GPT-4o mini vs Mistral Small 3.2: API Cost Comparison
Compare the API pricing, context windows, features, and real-world cost projections for GPT-4o mini (OpenAI) and Mistral Small 3.2 (Mistral AI). Use the interactive calculator below to compute your exact monthly cost based on your token usage and request volume.
Prices verified Mar 8, 2026
Interactive Cost Calculator
Fills in typical token counts for a workload type
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Showing costs for 2 models. Cheapest: Mistral Small 3.2 at $2.50/month.
| Alert | |||||||
|---|---|---|---|---|---|---|---|
BestMistral Small 3.2 | Mistral AI | $2.50 | $0.000250 | $1.00 | $1.50 | 44.4% | Alerts coming soon |
GPT-4o mini | OpenAI | $4.50 | $0.000450 | $1.50 | $3.00 | — | Alerts coming soon |
Monthly Cost Comparison
Price Comparison at a Glance
All prices are in USD per 1 million tokens ($/M tokens). Lower is cheaper.
| Pricing Type | GPT-4o mini | Mistral Small 3.2 | Cheaper |
|---|---|---|---|
| Input (standard) | $0.15/M | $0.10/M | Mistral Small 3.2 |
| Output | $0.60/M | $0.30/M | Mistral Small 3.2 |
| Cached input | $0.08/M | N/A | — |
| Batch input | $0.08/M | N/A | — |
| Batch output | $0.30/M | N/A | — |
Prices last verified: 2026-03-06 – 2026-03-08
Cost Breakdown by Usage Volume
Estimated monthly costs at different request volumes, assuming 1,000 input tokens and 500 output tokens per request. Adjust in the calculator above for your specific use case.
| Volume | GPT-4o miniMonthly | Mistral Small 3.2Monthly | GPT-4o miniPer request | Mistral Small 3.2Per request |
|---|---|---|---|---|
| 1K requests/mo | $0.45 | $0.25 | $0.000450 | $0.000250 |
| 10K requests/mo | $4.50 | $2.50 | $0.000450 | $0.000250 |
| 100K requests/mo | $45.00 | $25.00 | $0.000450 | $0.000250 |
| 1M requests/mo | $450.00 | $250.00 | $0.000450 | $0.000250 |
Green values indicate the lower-cost option at each volume tier. Cost per request is calculated at 1,000 input + 500 output tokens using standard (non-batch, non-cached) pricing.
Price History
Input price per 1M tokensWhen to Choose GPT-4o mini
by OpenAI
- Text classification
- Customer support
- Data extraction
Input / 1M tokens
$0.15/M
Output / 1M tokens
$0.60/M
Context window
128,000
Tier
budget
When to Choose Mistral Small 3.2
by Mistral AI
- Text classification
- Data extraction
- Customer support
Input / 1M tokens
$0.10/M
Output / 1M tokens
$0.30/M
Context window
32,768
Tier
budget
Key Differences Beyond Price
Cost is only one factor in choosing an AI model. Context window size, rate limits, supported features, and latency all affect whether a model fits your use case.
| Capability | GPT-4o mini | Mistral Small 3.2 |
|---|---|---|
| Context window | 128,000 tokens | 32,768 tokens |
| Max output tokens | 16,384 tokens | 4,096 tokens |
| Performance tier | Budget | Budget |
| Vision / image input | Yes | No |
| Function calling | Yes | Yes |
| JSON mode | Yes | Yes |
| Prompt caching | Yes | No |
| Batch API (50% discount) | Yes | No |
| Extended reasoning | No | No |
| Fine-tuning | Yes | Yes |
| Rate limit (req/min) | 10,000 | Not published |
GPT-4o mini notes
Best cost-performance ratio for high-volume lightweight workloads.
Mistral Small 3.2 notes
Supports fine-tuning. Good for specialized classification tasks.
Frequently Asked Questions
Is GPT-4o mini cheaper than Mistral Small 3.2?
At standard usage (1,000 input tokens, 500 output tokens, 100,000 requests/month), Mistral Small 3.2 costs $25.00/month versus $45.00/month for GPT-4o mini — a 44% saving. Your actual savings will vary based on your token profile; output-heavy workloads amplify differences in output pricing.
Which model has a larger context window, GPT-4o mini or Mistral Small 3.2?
GPT-4o mini has a larger context window at 128,000 tokens, compared to 32,768 tokens for Mistral Small 3.2. A larger context window is important for processing long documents, multi-turn conversations, or large codebases without truncation.
Do GPT-4o mini and Mistral Small 3.2 support the Batch API?
GPT-4o mini supports the Batch API (50% discount for async processing), while Mistral Small 3.2 does not. If your workload tolerates up to 24-hour latency, routing to GPT-4o mini with batch pricing could significantly cut costs versus Mistral Small 3.2's standard rate.
Which model offers better prompt caching?
GPT-4o mini supports prompt caching at $0.08/M for cached input, while Mistral Small 3.2 does not offer prompt caching. For RAG applications or chatbots with large, repeated context, GPT-4o mini's caching capability can substantially reduce effective costs.
What are the best use cases for GPT-4o mini vs Mistral Small 3.2?
Both models are well-suited for Text classification, Customer support, Data extraction. GPT-4o mini is particularly strong for overlapping tasks. Mistral Small 3.2 is favored for similar workflows. At the same quality level, the lower-cost model is usually preferable; use this page's calculator to compare total monthly spend at your volume.
What is the cost per request for GPT-4o mini vs Mistral Small 3.2?
At 1,000 input tokens and 500 output tokens per request — a typical conversational workload — GPT-4o mini costs $0.000450 per request and Mistral Small 3.2 costs $0.000250 per request. At 100,000 requests/month, that translates to $45.00 and $25.00 respectively. Use the interactive calculator to adjust these parameters for your actual workload.
GPT-4o mini vs Mistral Small 3.2: Summary
When comparing GPT-4o mini and Mistral Small 3.2 for API cost, the right choice depends on your workload's token profile, required features, and tolerance for latency. Mistral Small 3.2 offers lower total cost at standard usage volumes (1,000 input + 500 output tokens per request at 100,000 requests/month) at $25.00/month, compared to $45.00/month for GPT-4o mini.
Both models are priced in USD per million tokens, the standard unit across all major AI API providers. GPT-4o mini charges $0.15/M for input tokens and $0.60/M for output tokens. Mistral Small 3.2 charges $0.10/M input and $0.30/M output. If your workload is output-heavy (more tokens generated than consumed as input), the model with the lower output price compounds cost savings significantly at scale.
Prompt caching is supported by GPT-4o mini but not Mistral Small 3.2. For workloads with large, repeated system prompts or document context — such as RAG pipelines or multi-turn conversations with a fixed knowledge base — prompt caching can reduce effective input costs by 60–90%, which may change the cost ranking between these two models at your specific usage pattern.
Context window capacity differs between the two: GPT-4o mini supports up to 128,000 tokens in a single request, versus 32,768 tokens for Mistral Small 3.2. A larger context window is essential for document summarization, large codebase analysis, and multi-document retrieval-augmented generation (RAG) applications.
Use the interactive calculator at the top of this page to enter your actual token usage and monthly request volume for a precise cost comparison tailored to your workload. Adjust for batch API discounts and prompt caching to find the most cost-effective option for your specific deployment.
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Relevant Use Cases
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