The Guide to Claude Opus 4 & 4.5 API Pricing in 2026

In late 2025, Anthropic disrupted its own pricing tier with the release of Claude Opus 4.5, dramatically undercutting its predecessor, Claude Opus 4. This article provides a deep dive into the cost structure of Claude Opus 4, contrasts it with the revolutionary pricing of Opus 4.5, and offers actionable strategies—including Python code—for optimizing your AI spend.

CometAPI currently integrates Claude 4.5 Opus API, with CometAPI, you can use API via 20% off price than Anthropic 's API without the expensive subscription.

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What Is the Exact Pricing of the Claude Opus 4 API?

To understand the current market, we must first anchor ourselves in the pricing of the flagship model that defined much of 2025: Claude Opus 4.

Despite the release of newer models, Claude Opus 4 remains available via the API for legacy systems and specific reproducibility workflows. However, it carries a "legacy premium" that developers must be acutely aware of.

The Legacy Cost Structure (Opus 4 / 4.1)

As of January 2026, the standard pay-as-you-go pricing for Claude Opus 4 (and the minor update 4. 1) is:

• Input Tokens: $15.00 per million tokens (MTok)
• Output Tokens: $75.00 per million tokens (MTok)

This pricing structure reflects the immense computational overhead required by the Opus 4 architecture when it was first released in May 2025. At the time, it was the only model capable of reliable "Level 3" complex reasoning, justifying the premium.

The New Standard: Claude Opus 4.5 Pricing

On November 24, 2025, Anthropic released Claude Opus 4.5, delivering a massive price reduction alongside performance gains (80.9% on SWE-bench Verified).

• Input Tokens: $5.00 per million tokens
• Output Tokens: $25.00 per million tokens

Key Takeaway: The newer, smarter model is 66% cheaper than its predecessor. For any new integration on your aggregation platform, Opus 4.5 is the logical default, while Opus 4 serves primarily as a benchmark for legacy compatibility.

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How Does Claude Opus 4 Compare to Opus 4.5 and Competitors?

For decision-makers, raw numbers need context. Below is a detailed tabular analysis comparing the Opus family against other frontier models available in early 2026, including the Sonnet series which offers a "middle ground" for cost efficiency.

Table 1: Frontier Model Pricing Comparison (Jan 2026)

Analysis of the Data

1. The "Opus 4 Tax": Using Opus 4 in 2026 effectively incurs a 300% markup compared to Opus 4.5. A single complex coding task consuming 10k input and 2k output tokens would cost roughly 0.30** on Opus 4, but only **0.10 on Opus 4.5.
2. Output Asymmetry: Note the 5:1 ratio between Output and Input costs for Opus 4.5 (25 vs 5). This is an improvement over the 5:1 ratio of Opus 4 (75 vs 15), but the absolute savings are massive. Applications that generate long-form content (reports, code files) see the biggest benefit from migrating to 4.5.

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Why Was Claude Opus 4 So Expensive?

Understanding the expense of Opus 4 requires looking at the "Intelligence Cost Curve." When Opus 4 launched, it pushed the boundaries of Mixture-of-Experts (MoE) architectures.

1. Parameter Density: Opus 4 utilized a massive number of active parameters during inference to achieve its reasoning capabilities.
2. Hardware Scarcity: In mid-2025, H100 and Blackwell GPU availability was tighter, driving up the amortization costs passed on to API users.
3. Lack of Optimization: The "Extended Thinking" and dynamic compute allocation features introduced in Opus 4.5 were not present in Opus 4. Opus 4 applied maximum compute to every token, whereas newer models are better at routing easy tokens to cheaper experts.

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Is the High Price of Opus 4 Ever Justified in 2026?

This is a critical question for your users who might see "Opus 4" listed on your API aggregation site and assume "more expensive = better."

The short answer is: Almost never.

There are extremely niche scenarios where Opus 4 might be preferred:

• Prompt Sensitivity: If a highly complex, brittle prompt was engineered specifically for Opus 4's quirks and fails on Opus 4.5 (unlikely, but possible in rigid enterprise workflows).
• Regulatory Compliance: If a system was certified on a specific model snapshot (e.g., medical or legal advice bots locked to a validated version) and recertification is cost-prohibitive.

For 99% of developers, choosing Opus 4 over 4.5 is burning capital.

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What Are the Hidden Costs and Savings in the Anthropic API?

A professional cost analysis cannot stop at base token rates. Anthropic provides powerful levers to reduce your effective cost per million tokens, primarily through Prompt Caching and Batch Processing.

1. Prompt Caching: The Game Changer

For applications with large contexts (e.g., chatting with a 100-page PDF or a large codebase), prompt caching reduces input costs by up to 90%.

• Cache Write (First hit): 25% surcharge (e.g., $6.25/MTok for Opus 4.5).
• Cache Read (Subsequent hits): 90% Discount (e.g., $0.50/MTok for Opus 4.5).

2. Batch API

For non-urgent tasks (reports generated overnight), the Batch API offers a flat 50% discount on all token costs.

Table 2: Effective Cost Calculation (Opus 4.5)

Note: The "Total Cost" column assumes a task with 500k input and 500k output for illustration.

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How Can Developers Estimate and Control Costs?

Publishing an article on an API aggregation site requires technical substance. Below is a Python implementation that helps users calculate the cost of a request before they scale, including logic for selecting between Opus 4 and Opus 4.5.

