Real-World Test: Codex Pro's Hidden Value Delivers 62x Leverage Over DeepSeek

A Misunderstood Pricing: Is GPT-5.6 Really Expensive?

When people think of OpenAI, the first reaction is often "expensive." The official API pricing for the GPT-5.6 series—$5 per million input tokens and $30 per million output tokens—has indeed deterred many developers.

To understand this pricing, you first need to understand what tokens are. Tokens are the basic units that large language models use to process text, roughly corresponding to 3/4 of an English word or about 4 characters (Chinese is approximately 1.5-2 characters per token). OpenAI uses separate billing for input and output because their computational cost structures differ: input tokens require full attention calculation, while output tokens also involve autoregressive generation requiring multiple forward passes, making their marginal cost higher. That's why output pricing is typically 4-8x that of input. Prompt Caching is an important API optimization in recent years: when the same prefix is reused multiple times, the server can directly read the KV Cache and skip recomputation, reducing costs to 10-20% of normal input.

By contrast, Chinese models like DeepSeek, Kimi, and GLM have long been regarded as the "price-performance kings."

But a Bilibili content creator named TuTu conducted a systematic real-world test and reached a counterintuitive conclusion: If you convert the subscription-based Codex Pro quota at official API rates, GPT-5.6 actually becomes "the cheapest model in the world" at equivalent flagship capability levels. This sounds like clickbait at first, but there's a complete calculation framework worth dissecting.

The core variable lies in a series of adjustments OpenAI made to Codex quotas: 9 global quota resets occurred, plus 2 user-triggered backend resets, totaling 11 additional quota recovery opportunities. These "resets" aren't minor top-ups of message counts—regardless of how much weekly quota has been consumed, it gets pulled back to 100%. Additionally, Codex removed the original hourly rate limit, retaining only the weekly limit.

Test Methodology and Token Calculation: How Much Is One Weekly Pool Actually Worth?

To obtain real data, the creator employed strict single-variable control: one account, one machine, no consumption on other devices or quota pools, using GPT-5.6 at the highest tier, standard speed, with multi-agent collaboration disabled.

Here we need to explain Codex's quota mechanism. ChatGPT Pro is OpenAI's top-tier subscription at $200/month, and Codex is an Agent system built on GPT-5.6 specifically optimized for software engineering tasks. It supports autonomous execution of code generation, debugging, refactoring, and other operations in the cloud, with access to terminals, browsers, and other tools. Its quota uses a Weekly Pool mechanism: refreshing once per calendar week, measured in Credits, where 1 Credit roughly corresponds to a specific amount of API compute consumption. The removal of hourly limits further opened up possibilities for continuous operation, which is especially critical for multi-agent workflows requiring sustained long-running execution.

Before one reset, he consumed the weekly quota down to less than 5% remaining, then exported all normal input, cached input, and output tokens for that period, converting them at Codex's official Credit rate. The results showed: this 95% consumption used approximately 19,687 Credits, extrapolating a full weekly pool to about 20,723 Credits.

Calculated at GPT-5.6's official API pricing, one complete weekly pool equates to approximately $829 in API consumption. This number alone tells the story—actual usage within a single subscription period far exceeds the subscription fee itself.

The 62x Leverage Behind a $200 Subscription

What's truly staggering is the monthly cumulative total. With 4 normal weekly pools plus the additional 11 recovery opportunities, full capacity yields 15 complete weekly pools.

15 weekly pools equals approximately 310,848 Credits. At 4 cents per Credit in API value, this ultimately equals approximately $12,434. In other words, if every pool in the $200 ChatGPT Pro subscription is fully utilized, converting to GPT-5.6's official API pricing yields over $12,400—a full 62x leverage.

From a token perspective, the numbers are equally staggering: based on real usage ratios, one complete weekly pool can process approximately 1.1 billion tokens, and 15 weekly pools amounts to roughly 16.6 billion tokens. Of these, normal input accounts for about 647 million, cached input about 15.93 billion, and output about 41.17 million. The extremely high proportion of cached input is because Codex heavily reuses system prompts and code context in multi-turn Agent conversations, with cache hit rates far exceeding normal chat scenarios—this is the key reason why the converted price is exceptionally low. Spreading the $200 across all of this, GPT-5.6's effective price becomes approximately 8 cents per million normal input tokens, 0.8 cents per million cached input tokens, and 48 cents per million output tokens.

