Ministral 3 8B 2512 APIOPEN WEIGHTS262K context
A balanced model in the Ministral 3 family, Ministral 3 8B is a powerful, efficient tiny language model with vision capabilities.
Pricing & specs mirror our live pricing as of July 2026 (pay-as-you-go).
What is Ministral 3 8B 2512?
A balanced model in the Ministral 3 family, Ministral 3 8B is a powerful, efficient tiny language model with vision capabilities.
Ministral 3 8B 2512 on the release timeline
Ministral 3 8B 2512 pricing
Pay-as-you-go on DataLLM Lab — these are our live list prices (identical to the pricing page):
| Model | Input / 1M | Output / 1M | Cache read | Cache write | Context |
|---|---|---|---|---|---|
| Ministral 3 8B 2512 | $0.15 | $0.15 | $0.01 | — | 262K |
On output price, Ministral 3 8B 2512 is cheaper than 92% of the 309 models in the catalog. It is served by 2 providers; the best combined rate at our last snapshot was ≈ $0.15 / $0.15 per 1M via Mistral, with upstream quantizations fp8.
What Ministral 3 8B 2512 costs per month
| Workload | Tokens in / out (monthly) | Est. cost |
|---|---|---|
| Support chatbot | 40M / 12M | $7.80 |
| RAG / knowledge base | 200M / 20M | $33.00 |
| Coding agent | 80M / 25M | $15.75 |
| Batch extraction | 150M / 8M | $23.70 |
| Content generation | 20M / 40M | $9.00 |
Estimate your own monthly cost
Cost = input price × input volume + output price × output volume. The same five workloads run on every model page, so any two compare directly.
Ministral 3 8B 2512 vs alternatives
| Model | Input / 1M | Output / 1M | Context | Our test |
|---|---|---|---|---|
| Ministral 3 8B 2512 | $0.15 | $0.15 | 262K | — |
| Ministral 3 14B 2512 | $0.20 | $0.20 | 262K | — |
| Mistral Small 3.2 24B | $0.07 | $0.20 | 128K | — |
| Ministral 3 3B 2512 | $0.10 | $0.10 | 131K | — |
| Trinity Mini | $0.04 | $0.15 | 131K | — |
| Command R7B (12-2024) | $0.04 | $0.15 | 128K | — |
Specs
| Model ID | mistralai/ministral-8b-2512 |
| Modality | text+image->text (input: text, image) |
| Context window | 262,144 tokens |
| Tool / function calling | ✅ Yes |
| Structured output (JSON) | ✅ Yes |
| Released | 2025-12-02 |
| Open weights | ✅ mistralai/Ministral-3-8B-Instruct-2512 · 101.4K downloads / 179 likes (30d) |
How to call Ministral 3 8B 2512
from openai import OpenAI
client = OpenAI(base_url="https://api.datallmlab.com/v1", api_key="YOUR_DATALLM_LAB_KEY")
resp = client.chat.completions.create(
model="mistralai/ministral-8b-2512",
messages=[{"role": "user", "content": "Hello"}],
# supports tools=[...] and tool_choice
)
print(resp.choices[0].message.content)curl https://api.datallmlab.com/v1/chat/completions \
-H "Authorization: Bearer YOUR_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"mistralai/ministral-8b-2512","messages":[{"role":"user","content":"Hello"}]}'import OpenAI from "openai";
const client = new OpenAI({ baseURL: "https://api.datallmlab.com/v1", apiKey: process.env.DATALLM_KEY });
const r = await client.chat.completions.create({
model: "mistralai/ministral-8b-2512",
messages: [{ role: "user", content: "Hello" }],
});
console.log(r.choices[0].message.content);The same key routes Ministral 3 8B 2512 and 300+ other models — switch models by changing one string. How OpenAI-compatible APIs work.
Prompting tips for Ministral 3 8B 2512
- Be explicit and show the shape of the answer. Spell out format, tone and constraints up front, and include one short example of the output you want — Ministral 3 8B 2512 follows concrete instructions better than abstract ones.
- Huge 262K context — but anchor the ask. You can paste whole documents or codebases; models attend most to the start and end, so put the key instruction at the top and restate it after long inputs.
- It reads images. Send image parts alongside your text and ask for specific outputs (named JSON fields, a table, "what changed") rather than "describe this".
- Give it real tools. Pass a
tools=[...]schema instead of asking it to "pretend" — let Ministral 3 8B 2512 emit tool calls, execute them, and feed results back for the next turn. - Constrain JSON with a schema. Use
response_format/ structured outputs rather than "reply in JSON" to get valid, parseable objects every time.
Tips are derived from Ministral 3 8B 2512's actual capabilities — context window, tool & JSON support, modality and price tier — not generic advice.
Open-source tools for Ministral 3 8B 2512
Popular open-source projects for running and building with Ministral 3 8B 2512 — star counts pulled from GitHub (July 2026).
Listed by GitHub stars; inclusion is by ecosystem relevance (inference engines, agent frameworks and SDKs), not affiliation. Stars change — see each repo for current numbers.
Migrating to Ministral 3 8B 2512
Coming from OpenAI or another gateway? On an OpenAI-compatible setup the only changes are base_url → https://api.datallmlab.com/v1 and model → mistralai/ministral-8b-2512. Messages, streaming, tool calls and the rest of your code stay the same. Routing & failover guide.
Self-host or use the API?
Ministral 3 8B 2512 ships open weights (mistralai/Ministral-3-8B-Instruct-2512, ~101.4K downloads and 179 likes in the last 30 days), with community quantizations (fp8) for smaller GPUs, so you can run it yourself. Most teams still use the API: no GPU to provision or keep warm, no inference ops, and instant access across 2 providers with automatic failover. Self-host when data residency or fixed per-token economics matter most.
Rate limits & reliability
Rate limits here are the DataLLM Lab gateway's, not the upstream vendor's. On 429 (rate limited) or 503 (provider busy), retry with exponential backoff; because Ministral 3 8B 2512 is served by 2 providers, requests can fail over to a healthy one automatically. See the error-code guide and failover setup.
Related reading
Call Ministral 3 8B 2512 with one key
300+ models behind one OpenAI-compatible endpoint — better prices, better uptime, no subscriptions.
Get an API keyCompare pricingFrequently asked questions
What is Ministral 3 8B 2512?
A balanced model in the Ministral 3 family, Ministral 3 8B is a powerful, efficient tiny language model with vision capabilities. It accepts text, image input with a 262K-token context window and was released on 2025-12-02. On DataLLM Lab it is callable through one OpenAI-compatible endpoint.
How much does Ministral 3 8B 2512 cost?
On DataLLM Lab it is $0.15 per 1M input tokens and $0.15 per 1M output tokens, with cached input at $0.01/1M — cheaper than about 92% of the 309-model catalog on output price. Pay-as-you-go, no subscription.
What is the context window of Ministral 3 8B 2512?
262K tokens.
Is Ministral 3 8B 2512 open source?
Yes — open weights are published on Hugging Face (mistralai/Ministral-3-8B-Instruct-2512), with about 101.4K downloads and 179 likes in the last 30 days. You can self-host it or call it via DataLLM Lab.
How do I call the Ministral 3 8B 2512 API?
Point any OpenAI SDK at https://api.datallmlab.com/v1 and set model to "mistralai/ministral-8b-2512". One DataLLM Lab key routes this model and 300+ others; no code changes beyond the base URL and model string.
What are good alternatives to Ministral 3 8B 2512?
Close options by price and capability include Ministral 3 14B 2512, Mistral Small 3.2 24B, Ministral 3 3B 2512 — all callable with the same DataLLM Lab key.