.

    Scale without bottlenecks

Decision guide

When to Use — When NOT to Use

Use it if...

  • You need a mid-size open-weight model with solid general reasoning and language capabilities.
  • You need a Google-aligned model that integrates well with Google Cloud tooling and infrastructure.
  • Your use case involves moderate-length chatbots, assistants, or agents with fluent English responses.
  • Your use case involves fine-tuning or adapting an open model for domain-specific tasks.
  • You need cost-efficient inference with better quality than small models but below frontier pricing.
  • Your use case involves experimentation with quantization-friendly models optimized for A4 GPU configurations.
  • You need an open model whose weights can be self-hosted for compliance or data residency.

Avoid if...

  • You need state-of-the-art performance comparable to Google’s largest proprietary Gemini or frontier models.
  • Your workload requires extremely long-context processing, such as entire books or multi-hour transcripts.
  • You need strong multimodal capabilities like image understanding, generation, or complex vision-language tasks.
  • Your workload requires ultra-low latency, real-time streaming responses on constrained edge hardware.
  • You need highly specialized reasoning in domains like cutting-edge science where top models excel.
  • Your workload requires enterprise-grade support SLAs that are only available for Google proprietary models.
  • You need tightly integrated product features only exposed through Gemini APIs or Google Workspace add-ons.

FAQ

Frequently Asked Questions

  • What is Gemma 4 26B A4B?

    Gemma 4 26B A4B is a 26B-parameter Google Gemma 4 language model variant optimized for low-cost, 4-bit quantized inference via LLM.API.

  • What is Gemma 4 26B A4B best suited for?

    Gemma 4 26B A4B is best for general-purpose chat, code assistance, and knowledge-intensive tasks where strong reasoning is needed at moderate cost.

  • What context window does Gemma 4 26B A4B support on LLM.API?

    Gemma 4 26B A4B supports a 32,768 token context window for combined input and output on LLM.API.

  • Does Gemma 4 26B A4B support images or other modalities?

    Gemma 4 26B A4B is text-only and currently supports neither image input nor other multimodal capabilities via LLM.API.

  • How fast is Gemma 4 26B A4B on LLM.API?

    Latency depends on load and max_tokens, but 26B A4B is tuned for faster, cheaper decoding than full-precision 26B deployments.

  • How is Gemma 4 26B A4B priced on LLM.API?

    Pricing is usage-based per 1,000 tokens, with lower rates than larger Gemma 4 models; check the LLM.API pricing page for current numbers.

  • How do I call Gemma 4 26B A4B through the LLM.API?

    Select the Gemma 4 26B A4B model ID in your LLM.API request and send standard Chat Completions-style messages with temperature and max_tokens parameters.

  • How does Gemma 4 26B A4B compare to larger Gemma models?

    Gemma 4 26B A4B generally offers lower latency and cost but slightly weaker reasoning and coding performance than larger Gemma 4 variants.

  • What are the main limitations of Gemma 4 26B A4B?

    Limitations include potential hallucinations, lack of multimodal support, and no built-in browsing or tools, so outputs should be validated for critical use.

  • Can Gemma 4 26B A4B handle long-running or streaming responses?

    Yes, Gemma 4 26B A4B supports streaming responses via LLM.API, suitable for interactive chat or partial-output UIs.

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