Open Weight Models Explained: IBM Says 90% as Good, Cheaper

On IBM's third-quarter earnings call, reported by The Register on October 22, 2026, CEO Arvind Krishna made a blunt argument: open weight models, the AI models anyone can download and run on their own hardware, are now good enough and cheap enough for most everyday business work. He put their price at 10 to 25 percent of frontier token prices and called them "90 percent as effective."

What is an open weight model, and how does open weight vs open source differ? This piece answers both.

Keep in mind who is talking. That 90 percent figure is a CEO's claim on an earnings call, not a lab result, and IBM sells the hardware and software to run these models. Still, independent benchmark data points the same way: the gap between the best closed models and the best open ones is real, but it is now measured in months, not years.

By the end you will know why the label is more than a wording quibble, where closed models still win, and how to decide which kind fits your team.

What Is an Open Weight Model?

An AI language model is, at bottom, a very large file of numbers called weights (you will also hear "parameters"). During training, a lab feeds the model huge amounts of text and nudges those numbers over and over until the model gets good at predicting what comes next. The finished weights are the model. Everything it has learned lives in them.

An open weight model is one whose finished weights the developer publishes for anyone to download. You can run it on your own servers, on a rented cloud GPU, or, for the smaller ones, on a decent laptop. You can also fine-tune it, which means training it a little further on your own data so it gets better at your contracts, your support tickets or your codebase.

A closed model works the other way round. The weights never leave the company's servers. You reach the model through an API: you send text in, text comes back, and you pay per token (a token is roughly a fragment of a word). You never touch the model itself.

What is actually in the download?

When a lab releases an open weights model, you typically get the weight files, a description of the model's architecture so software knows how to load them, and a licence that spells out what you may do. Free tools such as vLLM or Hugging Face's libraries handle the job of actually running it, a step called inference.

What you usually do not get is the recipe: the training data, the full training code and the many choices about filtering and tuning that shaped the model's behaviour. Think of it as receiving a finished cake and the right to slice it, re-ice it and serve it, without the recipe or a list of where the flour came from.

Open Weight vs Open Source: Why the Label Matters

Plenty of headlines call these models "open source." In most cases that is loose language. In software, open source means you get the source code and the freedom to study, change and share it. The closest AI equivalent would include the training data and training code, so anyone could rebuild the model from scratch and check how it was made. Most open-weight releases stop at the weights.

Licences add a second wrinkle. Some open weight releases come with permissive terms, while others attach conditions on commercial use or on what the model may be used for. Where exactly "open" begins for AI is still argued over, so treat the word as a starting point and read the licence before you build a product on any model.

What IBM's CEO Said About Open Weight Models

Krishna framed his case as a story of a closing gap. In 2024, he said, closed frontier models were clearly superior: on a broad benchmark, the top model might score 70 while an open model managed 20 or 30. By late 2025, by his account, that had narrowed to roughly 70 versus 50.

Today, he argued, open-weight models cost 10 to 25 percent of frontier token prices while being, in his words, "90 percent as effective." His point was less about benchmarks than about the work companies actually do. Summarizing emails, reviewing contracts and handling customer calls, he said, do not need the most expensive model on the market. In his view, many businesses can build useful on-prem AI in two or three racks of servers.

He was also candid about the catch. According to The Register's report, Krishna said enterprises struggle to run these systems because doing it well takes kernel engineers and network specialists, people most companies do not employ. IBM plans software to make deployment easier, and it expects on-prem demand to lift sales of its Spyre accelerator cards for mainframes. So the pitch is honest about the hard part, and IBM is positioned to sell the fix.

Are Open Weight Models as Good as Closed Models?

Short answer: close, not equal, and it depends on the job.

The FutureSearch analysis of mid-2026 leaderboards gives the clearest picture. On the Artificial Analysis Intelligence Index, Claude Fable 5 scores 60 and GPT-5.6 Sol scores 59, while the best open-weight model, GLM-5.2 (max), scores 51, about 9 points behind. On LMArena's text leaderboard, where people vote on which answer they prefer, Claude Fable 5 leads with an Elo rating of 1507; the top open model, GLM-5.1, sits around 27th place at 1471.

