GLM-5.3 (open-weight) beat Anthropic/OpenAI models – for 1/5 the cost
- ID
- 17716
- Status
- summarized
- Published
- 24 Aug 2026, 12:24 AM
- Fetched
- 26 Aug 2026, 12:48 AM
- Provider
- Hacker News
- Category
- dev-community
- Original URL
- https://reinvently.co.uk/tools/ed-o-meter/
- Source URL
- https://hnrss.org/best
Summary
- Score
- 7.5
- Created
- 26 Aug 2026, 12:48 AM
- Tags
- Audience
- developersvibe_codersai-ml-learnersai-agent-userssaas-founders
What happened
An independent LLM leaderboard ('Ed-o-meter') tested 17 models on 28 real-world tasks with identical prompts and deterministic grading. GLM-5.3 (open-weight) achieved 100% pass rate, a 9.3 rubric score, and $0.28 per full lap—roughly one-fifth of GPT-5.5's cost—though with a 16.3s TTFT versus GPT-5.5's 13.2s. The full suite costs ~$30 to run, and the author flags caveats including single-trial wide confidence intervals and self-judge bias for fable-5's rubric score.
Why it matters
If you're picking a single model for agentic or task pipelines, GLM-5.3's open-weight model passed all 28 tasks at ~20% of GPT-5.5's cost—but you must check compliance before deploying an open-weight model, and accept 3s slower time-to-first-token. If latency is critical, GPT-5.5 is the faster alternative; GPT-5.6-luna is cheapest for retryable low-risk jobs; haiku-4-5 for right-first-time; sonnet-4-6 for quality without the wait. The benchmark methodology (single trial, 28 tasks, ~$30 full suite) is cheap enough to replicate yourself before committing.
Discussion angle
The benchmark is single-trial with wide Wilson intervals—does a 100% pass on 28 tasks actually prove GLM-5.3 is better than GPT-5.5, or is the task set too narrow to distinguish them? Replicate the suite (~$30) on your own workloads before switching.