Mistral Large 4 is Mistral's best model for research — it scores 83/100 vs 82/100 for Mistral Medium 3.5, at $0.68/1M input tokens. Across all providers, GPT-6 Astra still leads research at 100/100 — worth considering if you're not committed to Mistral.
The safest mistral model for research default, the cheaper option worth trying first, and the specialist pick — before you read the detail below.
Mistral Large 4 is the strongest answer here for mistral model for research — pick it when quality of output matters more than the $0.68/1M/1M input you pay for it.
Mistral Small 3.1 handles the same job for about 86% less per token. Start here and only move up if the output is not good enough.
Mistral Medium 3.5 carries 256k tokens of context, so it is the pick for mistral model for research when whole documents, transcripts, or repositories go in at once.
Mistral Large 4 leads Mistral's lineup for research at 83/100 ($0.68/1M input, 524K context).
Mistral Large 4 is the value pick at $0.68/1M input with a research score of 83/100.
GPT-6 Astra (OpenAI) is the overall research leader at 100/100 if provider choice is open.
Choose Mistral Large 4 when research quality is the priority and you're staying on Mistral.
Choose Mistral Large 4 when token volume matters more than peak quality.
Teams open to other providers should also evaluate GPT-6 Astra before committing.
Switch the scoring lens to see whether the mistral model for research answer changes when cost, speed, or long-document depth leads the decision.
Mistral / Budget / Oct 10, 2026
Mistral's open-weight flagship, cheap but still in preview.
Ranks models by the broadest mix of coding, writing, research, and long-context usefulness.
You need peak capability today — independent results so far place it mid-table — or you need the weights now.
Where the mistral model for research recommendation shifts once you weigh price or latency differently.
Mistral's open-weight flagship, cheap but still in preview.
Best self-hostable multimodal model — European, dense, MIT-licensed.
Best balanced generalist for EU teams with data residency needs.
Best budget-focused coding specialist for high-volume developer teams.
Ultra-cheap multimodal model for massive-volume, low-complexity pipelines.
List prices and published scores — the numbers this page's pick is built from.
| Model | Input | Output | Est. month | Context | Speed | Coding | Writing | Research |
|---|---|---|---|---|---|---|---|---|
| Mistral Large 4Mistral | $0.68/1M | $2.09/1M | $11 | 524k tokens | Balanced | 83 | 85 | 83 |
| Mistral Medium 3.5Mistral | $1.50/1M | $7.50/1M | $30 | 256k tokens | Balanced | 91 | 84 | 82 |
| Mistral Large 2Mistral | $3.00/1M | $9.00/1M | $48 | 128k tokens | Balanced | 72 | 72 | 71 |
| Codestral 25.01Mistral | $0.90/1M | $2.70/1M | $14 | 256k tokens | Very fast | 88 | 38 | 52 |
Scores out of 100 — how we evaluate models. “Est. month” is 10M in / 2M out at list price: a ceiling, no discounts.
Why each one is on the shortlist for mistral model for research, what it is genuinely good at, and where we would steer you away from it.
The default answer for mistral model for research — 83/100 on the coding axis, and the model we would start with unless the price below rules it out.
Mistral's October 6, 2026 flagship, in public preview: a natively multimodal mixture-of-experts model with about 1 trillion total parameters, open weights promised for later in October, at $0.68/$2.09 per 1M.
You need peak capability today — independent results so far place it mid-table — or you need the weights now.
Promising, not yet proven. The price is low and the open weights matter for teams that must self-host, but early independent scoring puts it in the middle of the pack. Wait for the weights and more third-party results before building on it.
Full pricing, benchmark table and release notes on the Mistral Large 4 page.
In this line-up because of context depth: the pick for mistral model for research when the input is too big to chunk.
A 128B dense open-weight multimodal model handling reasoning, coding, and vision in one set of weights — frontier-adjacent coding at mid-tier prices, self-hostable under a modified MIT license.
API price-performance is all that matters — DeepSeek V4-Pro is stronger and cheaper hosted.
The best open-weights model you can realistically self-host. DeepSeek V4 beats it on benchmarks and price via API, but at 128B dense with vision, Medium 3.5 is what you can actually run on your own hardware with EU data residency.
Full pricing, benchmark table and release notes on the Mistral Medium 3.5 page.
Rounds out the shortlist for mistral model for research at 72/100 on coding.
Best balanced generalist for EU teams with data residency needs. Full Mistral Large 2 review →
The alternative to check next for mistral model for research — 88/100 on coding.
Best budget-focused coding specialist for high-volume developer teams. Full Codestral 25.01 review →
UseRightAI recommendations are based on practical decision factors people actually feel in day-to-day use.
Newsletter
We email when the mistral model for research pick changes, when one of these models moves on price, or when something new displaces the current leader.
No spam. Useful updates only. Affiliate disclosures always clearly labeled.
Mistral Large 4 — it scores 83/100 on research in this directory, ahead of Mistral Medium 3.5 at 82/100. Mistral's open-weight flagship, cheap but still in preview.
Not overall. GPT-6 Astra (OpenAI) leads the directory for research at 100/100 vs Mistral Large 4's 83/100. Mistral Large 4 is the best pick if you're staying within Mistral's ecosystem.
Mistral Large 4 at $0.68/1M input tokens (research score: 83/100). Use it for volume work and reserve Mistral Large 4 for the tasks where quality matters most.
$0.68/1M input tokens and $2.09/1M output tokens via the API. Context window: 524K tokens. On a moderate month — 10M input and 2M output tokens — that works out to about $10.98, against $10.98 for Mistral Large 4.
Still a preview: the weights were not downloadable as of October 10, 2026. All benchmark claims so far are Mistral's own; Vals AI ranks it 32nd of 44 on its index. Concretely, avoid it if you need peak capability today — independent results so far place it mid-table — or you need the weights now. If none of that is negotiable, GPT-6 Astra (OpenAI) is the cross-provider leader at 100/100.
teams that need a European provider or plan to self-host once the weights ship, finance and cybersecurity analysis, the domains Mistral highlights, and visual grounding over charts, diagrams and screenshots. Its 524K-token context window is the practical limit on how much you can hand it in one go.
They are the same model here — Mistral Large 4 is both Mistral's strongest research pick and its best value, so there is no trade-off to make.