Greg 2 Super
nano-gpt/greg-2-superVon nano-gpt · veröffentlicht 2026-06-19
Prices in USD per 1M tokens. Unknown means the provider does not publish per-token pricing.
Fähigkeiten
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.
Coding68
- Tool calling40/40
- Structured output20/20
- Reasoning0/10
- Context window (100K → 1M)7/20
- Provider availability1/10
Agents76
- Tool calling35/35
- Structured output25/25
- Reasoning0/15
- Output token limit15/15
- Provider availability1/10
JSON / structured output77
- Structured output / JSON mode50/50
- Tool calling20/20
- Temperature control0/10
- Price-friendly for high-volume7/20
Cost efficiency47
- Headline price (log-scaled)42/95
- Has prompt-cache pricing5/5
Long context58
- Context window (100K → 2M)48/90
- Has published price for full window10/10
Production-readiness65
- 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)15/15
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 | $10.00 $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 | $20.00 < $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 | $5.50 < $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 | $17.00 $0.02 per request | 8K input tokens (diff + surrounding files) and a 1K-token review comment. PR-bot workloads. |
Agent step per 1,000 steps | $21.00 $0.02 per request | 12K input tokens (long-running tool history) and a 600-token tool-call decision. Cost per agent step. |
Preis-Details
Empfohlene Preise von nano-gpt · crofai/greg-2-super
Bei 1 Anbietern verfügbar
| Anbieter | Anbieter-Modell-ID | Eingabe / 1M | Ausgabe / 1M | Kontext | Veröffentlicht |
|---|---|---|---|---|---|
| NanoGPT nano-gpt | crofai/greg-2-super | $1.50 | $5.00 | 229K | 2026-06-19 |
Frequently asked questions
How much does Greg 2 Super cost?
Greg 2 Super costs $1.50 per 1M input tokens and $5.00 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 Greg 2 Super?
Greg 2 Super has a context window of 229K tokens, with a max output of 229K tokens per reply. This is the total combined size of prompt + completion.
Does Greg 2 Super support tool calling?
Yes. Greg 2 Super supports tool calling (function calling). This makes it suitable for production agent and automation workloads where the model has to invoke external functions reliably.
Does Greg 2 Super support structured output / JSON mode?
Yes. Greg 2 Super 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 Greg 2 Super accept image input?
No. Greg 2 Super only accepts text as input. If you need image input, see our /capabilities/vision list for current vision-capable models.
Is Greg 2 Super open-weight?
No. Greg 2 Super 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 Greg 2 Super?
If Greg 2 Super doesn't fit, consider Ernie 5.0 Thinking Preview, Baichuan 4 Turbo, Holo3-35B-A3B Thinking. 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
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- Holo3-35B-A3B Thinking$0.25 in / $1.80 out
- Jamba Large 1.7$1.99 in / $7.99 out
- Hunyuan Turbo S$0.19 in / $0.37 out
Capability lists this model is in
Zuletzt aktualisiert:
Pricing and capabilities are refreshed daily and reconciled against each provider's official documentation. Always verify critical production decisions with the provider directly.