Mistral Studio / La Plateforme
$10 in credits refresh on a published schedule.
https://api.mistral.ai/v1Models mentioned
mistral-moderation-2603labs-leanstral-2603mistral-small-2603mistral-medium-3-5mistral-large-2512ministral-14b-2512ministral-8b-2512ministral-3b-2512Equivalent paid value
How this was valued: Face value of the recurring API credit. Each eligible paid model can consume the shared balance; always-zero-priced experimental models add unquantified value.
Limits and terms
- Monthly API Credit USD
- 10
- Dimensions
- requests per secondtokens per minutetokens per month
- Numeric Limits
- dashboard only
What happens to your prompts?
Free-mode inputs and outputs are eligible for model improvement and training by default unless the user opts out.
- Plan Scope
- Mistral Studio and API. Free, pay-as-you-go, enterprise, Labs, and stateful features have materially different data-use and retention rules.
- Prompt Retention
- Free-mode inputs and outputs may be retained for model improvement unless the user opts out. Paid stateless API traffic can qualify for zero data retention when approved; stateful features necessarily store application data.
- Response Retention
- Follows the same plan, endpoint, and feature-specific rules as prompts.
- Ordinary Logging
- Mistral processes technical and usage logs for service delivery, security, abuse prevention, and legal compliance; zero-data-retention eligibility does not eliminate all account or operational metadata.
- Model Training
- Free Mistral Studio and API usage may be used to train and improve models by default, with an opt-out. Pay-as-you-go API usage is opted out by default. Labs models may use data for training regardless of the general setting.
- Product Improvement
- Depends on plan and opt-out status. Free usage is eligible by default; paid API traffic is excluded by default, subject to feature and model exceptions.
- Human Or Operator Access
- Content retained for improvement, safety, support, or legal reasons may be accessed by authorized personnel under Mistral's controls; no universal operator-blind commitment applies to free mode.
- Subprocessors And Routing
- Mistral and its listed subprocessors process service data under the privacy policy and DPA; third-party integrations can add separate terms.
- Deletion Controls
- Users can opt out of training in privacy controls. Zero data retention is available only for eligible paid stateless endpoints and requires approval; stored conversations, files, fine-tunes, and other stateful resources must be deleted through their feature controls.
- Caveat
- Mistral's general API privacy documentation and its free-mode documentation are easy to read as conflicting. The narrower free-mode disclosure controls this audit's free-tier finding, and Labs models remain an explicit exception.
Governing documents
- Terms Of Service
- https://legal.mistral.ai/terms
- Data Processing Addendum
- https://legal.mistral.ai/terms/data-processing-addendum
Eligibility
- Account Required
- Yes
- Payment Method Required
- No
- Intended Use
- evaluation and prototyping
Primary sources
Before you build with Mistral Studio / La Plateforme
Read the classification narrowly
This record has current first-party evidence for some zero-cost hosted inference. The label describes the bounded offer supported by evidence on 2026-09-19; it does not guarantee permanence, production suitability, uptime, latency, model quality, or access from every region.
Resolve the live model route
This snapshot records 8 model IDs. Match the exact ID against the provider's live catalog before using it in code; zero-price routes and aliases can rotate while an older documentation page remains online. The recorded base endpoint is https://api.mistral.ai/v1, but the provider's current API reference remains authoritative for paths, authentication, and request shape.
Confirm account and billing boundaries
An account is required, so public catalog evidence cannot by itself prove the limits, balance, or billing behavior attached to your workspace. The reviewed evidence explicitly says no payment card is required. Re-check the signup flow because eligibility and billing controls can change after the snapshot. The equivalent paid value shown here prices a documented allowance where possible; it is not cash, guaranteed savings, or protection from overage.
Re-read the prompt policy for your plan
This record classifies the reviewed handling as “Not private”: Free-mode inputs and outputs are eligible for model improvement and training by default unless the user opts out. Provider policies can distinguish free, paid, enterprise, opted-in, and feature-specific traffic, so verify the governing terms for the exact account and route that will receive your data.
Follow the evidence, then re-check it
The record links 13 first-party sources covering the offer, catalog, limits, pricing, terms, privacy, or adoption evidence available to the audit. Prefer the newest governing document or live catalog when sources disagree, and submit a correction when a provider changes a material term.
Match the quota to the workload shape
Translate the published allowance into the traffic pattern you actually expect instead of comparing headline totals alone. A daily token pool can look generous while a low requests-per-minute or concurrency ceiling blocks interactive bursts; a high request limit can still fail a long-context job when input, output, or per-request tokens are capped. Separate prompt tokens from generated tokens, include retries and tool calls, and test the largest realistic payload. If the provider documents more than one limit window, the tightest window at your peak load is the practical ceiling. Research, experimental, and community access can also carry eligibility or fair-use constraints that cannot be modeled as a simple number.
Plan fallback without changing the rules
A fallback should preserve more than API syntax. Confirm that the substitute route supports the required modality, context length, streaming behavior, structured output, tools, and safety controls, then compare its prompt-retention and training terms. An OpenAI-compatible request shape does not make providers operationally or contractually equivalent. Decide which errors may trigger a retry, cap retry storms, and prevent an exhausted free route from silently switching to a billable model. If deterministic output matters, record the model revision and sampling settings; rotating aliases and free-model pools can change behavior even when the endpoint remains available.
Monitor the offer as a dependency
Capture the model ID, response model field, rate-limit headers, usage fields, latency, HTTP status, and any provider request identifier for each test. Watch for authorization failures, quota exhaustion, catalog removal, policy revisions, and dashboard balance changes as separate failure modes. Re-check the provider’s live catalog and governing pages on a schedule proportionate to the workload’s importance, and keep the dated sources that supported your decision. Free capacity is especially suitable for prototypes, evaluations, fallbacks, and bounded workloads when the application can tolerate change; a production dependency still needs observability, an exit path, and an owner responsible for re-verification.
Test one complete request before scaling
Start with the smallest permitted request using the exact credential, model ID, endpoint, and account type you intend to deploy. Record the HTTP status, response headers, usage fields, latency, and any dashboard balance change. Then exercise the failure path: an invalid model, an exhausted quota, or a rate-limit response should fail clearly without silently switching to a paid route. If the provider supports streaming or tool calls, validate those features separately because a free model can expose a narrower capability set than its paid counterpart. Keep a budget ceiling outside the application whenever billing is possible, and avoid sending sensitive data until the observed route matches the reviewed data agreement.