Ministral 3 3B 2512
Ministral 3 3B 2512Ranking?
Alternatives?
CheaperLing 3.0 Flash VLthinkingOpen×5.1-10%$0.06 / $0.18Memory (4-bit): 1.9 GB → 75 GB
BetterGPT-6 Lunalow×4.4-42%$0.005Function calling only with none · Chat Completions+3IOpenK2 Horizon 7Bthinking×4.3+425%$0.30 / $1.20Memory (4-bit): 1.9 GB → 4.5 GB+1CheaperLing 3.0 Flash FinthinkingOpen×4.7-10%$0.06 / $0.18Memory (4-bit): 1.9 GB → 76 GB+1CheaperLing 3.0 FlashthinkingOpen×4.2-10%$0.06 / $0.18Memory (4-bit): 1.9 GB → 76 GB+1Ling 3.0 Flash VLthinkingOpen×5.1-10%$0.06 / $0.18Memory (4-bit): 1.9 GB → 75 GBLing 3.0 Flash FinthinkingOpen×4.7-10%$0.06 / $0.18Memory (4-bit): 1.9 GB → 76 GB+1GPT-6 Lunalow×4.4-42%$0.005Function calling only with none · Chat Completions+3Ling 3.0 FlashthinkingOpen×4.2-10%$0.06 / $0.18Memory (4-bit): 1.9 GB → 76 GB+1
Qwen 3.5 9Bnon-thinkingOpen×2.7-8%$0.08 / $0.13Memory (4-bit): 1.9 GB → 4.8 GBGPT-6 Lunalow×4.4-42%$0.005Function calling only with none · Chat Completions+3
Granite 4.2 3BthinkingOpen+87%-24%$0.006Context: 131K → 128K+1
Gemma 4 E4BthinkingOpen+84%+4%$0.008Context: 131K → 128K+1GPT-5.6 Lunalow×4.3+25%$0.01GPT OSS 20BhighOpen+85%+46%$0.011Memory (4-bit): 1.9 GB → 13 GB+1Gemma 4 12BthinkingOpen×2.9+50%$0.10 / $0.30Memory (4-bit): 1.9 GB → 6 GBQwen 3.5 9Bnon-thinkingOpen×2.7-8%$0.08 / $0.13Memory (4-bit): 1.9 GB → 4.8 GBQwen3.5 4BthinkingOpen×2.7-40%$0.03 / $0.15Memory (4-bit): 1.9 GB → 2.3 GBMistral Large 4thinking×7.9+14,353%$1.13Infinite whitespace stream+4Mistral Medium 3.5highOpen×2.9+6,298%$0.50Memory (4-bit): 1.9 GB → 77 GBMistral Small 4thinkingOpen×2.3+92%$0.015Memory (4-bit): 1.9 GB → 72 GBMistral Large 3non-thinkingOpen+92%+297%$0.031Memory (4-bit): not verifiedMagistral Medium 1thinking+87%——Context: 131K → 40K+1Devstral Mediumnon-thinkingOpen+86%—$0.40 / $2Context: 131K → 128K+2Magistral Small 1thinkingOpen+70%—$0.70 / $1.16Context: 131K → 128K+2Mistral Small 3non-thinkingOpen+38%—$0.05 / $0.08Context: 131K → 32K+2All Mistral models →Run it yourself
estimateBenchmarks
all categoriesFAQ
How much does Ministral 3 3B 2512 cost?›
$0.10 per 1M input tokens and $0.10 per 1M output tokens; cached input $0.01 per 1M. A typical benchmark task costs about $0.008 at default effort.
Is Ministral 3 3B 2512 free to use?›
There is no free API; the cheapest paid price is $0.10 per 1M input tokens. The weights are open, so you can run it yourself.
How good is Ministral 3 3B 2512, and what is it best at?›
#262 of 334 in Chat & Knowledge, #75 of 132 in Coding, #116 of 133 in Agents & Tool Use, #220 of 245 in Writing & Language, #119 of 124 in Vision & Documents.
Is Ministral 3 3B 2512 better than LFM2 8B A1B?›
On AA Intelligence Index 4.3.2, Ministral 3 3B 2512 scores 4.8 vs 4.9 — about the same.
How can I use Ministral 3 3B 2512?›
Through the API of Mistral.
What is the context window of Ministral 3 3B 2512?›
131K tokens, with up to 105K output tokens.
When was Ministral 3 3B 2512 released? Is there a newer version?›
Released 2025-12-02. It is the current version in its line.
Is Ministral 3 3B 2512 open source?›
Yes, the weights are open (Apache-2.0 licence).
Can I run Ministral 3 3B 2512 locally?›
The weights are open and can be self-hosted; hardware needs are not confirmed yet.
Can Ministral 3 3B 2512 read images?›
It accepts text and images as input.
What is Ministral 3 3B 2512?›
Ministral 3 3B 2512 is an open-weights language model from Mistral, released Dec 2025.
What are the best alternatives to Ministral 3 3B 2512?›
Ling 3.0 Flash VL (cheaper), GPT-6 Luna (better), K2 Horizon 7B (open weights).