Ministral 8B for on-device llms
Ministral 8B is ranked #6 on LLMDex's on-device llms ranking out of 7 models we track for this use case. Below, the specific reasons it slots where it does, and when you should reach for an alternative.
Updated
At a glance
- Rank
- #6 of 7
- Context
- 128K tokens
- Output / 1M
- Pricing not published
- Released
- Oct 2024
Why Ministral 8B fits this task
Three things about Ministral 8B that map directly onto what this task rewards: Edge-optimized; Strong 8B-class quality. Beyond the task-specific fit, Ministral 8B also brings edge-optimized and strong 8b-class quality, both of which compound when the workload broadens.
The criteria this task rewards
LLMDex ranks best on-device llms on 5 criteria , these are the axes the ranking uses, in priority order:
- Performance under 8B parameters
- Quantization tolerance (int4, int8)
- Memory footprint after quantization
- Inference speed on Apple Silicon / mobile NPUs
- License permissiveness for commercial use
How Ministral 8B scores on each axis
Where Ministral 8B costs you: research license restricts unmodified commercial deployment. For most teams this is acceptable on this workload, the value of the strengths above outweighs the cost. For cost-bound workloads or teams with strict latency budgets, run an eval against the next two ranked models on real data before committing.
Strengths that pay off here
- Edge-optimized
- Strong 8B-class quality
Tracked weaknesses
- Research license restricts unmodified commercial deployment
When to pick something else
If you can pay slightly more or accept slightly different tradeoffs, Llama 4 8B from Meta ranks one position higher and tends to win on the hardest cases. Meta's small Llama 4, built for on-device and edge inference.
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Other models for on-device llms
- Phi-4 for on-device llms
Microsoft's 14B model, exceptional quality-per-parameter via curated synthetic training data.
Read guide - Phi-3.5 Medium for on-device llms
14B Phi-3.5, predecessor to Phi-4 with strong benchmark efficiency for its size.
Read guide - Gemma 2 9B for on-device llms
Google's mid-2024 open-weight 9B, strong quality for its size, friendly license.
Read guide - Qwen2.5-7B for on-device llms
Small Qwen, practical default for laptop and edge inference.
Read guide - Llama 4 8B for on-device llms
Meta's small Llama 4, built for on-device and edge inference.
Read guide
Ministral 8B for other use cases
Direct comparisons
Frequently asked
Is Ministral 8B good for on-device llms?
Ministral 8B is ranked #6 on LLMDex's on-device llms list. Mistral's 8B edge model, designed specifically for on-device and on-prem deployment.How much does Ministral 8B cost for on-device llms?
Mistral has not published per-token pricing for Ministral 8B at the time of writing.What's a cheaper alternative to Ministral 8B for on-device llms?
The next ranked model on this task is SmolLM2 1.7B. Compare both before committing.When should I NOT use Ministral 8B for on-device llms?
Tracked weakness: Research license restricts unmodified commercial deployment. If that constraint is binding for your workload, the next-ranked model on this task is the safer pick.
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