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    3. JPMorgan Analysis: Token and H100 Prices Drop Together, Is AI Cost Cooling Down?

    JPMorgan Analysis: Token and H100 Prices Drop Together, Is AI Cost Cooling Down?

    By: www.theblockbeats.info|2026/07/28 08:31:02
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    TL;DR
    · A July 2026 industry tracking report shows that the prices of input Tokens and H100 short-term rentals have both declined.
    · This price drop occurs against a backdrop of several-fold increases in usage and still high GPU utilization rates.
    · Cost pressures have eased, but the supply of output Tokens, HBM, and high-end clusters still limits the extent of price reductions.


    A report from JPMorgan's "Data Center Watch" released in July shows that the prices for AI inference calls and high-end GPU short-term rentals cooled down simultaneously at that time.


    The direct implication of this clue is that the most strained phase of AI infrastructure is beginning to ease. For ordinary users and enterprise clients, the drop in input Token prices will make calling large models cheaper. For AI application companies and cloud providers, the decline in H100 rental prices indicates that the computing power market is no longer as congested as it was during the previous shortage period.


    However, this is not a signal of waning demand. The tracking sample shows that the processing volume of LLM Tokens has increased several-fold since the beginning of the year, with the utilization rates of leading models still in the 70%-80% range, and high-performance GPU utilization exceeding 80%. The cooling of prices seems more like a response to supply, open-source models, and inference efficiency catching up with demand, rather than a sudden weakening of AI demand.


    Slight Decline in Token Prices, Usage Still Expanding


    In July 2026, the input Token prices of mainstream models continued to decline. According to the report's sample, the price for some mainstream models per million input Tokens roughly falls in the range of $0.2-5, with an average month-on-month decrease of about 3% in July.


    ![](https://public.chaincatcher.info/upload/news/202607/c46305d0d77c48beb28f63b14a76e491.png)

    This range needs to be qualified. The output Token prices of high-performance closed-source models like GPT-4o and Claude 3.5 Sonnet are usually significantly higher than input Token prices, and the prices of lightweight models like GPT-4o mini cannot be directly compared with flagship models. In other words, the lower prices in the report reflect more on input Tokens, lightweight models, or hosting samples, and should not be interpreted as all mainstream models having their complete calling costs reduced to the same range.


    Even so, the direction is clear. For AI application companies, the unit inference costs for the same Q&A, code generation, search enhancement, or customer service calls are decreasing. One of the hardest calculations in the commercialization of large models over the past few years has been that "the more you use, the more you lose," and the decline in input Token prices at least brings some high-frequency applications closer to an affordable cost range.


    More importantly, the price drop occurs against a backdrop of increasing call volumes. From the beginning of 2026 to July, the processing volume of LLM Tokens in the report's sample has achieved several-fold growth, with some niche segments growing over 4 times. The utilization rates of leading models from OpenAI, Anthropic, and Meta remain stable at high levels of 70%-80%, and the release of open-weight models in the Llama series is also a significant reason for the increase in call volumes.


    What the market sees is not "no one is using it, so the price drops," but rather that under greater call volumes, model service providers, the open-weight ecosystem, and infrastructure supply have collectively pushed down unit prices.


    Open-Weight Models No Longer Competing Solely on Price


    The rapid growth of open-weight models is another main theme of the Token market in July.


    The report shows that the Token usage of open-weight models increased by 26% month-on-month in July, reaching 63 times the same period last year; their volume-weighted average price increased by 36% month-on-month, and Token spending rose by 71% month-on-month.


    This indicates that open-weight models are not simply squeezing closed-source models through low prices. As performance gradually approaches the cutting edge, the calling prices, usage, and spending of some open-weight models are increasing in tandem.


    In July, the spending share of open-weight models rose to 20%, up from 12% in June and 10% in May. Although closed-source models only account for 29% of the total Token volume, they still contribute 80% of the spending, indicating that high-performance closed-source models still hold most of the commercial value, but open-weight models are rapidly catching up.


    Based on Token usage, the top five models in July were MiMo v2.5, DeepSeek v4 Flash, GLM 5.2, DeepSeek v4 Pro, and MiniMax M3, collectively accounting for 50% of the total Token volume.


    Based on Token spending, the top five were Claude Opus 4.8, Claude Opus 4.7, Fable 5, Kimi K3, and GPT-5.6 Sol, together accounting for 54% of total spending. Notably, Kimi K3 entering the top five in spending indicates that open-weight models are entering high-value segments previously dominated by closed-source models.



    For enterprise clients, the increase in selectable models helps reduce dependence on a single closed-source vendor. However, from this report, the changes brought by open-weight models are not just about lowering prices, but also about competing for higher-value calling scenarios and budgets.


    H100 Rental Prices Decline, But High-End Cards Are Not in Oversupply


    The GPU leasing market has also seen similar structural differentiation.


    In July 2026, the average rental price of H100 in the non-hyperscale cloud vendor market was $2.68 per GPU hour, a month-on-month decrease of 1.1%. This marks the first month-on-month decline after seven consecutive months of rising H100 rental prices.


