GPT-4o mini
OpenAI • Text + Vision
Context Window128K
Knowledge CutoffUnknown
GPT-4o mini vs Claude 3.5 Haiku
GPT-4o mini for OpenAI ecosystem integration, Claude 3.5 Haiku for Anthropic reliability in high-volume applications.
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Claude 3.5 Haiku
Unknown • Unknown
Context WindowUnknown
Knowledge CutoffUnknown
Editorial Verdict
GPT-4o mini offers much higher capability at minimal extra cost; Haiku is the ultra-budget option for simple tasks.
GPT-4o mini and Claude 3.5 Haiku are both excellent small models optimized for speed and cost. GPT-4o mini is the more capable of the two, outperforming Haiku on most benchmarks while maintaining low cost. Claude 3.5 Haiku is the fastest and cheapest, making it ideal for high-volume, simple tasks. For anything beyond basic extraction or classification, GPT-4o mini is the better choice.
GPT-4o mini
- Significantly higher capability on reasoning, coding, and instruction-following benchmarks
- 128K context window — 4x larger than Haiku's 32K for longer document processing
- Better at complex multi-step tasks despite being a 'mini' model
Best for: Complex mini tasks, longer document processing, and applications needing strong capability at low cost
Claude 3.5 Haiku
- Fastest inference of any capable model — ideal for real-time, latency-sensitive applications
- Lowest cost per token of any frontier-tier capable model
- Excellent for simple extraction, classification, and summarization at scale
Best for: Ultra-high-volume simple tasks, real-time extraction, and maximum cost savings
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 | Claude 3.5 Haiku | Winner |
|---|---|---|---|
| Input (per 1M tokens) | Custom | Custom | N/A |
| Output (per 1M tokens) | Custom | Custom | N/A |
| 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
VariableClaude 3.5 Haiku 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)Claude 3.5 Haiku
69%
N/A
GPT-4o miniHumanEval Signal (Coding)Claude 3.5 Haiku
69%
N/A
GPT-4o miniAgentic Tooling SignalClaude 3.5 Haiku
69%
N/A
GPT-4o miniMultimodal SignalClaude 3.5 Haiku
78%
N/A
Capabilities Matrix
Feature highlights for architecture and production fit.
GPT-4o mini Strengths
- Supports image inputs for multimodal analysis workflows.
- Balanced general-purpose profile for chat, extraction, and automation tasks.
Claude 3.5 Haiku 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 want a balanced default for mixed chat and workflow automation workloads.
- You can measure quality with your own benchmark and prompt set.
Choose Claude 3.5 Haiku when...
- Use Claude 3.5 Haiku 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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