Python Code: Smart Cost Estimator & Model Selector

This script demonstrates how to calculate costs dynamically and enforcing budget safety rails.

import math

class ClaudePricing:
    # Pricing Catalog (Jan 2026)
    PRICING = {
        "claude-3-opus-20240229": {"input": 15.00, "output": 75.00}, # [...](asc_slot://start-slot-21)Legacy
        "claude-opus-4-20250522": {"input": 15.00, "output": 75.00}, # [...](asc_slot://start-slot-23)Legacy Expensive
        "claude-opus-4.5-20251101": {"input": 5.00, "output": 25.00}, # [...](asc_slot://start-slot-25)Recommended
        "claude-sonnet-4.5-20250929": {"input": 3.00, "output": 15.00},
    }

    [...](asc_slot://start-slot-27)@staticmethod
    def calculate_cost(model_id, input_tokens, output_tokens, cached=False):
        """
        Calculates the estimated cost of an API call.
        """
        if model_id not in ClaudePricing.PRICING:
            raise ValueError(f"Model {model_id} not found in pricing catalog.")

        rates = ClaudePricing.PRICING[model_id]
        
        # Calculate Input Cost
        if cached and "opus-4.5" in model_id:
            # Approx 90% discount on input for cache hits
            input_cost = (input_tokens / 1_000_000) * (rates["input"] * 0.10)
        else:
            input_cost = (input_tokens / 1_000_000) * rates["input"]

        # [...](asc_slot://start-slot-29)Calculate Output Cost
        output_cost = (output_tokens / 1_000_000) * rates["output"]

        return round(input_cost + output_cost, 4)

    @staticmethod
    def recommend_model(budget_limit, input_tokens, estimated_output):
        """
        Recommends the best model based on a strict budget constraint.
        """
        print(f"--- Analyzing Model Options for Budget: ${budget_limit} ---")
        
        # Check Opus 4 (The Expensive Option)
        cost_opus4 = ClaudePricing.calculate_cost(
            "claude-opus-4-20250522", input_tokens, estimated_output
        )
        
        # Check Opus 4.5 (The New Standard)
        cost_opus45 = ClaudePricing.calculate_cost(
            "claude-opus-4.5-20251101", input_tokens, estimated_output
        )

        print(f"Legacy Opus 4 Cost:   ${cost_opus4}")
        print(f"New Opus 4.5 Cost:    ${cost_opus45}")

        if cost_opus45 > budget_limit:
            return "claude-sonnet-4.5-20250929", "Budget tight: Downgrade to Sonnet 4.5"
        elif cost_opus4 > budget_limit >= cost_opus45:
            return "claude-opus-4.5-20251101", "Optimal: Use Opus 4.5 (Opus 4 is too expensive)"
        else:
            return "claude-opus-4.5-20251101", "Budget allows Opus 4, but Opus 4.5 is cheaper & better."

# Example Usage
# Scenario: Processing a large 50k token document and expecting a 2k token summary
user_input_tokens = 50000
expected_output = 2000
user_budget = 0.50 # 50 cents

best_model, reason = ClaudePricing.recommend_model(user_budget, user_input_tokens, expected_output)

print(f"\nRecommendation: {best_model}")
print(f"Reason: {reason}")

Code Explanation

The code above highlights the stark reality of the pricing tiers. For a 50k input task:

• Opus 4 would cost roughly **0.90**, breaking the 0.50 budget.
• Opus 4.5 would cost roughly $0.30, fitting comfortably within budget.
  This logic is essential for users of your API aggregation site who may be automating model selection.

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What Does the "Effort" Parameter Add to the Cost?

A unique feature introduced with Claude Opus 4.5 is the effort parameter (Low, Medium, High). This allows the model to "think" longer before responding, similar to Chain-of-Thought reasoning but internal.

While the base pricing (5/25) remains the same, High Effort mode significantly increases the number of output tokens generated (as the model generates internal thought tokens).

• Standard Request: 1,000 output tokens = $0.025
• High Effort Request: Might generate 3,000 "thinking" tokens + 1,000 final tokens = 4,000 total output tokens = $0.10.

Pro Tip: When calculating expenses for Opus 4.5, always add a 2x to 4x buffer for output tokens if you plan to use the effort=high parameter for complex reasoning tasks.

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Conclusion: The Era of Affordable Intelligence

The narrative of "Claude is expensive" is outdated in 2026. While Claude Opus 4 remains one of the most expensive APIs on the market at 15/75 per million tokens, it is effectively a legacy artifact.

Claude Opus 4.5 has democratized high-end intelligence. At 5/25, it rivals the pricing of mid-tier models from 2024 while offering state-of-the-art coding and agentic capabilities.

Final Recommendations for Your API Strategy:

1. Deprioritize Opus 4: Mark it as "Legacy" on your dashboard to prevent accidental high-cost usage.
2. Default to Opus 4.5: Set this as the standard for "High Intelligence" tasks.
3. Implement Caching: If your users send repeated context (like codebases), implement prompt caching to drop input costs to near-zero ($0.50/MTok).

From the expensive Opus 4 and toward the efficient Opus 4.5, you not only save them money but also provide them with a more capable, faster, and smarter AI experience.

Developers can access Claude 4.5(Claude Sonnet 4.5 , Claude Haiku 4.5, Claude Opus 4.5) model through CometAPI. To begin, explore the model capabilities of CometAPI in the Playground and consult the API guide for detailed instructions. Before accessing, please make sure you have logged in to CometAPI and obtained the API key. CometAPI offer a price far lower than the official price to help you integrate.

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