MN-LooseCannon-12B-v1
nano-gpt/mn-loosecannon-12b-v1Von nano-gpt · Familie: mistral-nemo · veröffentlicht 2024-07-01
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.
Coding1
- Tool calling0/40
- Structured output0/20
- Reasoning0/10
- Context window (100K → 1M)0/20
- Provider availability1/10
Agents6
- Tool calling0/35
- Structured output0/25
- Reasoning0/15
- Output token limit5/15
- Provider availability1/10
JSON / structured output18
- Structured output / JSON mode0/50
- Tool calling0/20
- Temperature control0/10
- Price-friendly for high-volume18/20
Cost efficiency63
- Headline price (log-scaled)63/95
- Has prompt-cache pricing0/5
Long context0
- Context ≥ 100K0/100
Production-readiness58
- Number of independent providers5/40
- Has published per-token price20/20
- Context window ≥ 8K8/15
- 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 | $2.71 < $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 | $5.42 < $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 | $1.23 < $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 | $4.44 < $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 | $6.21 < $0.01 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 · GalrionSoftworks/MN-LooseCannon-12B-v1
Bei 1 Anbietern verfügbar
| Anbieter | Anbieter-Modell-ID | Eingabe / 1M | Ausgabe / 1M | Kontext | Veröffentlicht |
|---|---|---|---|---|---|
| NanoGPT nano-gpt | GalrionSoftworks/MN-LooseCannon-12B-v1 | $0.493 | $0.493 | 16K | 2024-07-01 |
Frequently asked questions
How much does MN-LooseCannon-12B-v1 cost?
MN-LooseCannon-12B-v1 costs $0.493 per 1M input tokens and $0.493 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 MN-LooseCannon-12B-v1?
MN-LooseCannon-12B-v1 has a context window of 16K tokens, with a max output of 8K tokens per reply. This is the total combined size of prompt + completion.
Does MN-LooseCannon-12B-v1 support tool calling?
No. MN-LooseCannon-12B-v1 does not support tool calling (function calling). If your workflow requires it, look at the /capabilities/tool-calling list for alternatives.
Does MN-LooseCannon-12B-v1 support structured output / JSON mode?
No. MN-LooseCannon-12B-v1 does not support structured output / JSON-schema-constrained decoding. If your workflow requires it, look at the /capabilities/structured-output list for alternatives.
Can MN-LooseCannon-12B-v1 accept image input?
No. MN-LooseCannon-12B-v1 only accepts text as input. If you need image input, see our /capabilities/vision list for current vision-capable models.
Is MN-LooseCannon-12B-v1 open-weight?
No. MN-LooseCannon-12B-v1 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 MN-LooseCannon-12B-v1?
If MN-LooseCannon-12B-v1 doesn't fit, consider Brave (Answers), Exa (Research), Auto model (Basic). 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 come from the public models.dev API and are normalised into a single canonical model record. We re-pull daily and write any changes (price, context, capability) to the /changelog page.
Explore more
More nano-gpt models
- Brave (Answers)$5.00 in / $5.00 out
- Exa (Research)$2.50 in / $2.50 out
- Auto model (Basic)$10.00 in / $19.99 out
- Jamba Mini$0.20 in / $0.41 out
- Yi Large$3.20 in / $3.20 out
Zuletzt aktualisiert:
Data is sourced from models.dev and normalized for comparison. Prices and capabilities may change. Always verify critical production decisions with the provider's official documentation.