Mistral Medium 3.5 API262K context

Pricing & specs mirror our live pricing as of July 2026 (pay-as-you-go).

What is Mistral Medium 3.5?

Mistral Medium 3.5 is a dense 128B instruction-following model from Mistral AI. It supports text and image inputs with text output, and is designed for agentic workflows, coding, and complex...

Mistral Medium 3.5 on the release timeline

Mistral Medium 3.5 pricing

Pay-as-you-go on DataLLM Lab — these are our live list prices (identical to the pricing page):

On output price, Mistral Medium 3.5 is cheaper than 25% of the 309 models in the catalog.

What Mistral Medium 3.5 costs per month

Cost = input price × input volume + output price × output volume. The same five workloads run on every model page, so any two compare directly.

Mistral Medium 3.5 vs alternatives

When not to use Mistral Medium 3.5

• It is premium-priced (output cheaper than only 25% of the catalog) — for routine work a cheaper model likely does the job.
• It is served by a single provider, so there is less failover headroom during an outage.

Specs

How to call Mistral Medium 3.5

Copyfrom 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/mistral-medium-3-5",
    messages=[{"role": "user", "content": "Hello"}],
    # supports tools=[...] and tool_choice
)
print(resp.choices[0].message.content)

Copycurl https://api.datallmlab.com/v1/chat/completions \
  -H "Authorization: Bearer YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"mistralai/mistral-medium-3-5","messages":[{"role":"user","content":"Hello"}]}'

Copyimport 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/mistral-medium-3-5",
  messages: [{ role: "user", content: "Hello" }],
});
console.log(r.choices[0].message.content);

The same key routes Mistral Medium 3.5 and 300+ other models — switch models by changing one string. How OpenAI-compatible APIs work.

Prompting tips for Mistral Medium 3.5

• State the goal, not every step. Mistral Medium 3.5 reasons internally — give it the objective, constraints and success criteria and let it plan the approach; over-scripting each step tends to lower quality. Turn effort up for hard problems.
• 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 Mistral Medium 3.5 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 Mistral Medium 3.5's actual capabilities — context window, tool & JSON support, modality and price tier — not generic advice.

Open-source tools for Mistral Medium 3.5

Popular open-source frameworks and agents to build with Mistral Medium 3.5 over the API — star counts pulled from GitHub (July 2026).

Listed by GitHub stars; inclusion is by ecosystem relevance (agent frameworks, SDKs and gateways), not affiliation. Stars change — see each repo for current numbers.

Migrating to Mistral Medium 3.5

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/mistral-medium-3-5. Messages, streaming, tool calls and the rest of your code stay the same. Routing & failover guide.

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. See the error-code guide and failover setup.

Related reading

Frequently asked questions