Choosing between Anthropic and OpenAI isn't just a model quality decision — it's a cost decision. The two providers price their models differently, offer different efficiency trade-offs, and work best for different use cases.
This guide compares their pricing, models, and real-world cost implications to help you make an informed choice — or decide when to use both.
Current pricing at a glance
Here's a side-by-side comparison of the most popular models from each provider as of early 2026:
Anthropic (Claude)
| Model | Input (per 1M tokens) | Output (per 1M tokens) | Context window |
|---|---|---|---|
| Claude 4 Opus | $15.00 | $75.00 | 200K |
| Claude 4 Sonnet | $3.00 | $15.00 | 200K |
| Claude 3.5 Haiku | $0.80 | $4.00 | 200K |
OpenAI (GPT)
| Model | Input (per 1M tokens) | Output (per 1M tokens) | Context window |
|---|---|---|---|
| GPT-4o | $2.50 | $10.00 | 128K |
| GPT-4o-mini | $0.15 | $0.60 | 128K |
| o1 | $15.00 | $60.00 | 200K |
| o3-mini | $1.10 | $4.40 | 200K |
At first glance, OpenAI appears cheaper at both the high end and the low end. But raw per-token pricing doesn't tell the full story.
Price isn't cost: what actually drives your bill
Your total cost depends on three factors: price per token, tokens consumed per task, and quality-adjusted throughput (how often you need to retry or post-process).
Token efficiency varies by model
Different models produce different amounts of output for the same task. In practice, Claude models tend to be more verbose — generating longer, more detailed responses. This means:
- A summarization task might cost more on Claude simply because the output is longer.
- A classification task (short output) might be nearly equivalent in cost.
- A code generation task might favor whichever model gets it right on the first attempt.
The lesson: Compare costs per task, not per token. Run the same workload through both providers and measure the actual bill, not the theoretical pricing.
Quality affects cost through retry rates
If a cheaper model gets the answer wrong 15% of the time and you need to retry or add a human review step, the effective cost is higher than the token price suggests.
For complex reasoning tasks, Claude Sonnet and GPT-4o are roughly comparable in quality, but each has strengths:
- Claude tends to excel at long-context tasks, following nuanced instructions, and maintaining consistency across long outputs.
- GPT-4o tends to excel at structured output, function calling, and tasks requiring broad world knowledge.
Choosing the model that's naturally better at your specific task reduces retries and lowers effective cost.
Head-to-head: four common workloads
Let's compare real costs across workloads that production teams actually care about.
1. Customer support summarization
Task: Summarize a 3,000-token support conversation into a 200-token summary.
| Provider | Model | Input cost | Output cost | Total per request |
|---|---|---|---|---|
| Anthropic | Claude 3.5 Haiku | $0.0024 | $0.0008 | $0.0032 |
| OpenAI | GPT-4o-mini | $0.00045 | $0.00012 | $0.00057 |
Winner: OpenAI — GPT-4o-mini is roughly 5x cheaper for this straightforward summarization task. Both models handle it well.
2. Long document analysis (50K tokens)
Task: Analyze a 50,000-token legal document and extract key clauses (2,000-token output).
| Provider | Model | Input cost | Output cost | Total per request |
|---|---|---|---|---|
| Anthropic | Claude 4 Sonnet | $0.15 | $0.03 | $0.18 |
| OpenAI | GPT-4o | $0.125 | $0.02 | $0.145 |
Close call. OpenAI is slightly cheaper on paper, but Claude's 200K context window and strong long-context performance may mean fewer errors and less post-processing. Quality-adjusted costs are likely similar.
3. Code generation
Task: Generate a 500-line function with tests from a detailed spec (1,500 input tokens, ~4,000 output tokens).
| Provider | Model | Input cost | Output cost | Total per request |
|---|---|---|---|---|
| Anthropic | Claude 4 Sonnet | $0.0045 | $0.06 | $0.065 |
| OpenAI | GPT-4o | $0.00375 | $0.04 | $0.044 |
Winner: OpenAI on price. But code generation quality varies significantly by task. Teams often find that one model is dramatically better for their specific codebase and language. Test both — the model that requires fewer iterations wins on total cost.
4. High-volume classification
Task: Classify 10,000 customer messages (average 100 tokens each) into 5 categories (10-token output).
| Provider | Model | Total input cost | Total output cost | Total batch cost |
|---|---|---|---|---|
| Anthropic | Claude 3.5 Haiku | $0.80 | $0.40 | $1.20 |
| OpenAI | GPT-4o-mini | $0.15 | $0.06 | $0.21 |
Winner: OpenAI — GPT-4o-mini is the clear winner for high-volume, simple classification tasks. At $0.21 per 10K messages, it's hard to beat.
The multi-provider strategy
Most production teams don't choose one provider exclusively. They use both — routing different tasks to different models based on cost and quality trade-offs.
A typical setup:
| Task type | Provider | Model | Rationale |
|---|---|---|---|
| Simple classification/extraction | OpenAI | GPT-4o-mini | Lowest cost for simple tasks |
| Summarization | OpenAI | GPT-4o-mini | Cost-effective for straightforward summaries |
| Long-context analysis | Anthropic | Claude Sonnet | Superior long-context handling |
| Complex reasoning | Either | GPT-4o or Claude Sonnet | Test both, use whichever performs better |
| Bulk processing | OpenAI | Batch API + GPT-4o-mini | 50% batch discount makes this unbeatable |
This multi-provider approach typically reduces costs by 25–40% compared to using a single provider for everything.
Hidden costs to consider
Token pricing isn't the only cost. Factor in:
Prompt caching. Anthropic's prompt caching reduces input costs by up to 90% for cached prefixes. OpenAI's automatic caching reduces by 50%. If your workload has long, repeated prefixes, this significantly changes the math.
Rate limits. If you hit rate limits, you need to queue requests or upgrade your tier. Both providers have different rate limit structures — being rate-limited costs you in latency and engineering time.
Reliability. Downtime costs money. If one provider has an outage and you're single-provider, your entire AI pipeline stops. Multi-provider setups provide natural redundancy.
Migration cost. Switching providers isn't free. Different prompt formats, different behaviors, different edge cases. Budget engineering time for testing and prompt adjustments.
How to track costs across providers
Managing costs across multiple providers creates a new challenge: fragmented billing data. You need to check Anthropic's billing page, OpenAI's usage dashboard, and any other providers separately.
A unified cost dashboard solves this by:
- Pulling daily cost data from every provider's API automatically.
- Normalizing the data into a consistent format (daily spend by provider and model).
- Showing trends, breakdowns, and anomalies in a single view.
- Alerting you when costs spike or approach budget thresholds.
Without centralized visibility, multi-provider cost optimization is guesswork. With it, you can make data-driven routing decisions and catch cost anomalies the day they happen.
Making the decision
If you're choosing between Anthropic and OpenAI for a specific use case:
- Run both on your actual workload. Don't rely on benchmarks — test with your real prompts and data.
- Measure cost per task, not per token. Account for output length, retry rates, and post-processing.
- Consider prompt caching impact. If your workload has long repeated prefixes, Anthropic's 90% cache discount could flip the cost comparison.
- Factor in the full picture. Rate limits, reliability, quality consistency, and engineering time all matter.
For most teams, the answer is: use both. Route simple, high-volume tasks to GPT-4o-mini. Route complex, long-context tasks to Claude. And track everything in one place so you can optimize continuously.