Calculated comprehensively based on the real input/output structure, every million processed tokens costs only about 1.2 cents.

Codex Pro vs. Chinese Models: DeepSeek Isn't Cheap Anymore

What does this number mean in market context? The creator provided direct comparisons.

To understand this comparison, you need to know the pricing background of Chinese LLMs. In 2024-2025, Chinese LLM companies launched a fierce price war as their primary competitive strategy. DeepSeek challenged OpenAI head-on with open-source plus ultra-low pricing; Moonshot AI (Kimi), Zhipu AI (GLM), and others also introduced dramatic price cuts. The underlying logic of this price war: domestic companies massively reduced inference costs through proprietary optimizations (such as MLA attention mechanisms and MoE sparse architectures) while subsidizing to gain market share.

• Kimi K3: Normal input at $3/million and output at $15/million—37x and 31x more expensive respectively;
• GLM-5.2: Normal input at $1.4/million and output at $4.4/million—17x and 9x more expensive respectively;
• DeepSeek V4 Pro: Even the "price killer" charges $0.435/million for normal input and $0.87/million for output—still about 5.4x and 1.8x more expensive.

Applying the real input/output structure from this test to DeepSeek V4 Pro, the same workload would cost approximately $375, while Codex Pro costs only $200.

In other words, if the comparison is locked to "equivalent flagship model + first-party hosted service + complete Agent capabilities" and calculated based on actual quota resets, GPT-5.6's comprehensive usage cost is so low it's virtually unmatched.

Key Caveats and Risk Warnings: Can 62x Leverage Be Sustained?

It must be emphasized that this calculation is built on a highly idealized premise: the extreme upper limit of 4 normal cycles + 9 global resets + 2 backend resets all fully utilized.

The creator candidly acknowledged that the biggest uncertainty is—there's no way to know when OpenAI will trigger resets. Frequent resets don't mean OpenAI promises to do this every month. These quota restorations are more like temporary operational adjustments rather than stable product commitments. From a business logic perspective, OpenAI's frequent global quota resets are essentially a demand-side management tool: releasing more compute when server resources are abundant, boosting user satisfaction without impacting the overall profitability structure.

Therefore, this "62x leverage" and "1.2 cents per million tokens" represents an extreme theoretical upper limit that ordinary users cannot reliably reproduce in daily use. However, looking at the trend, OpenAI has "indeed always been resetting"—and that's the basis for the creator's confidence in making this recommendation.

Conclusion: Subscription Plans Are AI Models' True Pricing Weapon

The biggest takeaway from this real-world test isn't that GPT-5.6's API is cheap—quite the opposite, its API pricing is expensive. What truly drives the price down is the $200 Pro subscription, Codex's massive weekly quota, and the multi-agent capability enabled by removing hourly limits.

Multi-Agent Collaboration is the core scenario where this leverage is fully realized. When multiple AI instances process code generation, testing, documentation, and review subtasks in parallel, token consumption is extremely high but output density increases proportionally—perfectly matching the weekly pool mechanism's "use it all to maximize value" logic.

For power users who frequently use AI for work and coding tasks, subscription-based Codex offers clear advantages in both stability and cost. For beginners who haven't yet tried Agent-based programming, free web-based Agent tools can provide initial hands-on experience, with the ability to download source code locally after building.

This discussion about the "cheapest model" fundamentally reveals an industry trend: As model capabilities converge, the competitive core is shifting from unit pricing to subscription quota operational strategy. When GPT-4-level capability has become the baseline and gaps between models narrow, the generosity of subscription quotas, native support for Agent workflows, and ecosystem completeness (IDE plugins, CLI tools, third-party integrations) are replacing raw model capability as the core decision variables for developers choosing platforms. Whoever can pack more real compute into their subscription will win the "best value" mindshare among developers.