Gemma 4 26B A4B benchmarks
Gemma 4 26B A4B has 9 published scores in this dataset, from a Google release dated 2026-04-02. Its composite index is 72.9, which places it 19th of 22 ranked models and 14th among 15 open-weight models. Its strongest area is knowledge (index 89.3) and its weakest is agentic (index 56.6). Its best placing is on LMArena Text Elo, where its 1438 ranks 17th of 21 models with a published score.
You can self-deploy Gemma 4 26B A4B on a dedicated GPU with OpenLLM Buddy and call it through an OpenAI-compatible API.
Category profile
Index 0–100: each score relative to the best published score on that test.
Model facts
- Lab
- Type
- OpenLLM Buddy catalog
- Released
- 2026-04-02
- Parameters
- 25.2B total, 3.8B active (MoE)
- Context
- 256K
- License
- Apache-2.0
Gemma 4 26B A4B hardware requirements and cost
At 4-bit it needs about 17 GB, which fits a single RTX 4090, so it can run on a home workstation with llama.cpp, Ollama, or vLLM. Full BF16 precision needs about 58 GB (1× H100 (80 GB)).
| Precision | Weights | With headroom | Smallest setup |
|---|---|---|---|
| BF16 (full precision) | 50 GB | 58 GB | 1× H100 (80 GB) |
| FP8 / 8-bit | 25 GB | 29 GB | RTX 5090 (32 GB) |
| 4-bit (GGUF Q4_K_M) | 15 GB | 17 GB | RTX 4090 (24 GB) |
Estimate: parameters × bytes per parameter, plus 15% for the KV cache and runtime at modest context lengths. Long contexts and large batches need more.
On OpenLLM Buddy: Gemma 4 26B A4B, with an OpenAI-compatible API and no per-token fees. Prices start at $13 USD for 11 Hours.
- 11 Hours: $13 USD
- 24 Hours: $28 USD
- 1 Week: $192 USD
- 1 Month: $767 USD
Lighter or stronger open models
Open models ranked just above and below on the composite index, with their 4-bit memory needs.
- Gemma 4 31B79.221 GBcompare
- Step 5 Preview78.7414 GB
- Nemotron 3 Ultra74.4379 GB
- gpt-oss-120b57.381 GBcompare
Gemma 4 26B A4B speed and pricing
Measured figures link to their source; estimates are labelled.
- API output speed
- 113 tok/sArtificial Analysis median · Artificial Analysis
- Time to first token
- 0.53 sOpenRouter model page provider stats
- API price per 1M tokens
- $0.150 in · $0.600 outlab's own endpoint on OpenRouter · OpenRouter endpoints API
- On your own RTX 4090 (4-bit)estimate
- 133–160 tok/s
Estimate for one user on one GPU: active weights read per token ÷ memory bandwidth at 50–70% efficiency, plus 3 ms overhead per token. Real speed varies with engine, context length and quantization; serving several users at once raises total throughput.
Score by test
Rank is among every model with a published score on that test.
Nearby in the composite ranking
Models with the closest composite index.
Head-to-head comparisons
Side-by-side pages built from the tests both models report.
Sources
| Model | Test | Score | Setting | Source |
|---|---|---|---|---|
| Gemma 4 26B A4B | GPQA Diamond | 82.3 | — | Gemma 4 31B IT model card (Hugging Face), 26B A4B column |
| Gemma 4 26B A4B | Humanity's Last Exam | 8.7 | no tools | Gemma 4 31B IT model card (Hugging Face), 26B A4B column |
| Gemma 4 26B A4B | Humanity's Last Exam (with tools) | 17.2 | with search | Gemma 4 31B IT model card (Hugging Face), 26B A4B column |
| Gemma 4 26B A4B | AIME | 88.3 | AIME 2026, no tools | Gemma 4 31B IT model card (Hugging Face), 26B A4B column |
| Gemma 4 26B A4B | LiveCodeBench | 77.1 | LiveCodeBench v6 | Gemma 4 31B IT model card (Hugging Face), 26B A4B column |
| Gemma 4 26B A4B | τ²-bench | 68.2 | avg. over 3 domains | Gemma 4 31B IT model card (Hugging Face), 26B A4B column |
| Gemma 4 26B A4B | MMLU-Pro | 82.6 | — | Gemma 4 31B IT model card (Hugging Face), 26B A4B column |
| Gemma 4 26B A4B | MMMU-Pro | 73.8 | MMMU-Pro | Gemma 4 31B IT model card (Hugging Face), 26B A4B column |
| Gemma 4 26B A4B | LMArena Text Elo | 1438 | LMArena text overall, 'Gemma 4 26B A4B', ±7, as of 2026-10-27 | Arena (LMArena) text leaderboard |
Scores are published results from model cards, lab reports, and public leaderboards, each linked to its source. Settings such as thinking mode or test version differ between sources and are shown next to each score. OpenLLM Buddy catalog models are uncensored fine-tunes; their scores are those of the official base model. Data collected 2026-10-30. How the index works