OpenAI has lowered API prices for two GPT-5.6 models only three weeks after their launch. On Thursday, the company announced that GPT-5.6 Luna is now 80% cheaper and GPT-5.6 Terra is 20% cheaper, while the price for its main reasoning model, GPT-5.6 Sol, stays the same.

“Major price cuts today,” OpenAI CEO Sam Altman writes in a post on X published on Thursday. “We want to offer the best price/intelligence tradeoff at every level.”

“We want to offer the best price/intelligence tradeoff at every level.”

Luna now costs $0.20 for a million input tokens and $1.20 for a million output tokens, down from $1 and $6. Terra is priced at $2 per million input tokens and $12 per million output tokens, reduced from $2.50 and $15. Sol’s price stays at $5 per million input tokens and $30 per million output tokens.

Developers using Luna do not need to change their processes, but their inference costs will go down. High-volume tasks will now be much cheaper to run, without requiring any code updates or model changes.

major price cuts today:

*80% drop for GPT-5.6 Luna, now $0.20 per million input tokens and $1.20 per million output
*20% drop for GPT-5.6 Terra, to $2/$12
*GPT-5.6 Sol gets Fast mode in the API, up to 2.5x the speed for 2x the price, same intelligence pic.twitter.com/erC6u4VoDR

— Sam Altman (@sama) July 30, 2026

This timing is unusual because AI vendors usually keep prices steady for several months after launching a new model family. OpenAI cut prices less than a month after GPT-5.6 became available on July 9.

…serving costs can be more important than small differences in benchmark performance between models.

Infrastructure gains drive savings

The company says these price cuts were possible because of improvements to the infrastructure behind GPT-5.6, which lets the company offer “substantially more intelligence per dollar.”

These infrastructure upgrades were expected, however. A day before the price announcement, OpenAI shared an engineering overview that explained optimizations across the inference stack for Codex and ChatGPT Work.

GPU kernels rewritten for efficiency

OpenAI engineers rewrote the production GPU kernels, cutting serving costs by about 20%. They also redesigned Sol’s speculative decoding system, making token generation over 15% more efficient. The company updated its agent runtime as well, reducing repeated prompt computation by using prompt caching more during multi-step workflows.

Agents amplify inference costs

Lately, developers are paying more attention to inference costs since agents often make dozens or even hundreds of model calls to finish a single task. For these workloads, serving costs can be more important than small differences in benchmark performance between models.

The elephant in the room is that the competition has intensified from overseas. Lower-cost open-weight models from Chinese AI companies like Moonshot are pushing commercial providers to show not just better performance, but also better pricing for production use. OpenAI and Anthropic know that leaning on performance just isn’t an option anymore, which is pushing them to match Chinese prices.

The issue here is that most of those steps don’t need a model like Sol, and Chinese labs have figured out how to pack better capabilities into efficient models; a helpful option for companies running through billions of tokens a day.

The ability to send the easy tasks to open models and save the pricey APIs for the tough stuff makes a difference; OpenAI is banking on its 80% price cut on Luna to narrow that gap. Suddenly, switching to self-hosted models doesn’t look worth the hassle.

Competition reshapes model pricing

These pricing changes come as both OpenAI and Anthropic keep adjusting the economics of their newest model families — a dynamic that played out across three companies in a single week earlier this month. Earlier this week, OpenAI raised GPT-5.6 Sol usage limits for ChatGPT Work and Codex after finding that long coding sessions used up allowances faster than expected. Anthropic has also made pricing changes and added premium inference tiers as enterprise customers move bigger agentic workloads into production.

This announcement highlights a trend in the industry for infrastructure. Now, every percentage point of serving efficiency can lead directly to lower API prices, turning cost optimization into a competitive advantage instead of just an engineering goal.

The post OpenAI slashes API costs amid rising global competition appeared first on The New Stack.

Moonshot AI has released the open weights for Kimi K3 on Hugging Face, giving developers access to one of the largest open-weight language models yet. The Monday release follows a wave of overwhelming demand that forced Moonshot to temporarily pause new API subscriptions. Now, organizations with the necessary hardware can deploy K3 themselves.

In its documentation, Moonshot describes the model as being built for “long-horizon coding and end-to-end knowledge work.” Another notable detail is that Kimi K3 uses an OpenAI-compatible API. Because teams can try the model without rebuilding their existing integrations, switching to K3 could be as simple as changing the endpoint and model name. 

For engineers who have already built around OpenAI-compatible SDKs, that makes it much easier to evaluate K3 alongside existing commercial models. Taken together with the one-million-token context window, it’s clear the company is targeting engineering teams that already build around models like Claude Fable 5 and OpenAI’s GPT-5.6 Sol. While K3 is openly available, running it is another matter.