    This drop is far less than 15% to 25% and cannot be described as "H100 rental prices have dropped by 20%". The report tracks the average monthly rental price calculated per GPU hour, not the weekly rental price per card.


    This indicates that the supply side is beginning to respond. More GPUs are entering the cloud leasing market, intensifying competition among cloud service providers and computing power platforms, and short-term rental prices are no longer solely determined by extreme shortages. Compared to the phase of high-end GPU scarcity, the pressure of not being able to buy, rent, or queue for cards has eased somewhat.


    However, high-end resources are still not cheap. H100 prices remain significantly higher than A100, and overall GPU utilization remains at 75%-90%, with high-performance GPU utilization exceeding 80%. As long as utilization remains in this range, the decline in rental prices seems more like the elimination of some scarcity premiums rather than an indication that computing power supply is fully in excess.



    For AI companies, this change brings two layers of impact.


    First, the cost of inference business is easier to decrease. Inference requires continuous, stable, low-latency computing power supply, and the decline in GPU rental prices will improve the cost structure for API providers, AI search, code assistants, and enterprise intelligent agent products.


    Second, the cost pressures of training large models are difficult to disappear simultaneously. High-end training relies not only on the number of GPUs but also on cluster interconnect, memory bandwidth, scheduling efficiency, and stable power supply. A decline in H100 rental prices does not equate to a proportional decrease in large-scale training costs, nor does it mean that all AI startups can obtain the same quality of cluster resources at low prices.


    -- Price

    --
    --
    --

    Memory Price Increases Slow, High-End Cluster Price Reductions Are Not as Direct as Short-Term Rentals


    The least thorough aspect of price loosening is in memory.


    In May-June 2026, the report sample showed that DRAM spot prices rose rapidly, with categories like DDR5 16Gb experiencing cumulative increases of over 90%-100%, stabilizing only by July. In contrast, HBM remains high due to demand from AI accelerators, with contract prices lagging behind spot prices by 1-2 quarters.


    For high-end AI accelerators, GPU chips themselves are not the only bottleneck; HBM, advanced packaging, and data center delivery all affect real supply. Even if some GPU prices in the leasing market have dropped, upstream memory and high-end cluster deliveries will still limit the overall speed of cost reductions.



    This is why saying "the tightest moments of computing power are beginning to ease" cannot be directly written as "the computing power shortage has ended." The drop in leasing market prices indicates that some short-term supply pressures have eased. However, high-end training and large-scale inference clusters are still constrained by memory bandwidth, advanced packaging, and the pace of new-generation GPU shipments.


    It is important to note the timing. This report reflects the price and utilization changes in the sample from July 2026, making it more suitable as a snapshot of the easing of AI computing power tightness in mid-2026, rather than a summary of prices across all cloud vendors, regions, and long-term contracts.


    Costs Have Decreased, but Model Vendors' Profit Pressures Have Changed Form


    The decline in Token and GPU leasing prices is beneficial for AI application companies, but it may not be entirely good news for model service providers.


    Lower calling prices help stimulate demand, expand API usage, and allow more enterprises to integrate large models into real business processes. The issue is that if prices drop faster than inference efficiency improves, the profit margins of model vendors and cloud platforms will be squeezed. Especially in the context of open-weight models catching up and customers' bargaining power increasing, the high price space of closed-source APIs will continue to face challenges.


    For hardware and cloud service providers, the decline in H100 short-term rental prices also signifies the normalization of the extremely high returns that came from extreme shortages. Long-term contracts, regional differences, batch sizes, and variations in spot price calculations can lead to significant differences in the real prices different customers receive. Looking solely at the average weekly rental price cannot directly infer that all cloud vendors' revenues will decline simultaneously.


    The clearest signal from this price tracking is that by mid-2026, the supply of AI infrastructure has caught up with part of the demand, and unit calling and short-term rental costs have begun to cool down. However, the boundaries are also clear: Token usage is still growing rapidly, high-end GPU utilization remains high, output Token prices are more expensive, and HBM remains tight. The cost reductions first occur in areas that are easier to market, while the most challenging to loosen remain high-end clusters and upstream memory supply.

    This content is provided for general informational purposes only and doesn't constitute financial, investment, legal, or tax advice. Any events, rewards, online promotions, or related information mentioned herein should not be considered a recommendation, solicitation, or invitation to purchase, sell, trade, or otherwise deal in any crypto assets. Crypto assets are highly volatile and may result in loss. The availability of WEEX services, products, and related events may vary by region. You are responsible for ensuring that your participation is in accordance with applicable local laws and regulations.

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    Contents

    Slight Decline in Token Prices, Usage Still Expanding
    Open-Weight Models No Longer Competing Solely on Price
    H100 Rental Prices Decline, But High-End Cards Are Not in Oversupply
    thena
    Memory Price Increases Slow, High-End Cluster Price Reductions Are Not as Direct as Short-Term Rentals
    Costs Have Decreased, but Model Vendors' Profit Pressures Have Changed Form

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