Ministral 3 3B Instruct 2512 vs GPT-4.1 Nano
At a Glance
| Compare |  Ministral 3 3B Instruct 2512Mistral AI |  GPT-4.1 NanoOpenAI |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.10Openrouter ↗ · Aug 29, 2026 | $0.050Openrouter ↗ · Sep 3, 2026 |
| Output priceFrom · USD / 1M tokens | $0.10Openrouter ↗ · Aug 29, 2026 | $0.20Openrouter ↗ · Sep 3, 2026 |
| Context windowMaximum documented tokens | 262K | 1,048K |
| Model facts checked | Aug 28, 2026View model evidence → | Aug 29, 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 | Ministral-3-3B-Instruct-2512 | GPT-4.1 Nano |
|---|---|---|
| Developer | Mistral AI | OpenAI |
| Family | Ministral 3 3b Instruct 2512 | Gpt 4 1 |
| Model | Ministral-3-3B-Instruct-2512 | GPT-4.1 Nano |
| Version | Ministral-3-3B-Instruct-2512 | GPT-4.1 Nano |
| Lifecycle | active | active |
| Released | Unknown | Unknown |
| Knowledge cutoff | Unknown | 2024-06-01 |
| Input modalities | Text, Image | Text, Image |
| Output modalities | Text | Text |
| Context window | 262K | 1,048K |
| Total parameters | 3.8B | Unknown |
| Active parameters | Unknown | Unknown |
| License | apache-2.0 | Unknown |
| Open weights | Yes | No |
| API available | Yes | Yes |
| Self-hostable | Yes | No |
| Provider access | Openrouter (Standard) | Openai (Standard), Openrouter (Standard) |
| Capabilities | chat, generation, tools | chat, generation, tools |
Ministral 3 3B Instruct 2512 Capabilities
GPT-4.1 Nano Capabilities
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Primary Evidence
Sources and Freshness
Questions
Ministral 3 3B Instruct 2512 vs GPT-4.1 Nano FAQs
Is Ministral 3 3B Instruct 2512 or GPT-4.1 Nano better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Ministral 3 3B Instruct 2512 and GPT-4.1 Nano, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Ministral 3 3B Instruct 2512 or GPT-4.1 Nano?+
Ministral 3 3B Instruct 2512 is $0.10 and GPT-4.1 Nano is $0.050 per million tokens, so GPT-4.1 Nano is cheaper on this metric. Ministral 3 3B Instruct 2512 is $0.10 and GPT-4.1 Nano is $0.20 per million tokens, so Ministral 3 3B Instruct 2512 is cheaper on this metric.
Which has a larger context window, Ministral 3 3B Instruct 2512 or GPT-4.1 Nano?+
GPT-4.1 Nano has the larger sourced context window. Ministral 3 3B Instruct 2512 supports 262K and GPT-4.1 Nano supports 1,048K.
Which performs better in benchmarks, Ministral 3 3B Instruct 2512 or GPT-4.1 Nano?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Ministral 3 3B Instruct 2512 or GPT-4.1 Nano be self-hosted?+
Ministral 3 3B Instruct 2512 is the only model in this pair currently marked as self-hostable. Ministral 3 3B Instruct 2512 is open weight; GPT-4.1 Nano is not marked open weight.
Can Ministral 3 3B Instruct 2512 and GPT-4.1 Nano understand images?+
Ministral 3 3B Instruct 2512 is documented with image input; GPT-4.1 Nano is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Ministral 3 3B Instruct 2512 or GPT-4.1 Nano?+
Neither has a larger sourced maximum output. Ministral 3 3B Instruct 2512 is — and GPT-4.1 Nano is 33K.
Do Ministral 3 3B Instruct 2512 and GPT-4.1 Nano support reasoning and tool use?+
Ministral 3 3B Instruct 2512: tool calling and image input. GPT-4.1 Nano: tool calling and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Ministral 3 3B Instruct 2512 or GPT-4.1 Nano?+
Ministral 3 3B Instruct 2512 has 1 sourced provider route; GPT-4.1 Nano has 2, so GPT-4.1 Nano has broader tracked availability.
Which offers better value, Ministral 3 3B Instruct 2512 or GPT-4.1 Nano?+
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.