DiffusionGemma
nano-gpt/diffusiongemmaمن nano-gpt · أُصدِر 2026-09-19
⚠ هذا نموذج مُحسَّن من المجتمع أو مشتق — وليس إصدارًا رسميًا من المزود.
Prices in USD per 1M tokens. Unknown means the provider does not publish per-token pricing.
القدرات
Model fit scores
0–100 · higher is betterThese scores reward declared capabilities, context size, price and provider availability — they are not benchmark results. Use them as a directional signal alongside your own evaluation.
Coding19
- Tool calling0/40
- Structured output0/20
- Reasoning10/10
- Context window (100K → 1M)8/20
- Provider availability1/10
Agents31
- Tool calling0/35
- Structured output0/25
- Reasoning15/15
- Output token limit15/15
- Provider availability1/10
JSON / structured output20
- Structured output / JSON mode0/50
- Tool calling0/20
- Temperature control0/10
- Price-friendly for high-volume20/20
Cost efficiency84
- Headline price (log-scaled)79/95
- Has prompt-cache pricing5/5
Long context61
- Context window (100K → 2M)51/90
- Has published price for full window10/10
Production-readiness50
- Number of independent providers5/40
- Has published per-token price20/20
- Context window ≥ 8K15/15
- No data inconsistencies across providers10/10
- Official model (not derivative)0/15
Cost Efficiency Index
Open full calculator →Estimated cost using the recommended provider's headline rate. Each scenario fixes average input/output tokens — the assumptions are shown in the third column.
| Scenario | Cost | Assumption |
|---|---|---|
RAG answer per 1,000 RAG answers | $0.33 < $0.01 per request | 5K input tokens (query + 4 retrieved chunks of ~1K each) and a 500-token answer. Typical SaaS knowledge-base bot. |
Support ticket triage per 10,000 tickets | $0.65 < $0.01 per request | 1K input tokens (ticket body + system prompt) and a 100-token JSON classification reply. High-volume customer support. |
Data extraction per 1,000 documents | $0.17 < $0.01 per request | 2K input tokens (a single document page) and a 500-token JSON extraction. ETL / invoice / form pipelines. |
Code review per 1,000 PRs | $0.55 < $0.01 per request | 8K input tokens (diff + surrounding files) and a 1K-token review comment. PR-bot workloads. |
Agent step per 1,000 steps | $0.69 < $0.01 per request | 12K input tokens (long-running tool history) and a 600-token tool-call decision. Cost per agent step. |
تفاصيل التسعير
السعر المُوصى به من nano-gpt · google/diffusiongemma
متاح لدى 1 مزود
| المزود | معرف نموذج المزود | إدخال / 1M | إخراج / 1M | السياق | تاريخ الإصدار |
|---|---|---|---|---|---|
| NanoGPT nano-gpt | google/diffusiongemma | $0.050 | $0.150 | 262K | 2026-09-19 |
Frequently asked questions
How much does DiffusionGemma cost?
DiffusionGemma costs $0.050 per 1M input tokens and $0.150 per 1M output tokens, sourced from nano-gpt. Cache reads, audio tokens and >200K-context tiers (where applicable) are listed in the Pricing detail block above.
What is the context window of DiffusionGemma?
DiffusionGemma has a context window of 262K tokens, with a max output of 33K tokens per reply. This is the total combined size of prompt + completion.
Does DiffusionGemma support tool calling?
No. DiffusionGemma does not support tool calling (function calling). If your workflow requires it, look at the /capabilities/tool-calling list for alternatives.
Does DiffusionGemma support structured output / JSON mode?
No. DiffusionGemma does not support structured output / JSON-schema-constrained decoding. If your workflow requires it, look at the /capabilities/structured-output list for alternatives.
Can DiffusionGemma accept image input?
No. DiffusionGemma only accepts text as input. If you need image input, see our /capabilities/vision list for current vision-capable models.
Is DiffusionGemma open-weight?
Yes. DiffusionGemma's weights are publicly available, so you can self-host or fine-tune. Note that open weights ≠ open source — the training data and code are typically not released.
What are the best alternatives to DiffusionGemma?
If DiffusionGemma doesn't fit, consider NanoGPT Help, Web Answer, Ernie 5.0 Thinking Preview. Each one targets the same use case — see the Related links below for direct head-to-head pages.
Where does this data come from?
All numbers are normalised into a single canonical model record and reconciled with each provider's official documentation. We re-pull daily and write any changes (price, context, capability) to the /changelog page.
Explore more
More nano-gpt models
- NanoGPT HelpUnknown pricing
- Web Answer$7.50 in / $7.50 out
- Ernie 5.0 Thinking Preview$1.00 in / $3.50 out
- Venice Uncensored$0.40 in / $1.80 out
- Holo3-35B-A3B Thinking$0.25 in / $1.80 out
Capability lists this model is in
آخر تحديث:
Pricing and capabilities are refreshed daily and reconciled against each provider's official documentation. Always verify critical production decisions with the provider directly.