Gemma 4 31B Thinking TEE
nano-gpt/gemma4-31b-thinking出品方： nano-gpt · 發布 2026-05-02
⚠ 本模型為社群微調 / 衍生版本，並非廠商官方發布。
Prices in USD per 1M tokens. Unknown means the provider does not publish per-token pricing.
能力清單
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.
Coding39
- Tool calling0/40
- Structured output20/20
- Reasoning10/10
- Context window (100K → 1M)8/20
- Provider availability1/10
Agents56
- Tool calling0/35
- Structured output25/25
- Reasoning15/15
- Output token limit15/15
- Provider availability1/10
JSON / structured output67
- Structured output / JSON mode50/50
- Tool calling0/20
- Temperature control0/10
- Price-friendly for high-volume17/20
Cost efficiency58
- Headline price (log-scaled)58/95
- Has prompt-cache pricing0/5
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 | $2.75 < $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 | $5.50 < $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 | $1.40 < $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 | $4.60 < $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 | $6.00 < $0.01 per request | 12K input tokens (long-running tool history) and a 600-token tool-call decision. Cost per agent step. |
定價詳情
推薦定價來自 nano-gpt · TEE/gemma4-31b:thinking
於 1 家供應商可用
| 服務商 | 服務商模型 ID | 輸入 / 1M | 輸出 / 1M | 上下文 | 發布日期 |
|---|---|---|---|---|---|
| NanoGPT nano-gpt | TEE/gemma4-31b:thinking | $0.450 | $1.00 | 262K | 2026-05-02 |
Frequently asked questions
How much does Gemma 4 31B Thinking TEE cost?
Gemma 4 31B Thinking TEE costs $0.450 per 1M input tokens and $1.00 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 Gemma 4 31B Thinking TEE?
Gemma 4 31B Thinking TEE has a context window of 262K tokens, with a max output of 131K tokens per reply. This is the total combined size of prompt + completion.
Does Gemma 4 31B Thinking TEE support tool calling?
No. Gemma 4 31B Thinking TEE does not support tool calling (function calling). If your workflow requires it, look at the /capabilities/tool-calling list for alternatives.
Does Gemma 4 31B Thinking TEE support structured output / JSON mode?
Yes. Gemma 4 31B Thinking TEE supports structured output / JSON-schema-constrained decoding. This makes it suitable for production agent and automation workloads where the model has to invoke external functions reliably.
Can Gemma 4 31B Thinking TEE accept image input?
No. Gemma 4 31B Thinking TEE only accepts text as input. If you need image input, see our /capabilities/vision list for current vision-capable models.
Is Gemma 4 31B Thinking TEE open-weight?
No. Gemma 4 31B Thinking TEE is a proprietary model — only nano-gpt (and any approved hosting partners) can serve it. The pricing above reflects the cheapest API access.
What are the best alternatives to Gemma 4 31B Thinking TEE?
If Gemma 4 31B Thinking TEE doesn't fit, consider ERNIE X1.1, Brave (Research), MiroThinker 1.7 Deep Research Mini. 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
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- MiroThinker 1.7 Deep Research Mini$1.25 in / $10.00 out
- Baichuan 4 Turbo$2.42 in / $2.42 out
- v0 1.0 MD$3.00 in / $15.00 out
Capability lists this model is in
最近更新：
Pricing and capabilities are refreshed daily and reconciled against each provider's official documentation. Always verify critical production decisions with the provider directly.