- Text Generation
Wan 2.7 is Alibaba’s latest open-source multimodal visual generation model for high-quality video and image creation, offering text-to-video, image-to-video, text-to-image, and editing in a single architecture.
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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.
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 Gemma 4 26B A4B?
Gemma 4 26B A4B is a Google multimodal Mixture-of-Experts language model with 26B parameters (about 3.8B active per token) and a context window of roughly 256K tokens. It is mainly used for advanced reasoning, coding, and agentic workflows where long-context understanding and structured tool/function calling are needed. It is also used for multimodal applications that take text and images as input while generating text output across many languages. Gemma 4 26B A4B belongs to the Gemma 4 open-weight model family, alongside smaller edge-focused E2B/E4B variants and larger dense 31B and unified 12B models.
Model capabilities
5 Core Capabilities
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Conversational Chat
Engages in multi-turn, instruction-following dialogue, answering questions and following user directions while maintaining context and coherence.
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Code Assistance
Helps write, read, and reason about source code, suggesting corrections, explaining logic, and supporting common programming languages.
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Image Understanding
Interprets uploaded images, identifying objects, text, and visual relationships to support question answering and description tasks.
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Language Translation
Translates between major natural languages, preserving meaning and tone for general-purpose, non-specialized text content.
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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 |
| 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)
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 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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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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