Llama 3.1 70B vs Ministral 3 3B Instruct 2512
At a Glance
| Compare |  Llama 3.1 70BMeta |  Ministral 3 3B Instruct 2512Mistral AI |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | Not reported | $0.10Openrouter ↗ · Aug 29, 2026 |
| Output priceFrom · USD / 1M tokens | Not reported | $0.10Openrouter ↗ · Aug 29, 2026 |
| Context windowMaximum documented tokens | 131K | 262K |
| Model facts checked | Aug 28, 2026View model evidence → | Aug 28, 2026View model evidence → |
Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →
Available Benchmarks
Side-by-Side Facts
| Field | Llama-3.1-70B | Ministral-3-3B-Instruct-2512 |
|---|---|---|
| Developer | Meta | Mistral AI |
| Family | Llama 3 1 70b | Ministral 3 3b Instruct 2512 |
| Model | Llama-3.1-70B | Ministral-3-3B-Instruct-2512 |
| Version | Llama-3.1-70B | Ministral-3-3B-Instruct-2512 |
| Lifecycle | active | active |
| Released | 2024-07-23 | Unknown |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text, Image |
| Output modalities | Text | Text |
| Context window | 131K | 262K |
| Total parameters | 70.6B | 3.8B |
| Active parameters | Unknown | Unknown |
| License | llama3.1 | apache-2.0 |
| Open weights | Yes | Yes |
| API available | Yes | Yes |
| Self-hostable | Yes | Yes |
| Provider access | Hugging Face (Standard) | Openrouter (Standard) |
| Capabilities | generation | chat, generation, tools |
Llama 3.1 70B Capabilities
Ministral 3 3B Instruct 2512 Capabilities
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Llama-3.1-70BMeta | vs |  GLM-5.3Z.ai | cross-developer peerstext |
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Primary Evidence
Sources and Freshness
Questions
Llama 3.1 70B vs Ministral 3 3B Instruct 2512 FAQs
Is Llama 3.1 70B or Ministral 3 3B Instruct 2512 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Llama 3.1 70B and Ministral 3 3B Instruct 2512, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Llama 3.1 70B or Ministral 3 3B Instruct 2512?+
Only Ministral 3 3B Instruct 2512 has a directly sourced input price: $0.10 per million tokens. Only Ministral 3 3B Instruct 2512 has a directly sourced output price: $0.10 per million tokens.
Which has a larger context window, Llama 3.1 70B or Ministral 3 3B Instruct 2512?+
Ministral 3 3B Instruct 2512 has the larger sourced context window. Llama 3.1 70B supports 131K and Ministral 3 3B Instruct 2512 supports 262K.
Which performs better in benchmarks, Llama 3.1 70B or Ministral 3 3B Instruct 2512?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Llama 3.1 70B or Ministral 3 3B Instruct 2512 be self-hosted?+
Both models have the same recorded self-hosting status: supported. Llama 3.1 70B is open weight; Ministral 3 3B Instruct 2512 is open weight.
Can Llama 3.1 70B and Ministral 3 3B Instruct 2512 understand images?+
Llama 3.1 70B is not documented with image input; Ministral 3 3B Instruct 2512 is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Llama 3.1 70B or Ministral 3 3B Instruct 2512?+
Neither has a larger sourced maximum output. Llama 3.1 70B is — and Ministral 3 3B Instruct 2512 is —.
Do Llama 3.1 70B and Ministral 3 3B Instruct 2512 support reasoning and tool use?+
Llama 3.1 70B: none of these features are definitively sourced. Ministral 3 3B Instruct 2512: tool calling and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Llama 3.1 70B or Ministral 3 3B Instruct 2512?+
Llama 3.1 70B has 1 sourced provider route; Ministral 3 3B Instruct 2512 has 1, a tie.
Which offers better value, Llama 3.1 70B or Ministral 3 3B Instruct 2512?+
There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.