While K3 is openly available, running it is another matter.

Kimi K3: Its massive size and requirements mean few will be able to run it

The model uses a 2.8-trillion-parameter Mixture-of-Experts (MoE) architecture and ships in the hardware-friendly MXFP4 format. The weights alone occupy roughly 1.4 TB of storage, and practical self-hosted deployments require a distributed GPU environment — realistically eight or more servers equipped with eight NVIDIA H100 or B200 accelerators each.

That changes the conversation around open-weight AI. As The New Stack recently noted, the case for ownable models has grown stronger after Anthropic’s Fable 5 was pulled offline by a Commerce Department directive, a warning that access is not ownership.

Releasing the model weights and technical report of Kimi K3.

Kimi K3 is our most capable model: a 2.8T MoE model with native visual understanding and a 1M-token context window.

New model architecture: 2.5x the intelligence per unit of compute, not just more params.

Alongside… pic.twitter.com/Yz5uWeMbIm

— Kimi.ai (@Kimi_Moonshot) July 27, 2026

Ownership versus API economics

Instead of paying recurring API costs to OpenAI or Anthropic, organizations trade those operating expenses for significant investments in GPUs, networking, storage, power and operational expertise. That benefit is control. 

For organizations operating under strict regulatory requirements, the trade-off may justify the infrastructure investment. For many others, managed APIs will potentially remain the more economical option. The economics of open-weight models at enterprise scale remain an active area of debate across the industry.

Moonshot positions K3 as a frontier-class model capable of competing with OpenAI’s GPT-5.6 Sol and Anthropic’s Claude Fable 5 on a variety of public benchmarks.

Benchmarks versus real workloads

The developer community is already taking notice of these coding capabilities. As MindStudio recently noted, “If you want to understand why developers are paying attention to Kimi K3, the benchmark to look at is SWE-bench Verified… For most of its history, SWE-bench has been dominated by proprietary models.”

The company’s own documentation is notably candid about its ongoing limitations. K3 always runs with reasoning enabled and defaults to its highest reasoning-effort setting, though Moonshot has since added lower-effort tiers. It may also behave too proactively when prompts are ambiguous. Moonshot also cautions that switching models within an ongoing conversation can reduce response quality.

That type of transparency is refreshing, but it additionally reinforces that benchmark scores shouldn’t drive deployment decisions. Early hands-on comparisons, such as The New Stack‘s Fable 5 vs. K3 coding match-up, suggest K3 can match Fable 5 on programming tasks at roughly a third of the cost, but runs about four times slower.

Organizations evaluating K3 still need to test it against their own workloads. But early community sentiment shows promise; open-source developers are already successfully utilizing K3 for complex, system-level tasks like porting the Godot game engine to WebGPU.

Morningstar senior equity analyst Malik Ahmed Khan echoed that overall wariness about benchmarks. “While K3 constitutes progress, we’d hesitate to ascribe it near-parity with American frontier models, such as Fable 5, in actual tasks,” Khan writes in a research note published before the release of the model weights on Monday.

Geopolitical risks loom large

K3 also arrives under growing geopolitical scrutiny. Anthropic and U.S. officials have accused Moonshot AI of distilling outputs from American frontier models during training. Anthropic’s Head of Public Policy Sarah Heck characterized the practice as intellectual property theft, while White House Office of Science and Technology Policy Director Michael Kratsios publicly alleged Moonshot relied on Anthropic’s models during development.

Moonshot has denied the allegations. Huang Zhenxin, Moonshot’s head of enterprise business, told Chinese state media that K3’s performance gains stem from architectural enhancements — specifically Kimi Delta Attention and Attention Residuals — not distillation. Some industry analysts have also questioned whether the timeline supports large-scale distillation, noting that Fable 5 had only been publicly available since July 1 before K3 appeared on July 16.

Whether those claims are ultimately substantiated or not, they bring another consideration for enterprise buyers. Beyond performance and infrastructure costs, organizations evaluating K3 may also have to consider future compliance, procurement, and regulatory risks.

K3 matters because of where it’s aimed. Moonshot isn’t building another consumer chatbot; its documentation makes clear this model was built for enterprise coding agents, heavy knowledge work, and production systems. The fact that demand blew past Moonshot’s GPU capacity within 48 hours says it all: at this scale, infrastructure pressure is guaranteed, whether you’re making API calls or hosting the weights yourself. 

The post Moonshot opens Kimi K3 weights — but few can run it appeared first on The New Stack.

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