Google · Open weights
DiffusionGemma
DiffusionGemma is a high-speed diffusion-based version of Gemma 4 26B A4B. It supports optional reasoning and a 262,144-token context window. Temperature, min-p, seed, minimum output tokens, logit bias, bad words, and allowed token IDs are unavailable; omit these parameters to use native diffusion sampling.
- Reasoning
- Input
- Text
- Output
- Text
- Reasoning effort
- none · xhigh
- Discovery tier
- Open weights
Pricing
USD per 1M tokens- Input
- $0.05
- Output
- $0.15
- Cache read
- $0.025
Reference prices as published by the model provider. API example
Specifications
- Context window
- 262.1K
- Max output
- 32.8K
- Average output (7 days)
- 266 tokens
- Longer than 28% of 473 models
Side by side
DiffusionGemma compared
Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.
| DiffusionGemmaGoogleThis model | Llama 4 MaverickMetaRemove | |
|---|---|---|
| Provider | Meta | |
| Discovery tier | Open weights | Open weights |
| Added | Sep 19, 2026 | Sep 5, 2025 |
| Limits | ||
| Context window | 262.14K | 1.05MBest |
| Max output | 32.77K | 65.54KBest |
| Average output | 266 tokens | 114 tokens |
| Pricing, USD per 1M tokens | ||
| Input | $0.05Best | $0.19 |
| Output | $0.15Best | $0.65 |
| Cache read | $0.025Best | $0.05 |
| Performance | ||
| Request success (24h) | — | 99.6% |
| Benchmark indexes | ||
| Intelligence Index | 9.5Best | 9.3 |
| Coding Index | 19.7Best | 16.3 |
| Math Index | — | 19.3 |
| Capabilities | ||
| Reasoning | Yes | No |
| Tools | No | Yes |
| Vision | No | Yes |
| Structured output | No | Yes |
| Reasoning effort | none, xhigh | — |
Public evaluations
Benchmarks
Independent evaluation results, with how each score ranks among the models in the Noumen catalog.
- DiffusionGemma
- Llama 4 Maverick
Intelligence Index
- DiffusionGemma9.5
- Llama 4 Maverick9.3
DiffusionGemma: Better than 19% of 326 scored models
Indexes
Coding Index
Composite of the coding evaluations
- DiffusionGemma: 19.7Top
- Llama 4 Maverick: 16.3
Math Index
Composite of the math evaluations
- DiffusionGemma: —
- Llama 4 Maverick: 19.3
Agentic work
AutomationBench-AA
Workflow automation, penalized for guardrail violations
- DiffusionGemma: —
- Llama 4 Maverick: 0.2%
AutomationBench-AA tasks completed
Workflows finished end to end without a guardrail violation
- DiffusionGemma: —
- Llama 4 Maverick: 0.0%
GDPval-AAv2.1
Economically valuable occupational tasks, rated head to head
- DiffusionGemma: 321 Elo
- Llama 4 Maverick: —
Document reasoning
GDP.pdf
Professional reasoning over PDF documents, all-pass rate
- DiffusionGemma: —
- Llama 4 Maverick: 0.8%
AA-LCRv1.1
Reasoning across long documents
- DiffusionGemma: 19.7%
- Llama 4 Maverick: 50.0%Top
Reasoning and knowledge
Humanity’s Last Exam
Expert-level questions across academic fields
- DiffusionGemma: 10.8%Top
- Llama 4 Maverick: 4.9%
GPQA Diamond
Graduate-level science questions
- DiffusionGemma: 66.9%
- Llama 4 Maverick: 67.1%Top
MMLU-Pro
Multi-subject knowledge and reasoning
- DiffusionGemma: —
- Llama 4 Maverick: 80.9%
IFBench
Following precise output instructions
- DiffusionGemma: 59.5%Top
- Llama 4 Maverick: 43.0%
Coding
Terminal-Benchv4.0
Practical software engineering in a terminal
- DiffusionGemma: —
- Llama 4 Maverick: 0.0%
SciCode
Scientific computing in Python
- DiffusionGemma: —
- Llama 4 Maverick: 31.7%
LiveCodeBench
Recent competitive programming problems
- DiffusionGemma: —
- Llama 4 Maverick: 39.7%
Math
AIME 2025
Competition mathematics, 2025 problems
- DiffusionGemma: —
- Llama 4 Maverick: 19.3%
AIME
Competition mathematics
- DiffusionGemma: —
- Llama 4 Maverick: 39.0%
MATH-500
Competition math problems
- DiffusionGemma: —
- Llama 4 Maverick: 88.9%
Legacy evaluations
Terminal-Bench Hard
Earlier terminal task suite
- DiffusionGemma: —
- Llama 4 Maverick: 6.8%
τ²-Bench Telecom
Tool use in a customer-support simulation
- DiffusionGemma: —
- Llama 4 Maverick: 17.8%
AA-LCR (unversioned)
Long-context reasoning, earlier grading
- DiffusionGemma: 19.7%
- Llama 4 Maverick: 50.0%Top
Scores from Artificial Analysis, updated Oct 26, 2026. Artificial Analysis
Live routing data
Performance and reliability
Recent averages across requests routed for this model. Your own speed depends on prompt length, reasoning effort and load.
- Prompt cache hits
- 0.0%
- About $0.05 per 1M input tokens with caching
Cache hits are the share of input tokens recently served from a prompt cache across all requests. They are not a forecast of your own hit rate.
Developers
Use it through the API
Noumen’s API is OpenAI-compatible: any Chat Completions client works with a base URL and a key.
curl https://www.noumen.xyz/api/v1/chat/completions \
-H "Authorization: Bearer $NOUMEN_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "google/diffusiongemma",
"reasoning_effort": "none",
"messages": [{ "role": "user", "content": "Explain this stack trace." }]
}'import os
from openai import OpenAI
client = OpenAI(base_url="https://www.noumen.xyz/api/v1", api_key=os.environ["NOUMEN_API_KEY"])
reply = client.chat.completions.create(
model="google/diffusiongemma",
messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)Related models
Other Google models, starting with the same family.