Mistral Small 3.1 is Mistral's cheapest model at $0.1/1M input tokens — 97% less than the flagship Mistral Large 2. It is also the best capability-per-dollar pick in the lineup.
The safest mistral model worth using default, the cheaper option worth trying first, and the specialist pick — before you read the detail below.
Mistral Small 3.1 is the strongest answer here for mistral model worth using — pick it when quality of output matters more than the $0.10/1M/1M input you pay for it.
Claude Opus 4.5 is the cheaper way in for mistral model worth using, at $5.00/1M/1M input against Mistral Small 3.1's $0.10/1M/1M.
Codestral 25.01 is the fastest of these for mistral model worth using — worth it when latency is what the reader notices, not the last few points of reasoning depth.
Mistral Small 3.1 is the lowest-cost Mistral model: $0.1/1M input, $0.3/1M output.
Mistral Small 3.1 is the best capability-per-dollar pick (budget score 98/100).
Mistral Large 2 costs 30x more on input — reserve it for work where quality is the bottleneck.
Choose Mistral Small 3.1 for high-volume, low-stakes tasks like classification, extraction, and drafts.
Choose Mistral Small 3.1 as the everyday default if you want one budget model.
Route only the hardest tasks to Mistral Large 2 — a two-tier setup usually cuts spend 60–80%.
Switch the scoring lens to see whether the mistral model worth using answer changes when cost, speed, or long-document depth leads the decision.
Mistral / Balanced / Aug 6, 2026
Best self-hostable multimodal model — European, dense, MIT-licensed.
Ranks models by the broadest mix of coding, writing, research, and long-context usefulness.
API price-performance is all that matters — DeepSeek V4-Pro is stronger and cheaper hosted.
Where the mistral model worth using recommendation shifts once you weigh price or latency differently.
Ultra-cheap multimodal model for massive-volume, low-complexity pipelines.
Best budget-focused coding specialist for high-volume developer teams.
Best self-hostable multimodal model — European, dense, MIT-licensed.
Best balanced generalist for EU teams with data residency needs.
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 Small 3.1Mistral | $0.10/1M | $0.30/1M | $1.60 | 128k tokens | Very fast | 55 | 66 | 52 |
| Codestral 25.01Mistral | $0.90/1M | $2.70/1M | $14 | 256k tokens | Very fast | 88 | 38 | 52 |
| 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 |
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 worth using, what it is genuinely good at, and where we would steer you away from it.
Our pick for mistral model worth using. It scores 66/100 on the writing axis we weight this page by, and nothing else in this shortlist matches it on output quality.
Mistral's ultra-budget multimodal model — exceptionally cheap with vision support, built for high-volume lightweight tasks where cost is the primary constraint.
You need reliable multi-step reasoning or coding quality — it won't hold up.
The cheapest credible option in the directory. Use it when volume is enormous and task complexity is low.
Full pricing, benchmark table and release notes on the Mistral Small 3.1 page.
The fastest model in this shortlist for mistral model worth using. Pick it when turnaround is what your readers or users notice.
Coding-specialist model designed for fast engineering assistance at a budget-conscious price point.
You need a single model that also handles writing or deep document synthesis.
The sharpest cheap coding specialist when cost control is part of the engineering plan.
Full pricing, benchmark table and release notes on the Codestral 25.01 page.
Rounds out the shortlist for mistral model worth using at 84/100 on writing.
Best self-hostable multimodal model — European, dense, MIT-licensed. Full Mistral Medium 3.5 review →
The alternative to check next for mistral model worth using — 72/100 on writing.
Best balanced generalist for EU teams with data residency needs. Full Mistral Large 2 review →
UseRightAI recommendations are based on practical decision factors people actually feel in day-to-day use.
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Mistral Small 3.1 at $0.1/1M input and $0.3/1M output tokens. Ultra-cheap multimodal model for massive-volume, low-complexity pipelines.
Mistral Small 3.1 is the best capability-per-dollar pick in Mistral's lineup (budget score 98/100). It handles ultra-high-volume classification, summarisation, and lightweight vision tasks well — step up to Mistral Large 2 only where quality visibly falls short.
Mistral Small 3.1 costs $0.1/1M input vs $3/1M for Mistral Large 2 — a 97% saving on input tokens.
Codestral 25.01 — 256K tokens at $0.9/1M input. Context is where budget models are least compromised: you usually lose reasoning depth before you lose window size, so a cheap model is often a perfectly good choice for summarising or extracting from long documents.
Weak on complex reasoning, hard coding, and nuanced writing. Not suitable for tasks requiring deep context retention or multi-step logic. Avoid it if you need reliable multi-step reasoning or coding quality — it won't hold up.
On a moderate workload of 10M input and 2M output tokens, Mistral Small 3.1 runs about $1.60 against $48.00 for Mistral Large 2 — a difference of $46.40 a month at the same volume. Output tokens dominate the bill on both, so the length of the responses you generate matters far more than the length of your prompts.
Mixing is almost always cheaper for the same quality. Route high-volume, low-stakes work — classification, extraction, first drafts, routine agent steps — to Mistral Small 3.1, and reserve Mistral Large 2 for the calls where a wrong answer costs real time. Teams that split this way typically cut spend substantially without a quality drop anyone notices, because most tokens in a real workload are not hard problems.