Gemma 4 26B A4B IT

This Simplismart deployment-focused article targets the Gemma 4 26B MoE variant by name, framing it as delivering near-flagship 31B-level performance at roughly 4B-parameter active inference cost. The piece discusses the MoE routing mechanism, contrasts the 26B-A4B against the 31B dense flagship across reasoning and vi As a third-party provider blog with deployment framing rather than a first-party Google announcement, the article is useful as independent corroboration of the 26B-A4B variant's active-parameter architecture and single-GPU deployability rather than as breaking news. It does not mention Cortecs and is oriented toward Si

According to a benchr.org launch record published July 28, 2026, Google released the instruction-tuned model with the exact identifier "gemma-4-26b-a4b-it" on April 2, 2026 as part of the Gemma 4 launch. The page records that this exact identifier is made available in AI Studio and via the Gemini API, and frames it as The same benchr record is explicit about what it does and does not publish: the checked release note does not include context-window limits, per-token API pricing, or benchmark results for the exact IT variant. It warns against filling those gaps from sibling Gemma models, older cards, or third-party listings, and trea

An independent deep-dive analysis published on kie.ai on July 16, 2026 examines Google's Gemma 4 family as a five-size open-weight lineup under Apache 2.0: E2B (2.3B effective), E4B (4.5B effective), 12B Unified (encoder-free multimodal), 26B-A4B MoE (25.2B total, 3.8B active per token), and 31B dense. The 12B, 26B-A4B The analysis reports that Official Arena AI text rankings place the 31B at Elo 1452 and the 26B-A4B at 1441, both above Gemma 3 27B's 1365, with the 31B scoring 80.0% on LiveCodeBench v6. Community NVFP4 quantizations from Unsloth reportedly deliver 1.5× speedup on NVIDIA Blackwell hardware, fitting the 12B into 11GB V

DeepInfra's analysis (May 25, 2026) characterizes Gemma 4 26B A4B as an open-weight Google DeepMind reasoning model with 25.2B total / 3.8B active MoE parameters, 262K context, native function calling, multimodal input, and Apache 2.0 licensing released April 3, 2026. It highlights blended provider pricing ranging from The piece positions DeepInfra on blended cost and TTFT, Clarifai on throughput and time-to-first-token, Cloudflare on output pricing and latency, and Google AI Studio on direct managed access, framing Gemma 4 as suited to cost-sensitive production, long-context assistants, and multimodal pipelines. It targets technical

Compare DeepSeek V4 Pro from DeepSeek and Gemma 4 26B A4B from Google on key metrics including benchmarks, price, context length, and other model features.

Gemma 4 26B A4B IT is an instruction-tuned Mixture-of-Experts (MoE) model from Google DeepMind. $0 per million input tokens, $0 per million output tokens. 262,144 token context window, maximum output of 32,768 tokens. Higher uptime with 12 providers. Includes independent benchmarks from Artificial Analysis.

Third-party benchmark aggregator llm-stats.com ranks Gemma 4 26B-A4B at position 117 overall with a composite LLM Stats Score of 22.3 at a blended price of $0.14 per million tokens. The model's capability-tier placement is mixed: it lands in the top half for Data Viz (15 of 64), Audio (19 of 66), Websites (41 of 83), a Per-turn quality analysis shows the model declining from 14.2 at turn 1 to 13.6 at turns 31+, with the Quality Tracker showing +0.82σ improvement in the 7-day window (213 votes) driven by gains in Chat (+1.97σ), SVG (+2.02σ), and Games (+1.96σ), while declining in Websites (-0.64σ) and Audio (-0.63σ). Cost-efficiency c

The Kilo Code model page documents Gemma 4 26B A4B as available via OpenRouter with a 262,144-token context window, 16,384 max output tokens, and input modalities spanning text, image, and video. It confirms an April 3, 2026 creation date, the Gemma tokenizer, and pricing of $0.04 per 1M input tokens and $0.22 per 1M o The page lists coding-relevant capabilities including function calling, tool choice control, structured JSON schema outputs, and reasoning tokens support, while noting content moderation is disabled. A sample cost calculation shows analyzing a ~40k-token, 10k-token-output codebase at approximately $0.0039, giving devel

OpenRouter's official comparison page pits Google's Gemma 4 26B A4B IT against its larger sibling Gemma 4 31B IT, both sharing a 262K-token context window. Gemma 4 26B A4B is positioned as the more efficient MoE option at $0.07/M input and $0.34/M output, slightly cheaper than the 31B's $0.08/$0.35, with no cached-inpu On performance metrics, the 26B A4B variant delivers substantially better latency and throughput than its 31B counterpart, with a p50 latency of 0.34s versus 1.89s and p50 throughput of 23 tok/s versus 11 tok/s. Both support tool use, stream cancellation, and reasoning variants on the Artificial Analysis intelligence/a

Compare Gemma 4 26B A4B (free) from Google to other AI models on key metrics including benchmarks, price, context length, and other model features.

OpenRouter's free-tier listing for google/gemma-4-26b-a4b-it:free documents that Gemma 4 26B A4B IT is a Google DeepMind instruction-tuned MoE model with 25.2B total parameters and only 3.8B active per token, marketed as delivering near-31B quality at lower compute cost. It supports multimodal input including text, ima The free variant is routed across providers, with OpenRouter reporting a best P50 throughput of 38 tok/s and P50 latency of 0.88s on Google AI Studio versus 11 tok/s and 2.85s on Darkbloom. Benchmark scores shown include GPQA Diamond ranging from 76.0% (DekaLLM) to 78.6% (SiliconFlow) and TAU-Bench scores of 68.4%–71.5