Time is the other way to measure it. By FutureSearch's count, open models have trailed the closed frontier by roughly four months on average since January 2026. A May 2026 evaluation by CAISI at NIST put the lag for DeepSeek V4 Pro closer to eight months on less public benchmarks. That difference matters: tests that are widely published can be studied and tuned for, so private tests may give a more sober reading.

On some narrow tasks the race is a near tie. A June 2026 comparison reports DeepSeek V4-Pro at about 80.6 percent on SWE-bench Verified, a coding benchmark built from real software bugs, against roughly 80.8 percent for Claude Opus 4.6. That is a single blog source, so treat it as a direction rather than a verdict.

Where the gap still shows

According to one blog write-up, closed models still tend to lead in three areas: following complex instructions with many constraints at once, handling multimodal input such as images alongside text, and staying reliable over long, multi-step agent tasks. Treat that as directional: it is a single blog-level source, not a benchmark result.

There is also a strategic reason the very top may stay closed. FutureSearch notes that Meta is keeping frontier models such as Muse Spark 1.1 proprietary, and that OpenAI's open-weight gpt-oss-120b sits well below the frontier. So the strongest open models may keep sitting a step behind the best closed ones.

Which open weight AI models lead right now?

Based on the sources above, the names to know are the GLM family (GLM-5.2 tops the open side of the Intelligence Index), DeepSeek V4 Pro (strong at coding), and OpenAI's gpt-oss-120b. Nvidia has also shown how far tuning can push a free model, which we covered in our look at Nemotron.

What Do Open-Weight AI Models Really Cost to Run?

Start with Krishna's range. If his numbers hold, every dollar a team spends on frontier tokens would buy the same volume of work from an open model for 10 to 25 cents. For a company pushing millions of emails or call transcripts through a model, that is the whole argument. (Closed prices are falling too, as we covered in our breakdown of LLM API pricing cuts, so check current rates before you do the math.)

But per-token price only tells you part of the story when you run the model yourself. A self hosted LLM turns a usage bill into a fixed one: you buy or rent GPUs, pay for power and space, and pay people to keep it fast and secure. That trade pays off when your volume is high and steady, and it hurts when your servers sit idle. Krishna's own warning about kernel and network engineers is the hidden line item.

Self-hosted LLM vs using an API

• Using a closed API: no hardware, best raw quality, pay per token, and your data goes to the vendor.
• Using an open model through a hosting provider's API: many cloud providers serve open weight models per token, which is where much of the price gap shows up. Little setup, but your data still leaves your network.
• Self-hosting an open model: your data stays in your building, you can fine-tune freely, and costs become predictable. In exchange, you own every outage, upgrade and security patch.

Who Should Use Open Weight Models (and Who Should Not)

Here is a simple way to decide.

Good fit:

• Regulated or privacy-sensitive businesses, such as healthcare, finance, legal and government, where keeping data on your own machines matters more than squeezing out the last few benchmark points.
• High-volume, repetitive tasks like the ones Krishna listed: summarizing, classifying, reviewing documents, routing support calls.
• Teams that want to fine-tune a model on their own data and own the result.
• Students and hobbyists, who can run smaller open models locally to learn how these systems work with no API bill at all.

Poor fit, for now:

• Products that live or die on frontier quality, such as complex agents, multi-step reasoning or rich image understanding, where closed models still hold an edge.
• Small teams with no infrastructure people. If nobody can debug a GPU server at 2 a.m., start with an API, closed or open-hosted, and revisit when volume grows.

A practical middle path for founders: build on an API first, measure what your tasks really need, then test an open model on the same prompts. If it is good enough on your work, not on a leaderboard, the savings are yours to take.

What to Watch Next

Watch whether IBM's promised deployment software makes on-prem AI realistic for companies without specialist engineers, and whether Spyre sales show real demand behind the CEO's pitch. Watch the lag numbers: if the average gap shrinks below four months, the case for paying frontier prices on routine work gets thin. Watch whether more labs follow Meta in keeping their best models closed. And watch independent evaluations like CAISI's, which test models on benchmarks they have not been tuned for.