OpenAI API Pricing Breakdown With Claude and Gemini: What Opus 4.7 Teaches Us

OpenAI API pricing is the yardstick most teams use before they even look at Claude or Gemini. The Mem0 guide on LLM cost compares these providers using current API rates, and one of the data points it shares is Claude Opus 4.6 at $5.00 input / $25.00 output per million tokens with a 200K standard context. Those numbers matter because they make the OneMux rate for Claude Opus 4.7 — $1.50 input / $7.50 output — look like a meaningful shift in the cost curve.

Focusing on Claude Opus 4.7 while keeping Gemini API pricing as the comparison angle gives us a practical way to think about AI spend: compare the same dimensions across providers, then route each request to the model that makes financial sense.

Why OpenAI API Pricing Is Still the Baseline

OpenAI API pricing gets so much attention because it was the first widely adopted per-token pricing model. Many teams still call Claude and Gemini models through an existing OpenAI SDK, and the habit of thinking in dollars per million tokens comes straight from OpenAI's rate card. The Mem0 guide follows that same convention, which is why it can place Claude Opus 4.6 beside OpenAI and Gemini models in one comparison. When you compare any two LLMs, use the same units and the same context assumptions.

Source: LLM API cost breakdown — Claude, Gemini, and OpenAI compared states that OpenAI API pricing is analyzed alongside Claude and Gemini.

Claude Opus 4.7: Where Pricing Gets Interesting

Claude Opus 4.7, available through OneMux at $1.50 per million input tokens and $7.50 per million output tokens, sits at a significantly lower price point than the Opus 4.6 rate in the Mem0 guide. According to the Mem0 guide, Claude Opus 4.6 is listed at $5.00 input / $25.00 output per million tokens with a 200K standard context. On a chat request with 100K input tokens and 4K output tokens, Opus 4.7 costs $0.18; the older Opus 4.6 rate would be $0.60. That is a useful reminder: price per token is not the whole story, but it is the base of every budget.

Worked Example: 100K Input, 4K Output

Use this Python calculation to compare any model where you know the input and output rates:

def total_cost(input_tokens, output_tokens, input_rate, output_rate):
    return input_tokens / 1_000_000 * input_rate + output_tokens / 1_000_000 * output_rate

print(total_cost(100_000, 4_000, 1.5, 7.5))  # Claude Opus 4.7: $0.18
print(total_cost(100_000, 4_000, 5.0, 25.0))  # Claude Opus 4.6 (Mem0): $0.60

The same pattern applies to Gemini API pricing and OpenAI API pricing: input and output rates are separate, and context length changes the bill. A prompt that needs a 200K context is not comparable to a short prompt at the same per-token rate.

The real cost of an LLM is not the headline rate; it is the rate times the tokens you actually send to the model.

Gemini API Pricing: The Comparison Angle That Keeps Vendors Honest

Gemini API pricing is often the third data point in the comparison. The Mem0 source includes Gemini in the same current-pricing analysis, and that is exactly how you should approach it: not as a vague alternative, but as a rate card with input, output, and context limits. If you only compare OpenAI API pricing to Claude Opus 4.7, you will miss the broader trend of falling per-token rates. If you add Gemini API pricing to the mix, you force a conversation about what you actually need: a cheaper input-heavy model, a stronger output model, or a smaller context window.

Read Gemini's rate card the same way you read OpenAI API pricing: separate input and output rates, a defined context window, and a clear unit of measurement. The Mem0 guide treats all providers with the same lens, and that discipline prevents cherry-picking.

OpenAI API Pricing and Claude Models in One Table

OneMux lists the models below with pay-as-you-go rates per million tokens. This table is a snapshot of the OneMux model catalogue, not a full rate card.

Notice that the OpenAI family spans a wide range. Gpt 5.6 Luna is the outlier on cost, Gpt 5.6 Terra lands close to Claude Opus 4.7 on input rate, and Gpt 5.6 Sol is the most expensive in this snapshot. The right choice depends on the workload, not the brand.

Use a Routing Layer to Act on Pricing Data

Rate cards are just data. The value comes when you put that data inside a routing layer. OneMux pairs access to models like Claude Opus 4.7 with one OpenAI-compatible API, key management, spend visibility, credit top-ups, and lower-cost pay-as-you-go usage. Instead of committing to one provider, you can send each request to the model that fits the task and budget.

For example, a support team might use Claude Opus 4.7 for complex summarization and Gpt 5.6 Luna for simple intent classification. A marketing team might prefer Claude Opus 4.7 for longer drafting but use a cheaper model for headline variations. The API stays consistent, and spend stays visible.

See current terms on the OneMux pricing page.

Don't Compare Pricing Without Context

Pricing comparisons go wrong when they ignore context length and output behavior. The Mem0 guide notes a 200K standard context for Claude Opus 4.6, and your own workload will probably need far less or far more. Two models with identical input rates can produce wildly different bills if one needs to retain twice as much context.

When you evaluate Gemini API pricing, OpenAI API pricing, or Claude Opus 4.7, lock the prompt size before you lock the model. Then run the same request through at least two providers and compare the dollar amount, not the marketing language.

Make the Pricing Decision Repeatable

Good cost control is not a one-time spreadsheet. It is a process:

1. Record the model, input tokens, output tokens, and prompt template version for every important use case.
2. Compare the same request on at least two providers.
3. Let cost and quality decide, not habit.
4. Review the pattern weekly or monthly.

OneMux's spend visibility makes that process practical because you can see which model consumed what share of your balance. For teams new to model routing, the OneMux quickstart is the fastest way to make a first call; the OneMux docs contain deeper guidance on keys and APIs.

Conclusion

There is no single best model price, but there is a best practice: compare OpenAI API pricing, Claude Opus 4.7, and Gemini API pricing on the same dimensions, then route requests so each model is used where it earns its price tag. OneMux gives you a practical way to do that with one OpenAI-compatible API. Start with the quickstart, let usage data drive the next decision, and adjust as model prices shift.

Sources

• Mem0: LLM API cost breakdown — Claude, Gemini, and OpenAI compared