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Free AI Visibility AuditMistral Medium 3.5 is a dense 128B instruction-following model from Mistral, built for agentic workflows, coding and complex multi-step tasks. It takes text, images and files as input and returns text.
MistralVerified 24/09/2026Released 30/04/202601 / decision
Each figure sits next to the middle of the field, so it can be read as dear or cheap, wide or narrow, rather than floating on its own. Compared against the 231 models we track, not against an absolute standard.
Capability
Not measured
No published benchmark scores yet
Input / 1M tokens
$1.50
field median $0.43
Output / 1M tokens
$7.50
field median $1.80
Context
262K
field median 500K
02 / overview
Four questions, answered separately, because somebody arrives at one of them rather than at the top of the page.
The job it was built for, and the job it is not for.
Mistral Medium 3.5 is a mid-sized dense instruction-following model aimed at work that runs in steps: tool calls, coding tasks, and reasoning over documents rather than single-turn chat. It is a general working model, not a frontier research model and not a small cheap classifier, so it is the wrong choice if you want the absolute lowest cost per call or the largest possible model on a hard reasoning problem.
Where it sits in its line, and when a sibling is the better pick.
Mistral Medium 3.5 first appeared on 30 April 2026, sitting in the middle of Mistral's range, above the small models and below the large ones. It is the right pick when you want one model to handle agent loops, code and document work at a predictable cost. It is the wrong pick for high-volume, trivial classification, where a smaller Mistral model does the same job for less.
The API, the apps it powers, and what its limits let you do.
Mistral Medium 3.5 is reached through the Mistral API, and it accepts text, images and files in the same request. The context window is large enough to hold long codebases, full contract sets or a whole agent transcript without chunking, and the maximum output is unusually generous, so it can return long documents, full file rewrites or extended reasoning traces in a single response rather than forcing you to stitch continuations together.
What changes because this exists, or why it does not.
The combination that matters here is a long context plus a very high output ceiling on the same model. Work that previously needed chunking on the way in and continuation handling on the way out, such as refactoring a large module or drafting a long report from a folder of source files, becomes one call. For teams already running Mistral, it removes a tier of orchestration code.
03 / evidence
Split by category, so a strong number never hides a thin evidence base. Verified means we read it on the benchmark's own published results; a provider's claim about its own model is shown and labelled rather than dropped.
No published benchmark scores for this model yet.
We publish a score only where we can link the result to where it was published. Until a benchmark result for this model exists in a source we read, this section stays empty rather than being filled with a provider's marketing figure.
How we decide what counts as evidence04 / ledger
Every published row, grouped by category, each compared with the best published score on the same benchmark. 'Is 64% good' is a question nobody can answer; '26 points behind the leader' is one anybody can.
Nothing in the ledger yet.
Each row here carries a score, the benchmark it came from, what the leading model scored on the same test, and a link to the published result. Rows appear as results are published and read.
How we decide what counts as evidence05 / capability
Where this model is strong, and against how many peers. Ranks are against models with evidence in that category, not against everything we track: ranking against models nobody tested would rank who published, not who is better.
No category scores to shape yet.
A category score is the weighted mean of the benchmarks published for it. With no published rows there is nothing to average, and an empty chart drawn at zero would say something false.
How we decide what counts as evidence06 / cost
List API rates as last read from the provider, with the source on every row, plus every change we have recorded since we started tracking it.
| Charge | Price | Unit | Read on | Source |
|---|---|---|---|---|
| Input | $1.50 | per 1M tokens | 2026-09-24 | Check |
| Output | $7.50 | per 1M tokens | 2026-09-24 | Check |
Mistral Medium 3.5 sits in the affordable middle of the market, costing noticeably less than the frontier models from the large US labs while charging a clear premium over small open-weight options. Output costs several times more than input, which is worth planning around given how much text this model is capable of producing in one go.
| Workload | Input / month | Output / month | Cost |
|---|---|---|---|
| A small product team | 20M tokens | 5M tokens | $67.50 |
| A busy support assistant | 200M tokens | 40M tokens | $600.00 |
| A document pipeline | 1000M tokens | 100M tokens | $2,250.00 |
List API rates, no caching and no batch discount, which both providers offer and which change the answer a great deal. Treat these as the ceiling, not the bill.
07 / specs
As listed by the provider's own catalogue and re-read every few hours. Anything absent is absent there too.
| Context window | 262,144 tokens |
|---|---|
| Maximum output | 209,715 tokens |
| Modalities | text, image, file |
| Released | 30/04/2026 |
| Status | Current |
| Catalogue identifier | mistralai/mistral-medium-3-5 |
08 / lineage
What this model replaced, what replaced it, and what else its provider has in the field.
Also from Mistral
09 / line
Every model in this family in release order, so a page from eight months ago says in one glance that two newer ones exist.
Nothing else in this line yet.
A line is worked out from the naming and the release dates across every model page we hold. It fills in as the provider ships successors, or as we pick up the models that came before this one.
How we decide what counts as evidence10 / notes
What this model changes for a brand trying to be cited in AI answers, and every change we have logged since it launched.
What it changes for you
Mistral Medium 3.5 reads files and images alongside text, so the assets a buyer feeds it, your PDFs, your pricing sheets, your docs screenshots, are all in scope for the answer it gives. With a context window this large, a buyer can drop your entire documentation set and a competitor's into one prompt and ask for a comparison, which means the clarity and structure of your written material decides how you come out. Being retrievable in text is not enough if your key claims only live inside images without captions.
Most buyers meet Mistral Medium 3.5 through the API, either directly or inside a product their vendor has built on it. It also turns up in European deployments where teams have chosen Mistral for data residency or sovereignty reasons, so it may be the model answering questions inside an internal assistant rather than a consumer chat app.
Nothing published here yet. Changes appear within hours of a provider announcing them.
11 / questions
The things people ask about this model, answered from what is on this page rather than from anywhere else.
Models change what gets cited. We measure whether AI answers name your brand or your competitors across every assistant, and show you what to change.