GLM-5.3-Flash vs GPT-6 Luna
GLM-5.3-Flash scores higher on the Models at Work leaderboard (79 vs 74 out of 100). GPT-6 Luna is about 1.2× cheaper per blended million tokens ($0.20 vs $0.24). On Artificial Analysis' independent Intelligence Index, GLM-5.3-Flash leads 42 to 38.
| GLM-5.3-Flash · Z.ai | GPT-6 Luna · OpenAI | |
|---|---|---|
| Leaderboard rank | #9 | #13 |
| Score (0–100) | 79 | 74 |
| Tier | Open weights | Fast and cheap |
| Price per 1M tokens | $0.15 in · $0.50 out | $0.10 in · $0.50 out |
| Context window | 1M | 1M |
| Open weights | Yes | No |
| Artificial Analysis Intelligence Index | 42 | 38 |
| Arena Text score | 1475 (rank 38) | — |
| Arena WebDev score | 1609 (rank 26) | — |
| Best for | Cheap agentic coding execution, Bulk document and image processing, Self-hosted multimodal assistants | ChatGPT Free and Go tier, Simple extraction, Cost floors |
Choose GLM-5.3-Flash if…
- you need cheap agentic coding execution
- you need bulk document and image processing
- you need self-hosted multimodal assistants
Probably the best price-to-intelligence ratio on the market: AA 42 for $0.15/$0.50, MIT-licensed, natively multimodal, 1M context. Not actually fast despite the name; treat it as a cheap executor behind a stronger planner.
Caveats: 'Too slow for execution, despite the name' on Z.ai's own hosting (~60 tok/s); third-party hosts vary. Users report less consistent output than full GLM-5.3; pair it with a planning model. 320B total params: self-hosting still needs a multi-GPU node.
Choose GPT-6 Luna if…
- you need chatgpt free and go tier
- you need simple extraction
- you need cost floors
OpenAI's ten-cent model: the one every ChatGPT Free user now has, and the one Haiku users keep comparing themselves to.
What practitioners say
GLM-5.3-Flash: A month-long HN diary ('One month coding with GLM 5.3 Flash', 233 points) spent $68 total; commenters agreed 'a month of agentic coding for $68 is the headline'. The working pattern is 'GLM-5.3 to write the plan, Flash to implement', with one user noting that given an unambiguous plan 'GLM 5.3 Flash executes it just fine'. Negatives: 'too slow for execution, despite the name' on Z.ai hosting, and the full model is 'way more consistent'.
Questions people ask
Which is better, GLM-5.3-Flash or GPT-6 Luna?
GLM-5.3-Flash scores higher on the Models at Work leaderboard (79 vs 74 out of 100). GPT-6 Luna is about 1.2× cheaper per blended million tokens ($0.20 vs $0.24). On Artificial Analysis' independent Intelligence Index, GLM-5.3-Flash leads 42 to 38.
When should I choose GLM-5.3-Flash over GPT-6 Luna?
Choose GLM-5.3-Flash for cheap agentic coding execution, bulk document and image processing, self-hosted multimodal assistants. Probably the best price-to-intelligence ratio on the market: AA 42 for $0.15/$0.50, MIT-licensed, natively multimodal, 1M context. Not actually fast despite the name; treat it as a cheap executor behind a stronger planner.
When should I choose GPT-6 Luna over GLM-5.3-Flash?
Choose GPT-6 Luna for chatgpt free and go tier, simple extraction, cost floors. OpenAI's ten-cent model: the one every ChatGPT Free user now has, and the one Haiku users keep comparing themselves to.
Which is cheaper, GLM-5.3-Flash or GPT-6 Luna?
GLM-5.3-Flash costs $0.15 per million input tokens and $0.50 per million output tokens on Z.ai's list price (Z.ai Pricing, read Oct 11, 2026). GPT-6 Luna costs $0.10 per million input tokens and $0.50 per million output tokens on OpenAI's list price (OpenAI API pricing, read Oct 11, 2026).
Scores come from the Models at Work leaderboard: Score is 0 to 100 and computed, not typed: 55% independent evals (Artificial Analysis, Arena, Scale SEAL, Epoch, each scaled against its natural floor and the best score in this table, then averaged), 15% blended price on a fixed log scale ($0.05 per million is 100, $60 is 0), 15% practitioner sentiment from Signals and community threads, 15% operational fit (context window, open weights). A missing component drops out and the rest are reweighted. A model with no independent eval yet is provisional and ranks below every measured one. Within three points is a tie. Vendor-published figures are labelled and never counted. Prices are vendor list prices where published; blended figures assume three input tokens per output token.