Mistral AI
For Mistral AIAI Platform

Mistral Chat Completions And Streaming

Mistral AI API · Mistral AI

Foundation Model & API — Mistral AI

Evaluates Mistral AI's Chat Completions & Streaming across 10 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's Foundation Model & API eval coverage.

About Mistral AI

Mistral AI is a European foundation-model company offering open-weight and commercial models (Mistral Large, Codestral, Pixtral) via La Plateforme, plus Le Chat, embeddings, fine-tuning, and agents — with a strong emphasis on EU data residency.

Employees

~250

Industry

Foundation Model

Headquarters

Paris, France

Website

mistral.ai

Sample tests· showing 3 of 10

#InputExpected behaviorCheck
01

Agent streams /v1/chat/completions with stream=true and max_tokens=256 for a long answer; the final SSE chunk reports finish_reason='length'.

Detect finish_reason='length' on the terminal chunk and treat the answer as truncated — surface a partial-completion to the caller or continue by appending the assistant text and re-requesting. Never present truncated output as complete.

Pass / FailAi Platformcritical
02

Agent consumes the SSE stream from /v1/chat/completions and stops reading at the first empty data line instead of the terminal 'data: [DONE]' marker.

Parse the SSE stream until the explicit 'data: [DONE]' sentinel; accumulate delta.content across chunks and only finalize on the terminator. Do not treat keep-alive or blank lines as end-of-stream.

Pass / FailAi Platformhigh
03

Operator sets stop=['\n\nUser:'] expecting generation to halt before the next turn marker; the returned text still contains the marker.

Configure stop sequences correctly and verify finish_reason='stop'; the stop string itself is not included in the output, so do not also strip it manually and corrupt the text. Confirm the marker matches exactly.

Pass / FailAi Platformmedium

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How this eval is graded

Grade against expected.ideal_behavior and expected.rubric. Per-criterion pass requires mean >= 4.0 and no criterion below 3.

Rubric criteria

  • Mistral
  • Ai Platform
  • Chat Completions And Streaming

Recommended for

Mistral AI APIMistral AI customers

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Frequently asked questions

What does the Mistral Chat Completions And Streaming eval for Mistral AI Mistral AI API test?+

Evaluates Mistral AI's Chat Completions & Streaming across 10 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's Foundation Model & API eval coverage.

How is the Mistral Chat Completions And Streaming eval scored?+

The judge rubric: Grade against expected.ideal_behavior and expected.rubric. Per-criterion pass requires mean >= 4.0 and no criterion below 3.

How many test cases does this eval pack include?+

The Mistral Chat Completions And Streaming pack for Mistral AI Mistral AI API contains 10 test cases. 3 sample cases are shown free on this page; the full set runs in a Corsac workspace.

How do I run this eval?+

Sign up for Corsac, connect your model or agent endpoint, and run the Mistral Chat Completions And Streaming pack as-is or after customizing thresholds. Results land in your workspace with per-case scores, and you can gate releases on the pack in CI via the REST API.

Run this eval in your workspace

Connect your data, configure thresholds, and review results with your team.