Codex Mini vs Ministral 3 14B
Codex Mini vs Ministral 3 14B: Ministral 3 14B is cheaper for input-heavy usage ($0.20/M vs $1.50/M input tokens), while Ministral 3 14B is better for long-context tasks (256,000 tokens).
Direct answer: choose Ministral 3 14B for lower token spend and choose Ministral 3 14B when your workload needs longer context.
Compare input and output token pricing, context windows, and monthly cost estimates on one page so you can pick the cheaper model fast.
Cost Comparison (1000 input + 500 output tokens, 100 requests/day)
Codex Mini
Ministral 3 14B
Cost Differences
Ministral 3 14B costs less than Codex Mini
Quick Recommendation
Winner for direct API pricing: Ministral 3 14B. At the default workload, Ministral 3 14B saves about $12.60/month ($153.30/year) versus Codex Mini.
Feature Comparison
| Feature | Codex Mini | Ministral 3 14B |
|---|---|---|
| Provider | OpenAI | Mistral AI |
| Input Price | $1.50/1M tokens | $0.20/1M tokens |
| Output Price | $6.00/1M tokens | $0.20/1M tokens |
| Context Window | 200,000 tokens | 256,000 tokens |
| Max Output | 32,768 tokens | 32,768 tokens |
| Category | efficient | balanced |
| Capabilities | textcodereasoning | textvision |
| Release Date | 2/2/2026 | 12/2/2025 |
Codex Mini vs Ministral 3 14B: Which Should You Choose?
Choosing between Codex Mini and Ministral 3 14B depends on your priorities: cost efficiency, context length, or raw capability. Ministral 3 14B is the more affordable option at $0.20/1M input tokens — 87% cheaper than Codex Mini. Meanwhile, Ministral 3 14B offers a significantly larger context window at 256,000 tokens vs 200,000 for Codex Mini.
These models come from different providers — OpenAI and Mistral AI — which means different API ecosystems, SDKs, rate limits, and terms of service. If you're already integrated with OpenAI, switching to Mistral AIinvolves migration effort beyond just pricing. Factor in your existing infrastructure when deciding.
These models target different tiers: Codex Mini is a efficient model while Ministral 3 14B is balanced. This means they're optimized for different workloads. Ministral 3 14B targets more demanding workloads, while Codex Mini provides a cost-effective option for everyday tasks.
Output costs matter too. Codex Mini charges $6.00/1M output tokens vs $0.20 for Ministral 3 14B. For generation-heavy workloads (content creation, code generation, summarization), output pricing often dominates your bill. Ministral 3 14B has the edge here at $0.20/1M output tokens.
Multimodal capabilities: Ministral 3 14B supports vision (image inputs) while Codex Mini is text-only. If your application needs image understanding, this narrows your choice.
Best Use Cases
Choose Codex Mini when:
- • You need more capabilities (code, reasoning)
- • You're already using OpenAI's API ecosystem
- • You're running high-volume, latency-sensitive workloads
Choose Ministral 3 14B when:
- • Budget is a primary concern
- • You need a larger context window (256,000 tokens)
- • You're already using Mistral AI's API ecosystem
Pros and Caveats at a Glance
Codex Mini
- • Input pricing: $1.50/M tokens
- • Output pricing: $6.00/M tokens
- • Context window: 200,000 tokens
- • Max output: 32,768 tokens
Watch out for
- • Higher input cost than Ministral 3 14B
- • Higher output cost than Ministral 3 14B
- • Smaller context window than Ministral 3 14B
Ministral 3 14B
- • Input pricing: $0.20/M tokens
- • Output pricing: $0.20/M tokens
- • Context window: 256,000 tokens
- • Max output: 32,768 tokens
Watch out for
- • Trade-offs are minor in this matchup.
Try Different Scenarios
Use the calculator below to see how costs change with different usage patterns
Codex Mini (OpenAI)
Ministral 3 14B (Mistral AI)
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Sign Up for Mistral AI →Frequently Asked Questions
Which is cheaper, Codex Mini or Ministral 3 14B?▼
What is the context window difference between Codex Mini and Ministral 3 14B?▼
Which model is better for AI Chatbot?▼
Which model has better overall pricing for heavy usage?▼
Where can I compare OpenAI and Mistral AI API pricing beyond this model matchup?▼
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