GPT-4o-mini
OpenAI • text+image+file->text
Context Window128K
Knowledge CutoffJul 18, 2024
GPT-4o-mini vs Gemini 1.5 Pro
Gemini 1.5 Pro for large-scale document processing, GPT-4o mini for cost-efficient image understanding.
Open model page
Gemini 1.5 Pro
Unknown • Unknown
Context WindowUnknown
Knowledge CutoffUnknown
Editorial Verdict
GPT-4o mini is the cost-effective option; Gemini 1.5 Pro is the high-capability long-context option.
GPT-4o mini and Gemini 1.5 Pro are both capable but serve different use cases. Gemini 1.5 Pro is Google's flagship with 1M token context and strong reasoning. GPT-4o mini is optimized for efficient, cost-effective general tasks. For massive context processing, Gemini 1.5 Pro wins. For cost-effective general capability, GPT-4o mini wins.
GPT-4o-mini
- Significantly lower cost per token than Gemini 1.5 Pro
- Fast inference for real-time applications
- Good multimodal performance at efficient cost
Best for: Cost-sensitive general-purpose applications and real-time tasks
Gemini 1.5 Pro
- 1M token context for processing entire large document collections
- Higher overall reasoning and multimodal benchmark scores
- Strong coding and reasoning alongside multimodal capabilities
Best for: Long-context multimodal applications requiring maximum capability
Pricing sourced from OpenRouter — updates as their catalog changes.
Pricing Comparison
All values pull from OpenRouter and update as their catalog changes.
| Metric | GPT-4o-mini | Gemini 1.5 Pro | Winner |
|---|---|---|---|
| Input (per 1M tokens) | $0.15 | Custom | 🏆 GPT-4o-mini |
| Output (per 1M tokens) | $0.60 | Custom | 🏆 GPT-4o-mini |
| Request fee | N/A | N/A | N/A |
| Image fee | N/A | N/A | N/A |
Cost Estimator
Estimate monthly billing using real OpenRouter prices.
GPT-4o-mini Total
$14.25Gemini 1.5 Pro Total
VariableSavings unavailable (pricing not published for one model).
Capability Signals
Scores are directional estimates from model metadata — not official benchmark results.
GPT-4o-miniMMLU Signal (Reasoning)Gemini 1.5 Pro
69%
N/A
GPT-4o-miniHumanEval Signal (Coding)Gemini 1.5 Pro
76%
N/A
GPT-4o-miniAgentic Tooling SignalGemini 1.5 Pro
84%
N/A
GPT-4o-miniMultimodal SignalGemini 1.5 Pro
78%
N/A
Capabilities Matrix
Feature highlights for architecture and production fit.
GPT-4o-mini Strengths
- Supports image inputs for multimodal analysis workflows.
- Strong tool-calling and structured response support.
- Balanced general-purpose profile for chat, extraction, and automation tasks.
Gemini 1.5 Pro Strengths
- Live capability metadata is currently unavailable for this model.
- Re-check after the next OpenRouter catalog refresh.
- Use provider docs for exact benchmark claims.
API Implementation
Quick start snippets for each provider style.
OpenAI (Python)
from openai import OpenAI
client = OpenAI()
response = client.chat.completions.create(
model="openai/gpt-4o-mini",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)Unknown (Python)
# Model metadata unavailable # See provider docs for API usage.
Choose GPT-4o-mini when...
- Your application includes visual reasoning and image understanding tasks.
- You rely on function calling, tool chaining, and structured output contracts.
- You want a balanced default for mixed chat and workflow automation workloads.
Choose Gemini 1.5 Pro when...
- Use Gemini 1.5 Pro when this provider is required by policy constraints.
- Validate performance with your own evaluation set before production rollout.
- Confirm final cost in the provider dashboard for your deployment region.
Frequently Asked Questions
Practical checks before selecting a production model.
Which model is better for coding?
Coding preference depends on your stack and tool-calling needs. Compare the coding signal row, test with your repository tasks, and validate latency in your target region.
Which model is cheaper at scale?
Input and output token pricing can diverge by workload profile. Use the estimator with your monthly request count and token mix to get a realistic cost difference.
Are these official benchmark numbers?
Pricing is live from OpenRouter. Benchmark rows are ModelsAtlas metadata-based signals and should be treated as directional guidance, not official leaderboard scores.
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