@rohanpaul_ai: Surprising and such a good news for open source coding model, and also that there are lots of hidden chances to reduce …

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Summary

Databricks tested GLM-5.2, an open-source coding model, and found it competes with top closed models like Claude Opus 4.8 on real enterprise code tasks while being cheaper ($1.28/task vs $1.94/task). The evaluation also highlighted Pi, a harness that reduces costs by sending less context per turn.

Surprising and such a good news for open source coding model, and also that there are lots of hidden chances to reduce cost while improving quality. Databricks showed GLM-5.2 can compete with elite closed coding models inside real enterprise code. GLM 5.2 landed in Databricks’ top capability tier and was statistically tied with Claude Opus 4.8 on quality. - The Pareto frontier for coding tasks (i.e. best quality for a given cost) includes models from OpenAI, Anthropic, and open source. - This means today, only a mix of tools can provide frontier performance. - Open models, and GLM 5.2 in particular, are now able to handle even the highest level of task difficulty. Databricks built the test because public coding benchmarks can become too familiar to models. Instead, it used real internal PRs, real tests, and a multi-million-line codebase. The cost result makes it more serious. GLM 5.2 at $1.28/task, compared with $1.94/task for Opus 4.8. So in their multi-million lines of enterprise-grade code test, GLM-5.2 made open-weight routing credible, and Pi showed why a credible model must be tested inside the right harness. Their test also separates two things people often mix up: model intelligence and agent efficiency. A coding agent needs two parts. The model does the reasoning and code writing. The harness manages the work around it, including file search, terminal commands, test output, and context. GLM-5.2 proved the first part by landing near the top closed models on Databricks’ own private tasks. Pi, the harness, proved the second part by showing that the same level of work can be done with far less context sent into the model. Databricks found Pi could run the same model with the same thinking effort at over 2x lower cost because it sent about 3x less context per turn.
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Cached at: 07/09/26, 11:42 PM

Surprising and such a good news for open source coding model, and also that there are lots of hidden chances to reduce cost while improving quality.

Databricks showed GLM-5.2 can compete with elite closed coding models inside real enterprise code.

GLM 5.2 landed in Databricks’ top capability tier and was statistically tied with Claude Opus 4.8 on quality.

  • The Pareto frontier for coding tasks (i.e. best quality for a given cost) includes models from OpenAI, Anthropic, and open source.

  • This means today, only a mix of tools can provide frontier performance.

  • Open models, and GLM 5.2 in particular, are now able to handle even the highest level of task difficulty.

Databricks built the test because public coding benchmarks can become too familiar to models.

Instead, it used real internal PRs, real tests, and a multi-million-line codebase.

The cost result makes it more serious. GLM 5.2 at $1.28/task, compared with $1.94/task for Opus 4.8.

So in their multi-million lines of enterprise-grade code test, GLM-5.2 made open-weight routing credible, and Pi showed why a credible model must be tested inside the right harness.

Their test also separates two things people often mix up: model intelligence and agent efficiency.

A coding agent needs two parts. The model does the reasoning and code writing. The harness manages the work around it, including file search, terminal commands, test output, and context. GLM-5.2 proved the first part by landing near the top closed models on Databricks’ own private tasks.

Pi, the harness, proved the second part by showing that the same level of work can be done with far less context sent into the model.

Databricks found Pi could run the same model with the same thinking effort at over 2x lower cost because it sent about 3x less context per turn.

Coding-agent costs can change massively without changing the model.

Pi saved money by giving models less repeated context while keeping quality roughly stable.

So GLM-5.2 becomes more useful when efficient harnesses keep task costs under control.

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