GPT-6 Astra vs GLM-5.3
GPT-6 Astra and GLM-5.3 are within three points on the Models at Work leaderboard, which we treat as a tie (82 vs 81). GLM-5.3 is about 9.3× cheaper per blended million tokens ($2.15 vs $20). On Artificial Analysis' independent Intelligence Index, GPT-6 Astra leads 53 to 45.
| GPT-6 Astra · OpenAI | GLM-5.3 · Z.ai | |
|---|---|---|
| Leaderboard rank | #4 | #5 |
| Score (0–100) | 82 | 81 |
| Tier | Frontier | Open weights |
| Price per 1M tokens | $10 in · $50 out | $1.40 in · $4.40 out |
| Context window | 1M | 1M |
| Open weights | No | Yes |
| Artificial Analysis Intelligence Index | 53 | 45 |
| Humanity's Last Exam, Diamond (Scale) | 60.6 | — |
| Humanity's Sixth Sense (Scale) | 53.6 | — |
| FrontierMath Erdős (Epoch) | 3% (2 of 68) | — |
| Arena Text score | — | 1478 (rank 27) |
| Scale SWE-Bench Pro V2 (mini-swe-agent) | — | 84.30% |
| Scale MCP Atlas | — | 84.20 |
| Arena WebDev score | — | 1622 (rank 22) |
| Best for | Hard reasoning, Research-grade analysis, When cost is not the constraint | Agentic coding in Claude Code / Pi-style harnesses, Security research and code auditing, Self-hosted frontier-ish reasoning |
Choose GPT-6 Astra if…
- you need hard reasoning
- you need research-grade analysis
- you need when cost is not the constraint
The reasoning heavyweight: leads the hardest exams and the cipher-cracking party tricks, and charges like it.
Caveats: Long-context pricing doubles above 272K input tokens. Speed is the lowest of the frontier set in Artificial Analysis' measurement.
Choose GLM-5.3 if…
- you need agentic coding in claude code / pi-style harnesses
- you need security research and code auditing
- you need self-hosted frontier-ish reasoning
The open model people actually ship coding agents on. Same 744B base as GLM-5.2 with heavy agentic post-training; drops into Claude Code harnesses and holds its own on SWE-Bench Pro V2 and MCP Atlas at a third of frontier prices.
Caveats: License is muddled: AA lists a custom 'GLM-5.3 License' with commercial restrictions while Arena lists MIT. Read it before you self-host. Permissive on offensive-security tasks; that is a feature for red teams and a governance problem for everyone else. Open weights shipped ~2 weeks after the API; HN worried the public weights were safety-tuned differently.
What practitioners say
GPT-6 Astra: The launch drew the largest model thread of 2026 on Hacker News, and the stories that followed were about capability rather than cost: an Enigma message unsolved since 2005, a WWI cipher, a driving benchmark.
GLM-5.3: One of the biggest HN launches of the year (1,171 points, 584 comments). Practitioners report it 'fits Claude Code as its own, zero issues', that '$5 in tokens' of Claude work costs '$0.50' on GLM via Pi, and that it 'routinely finds bugs missed by Fable and Sol' in audits. The cyber angle was the controversy: users bragged about adapting kernel exploits that 'Claude and Opus outright refused', and others flagged the risk of nerfed public weights.
Questions people ask
Which is better, GPT-6 Astra or GLM-5.3?
GPT-6 Astra and GLM-5.3 are within three points on the Models at Work leaderboard, which we treat as a tie (82 vs 81). GLM-5.3 is about 9.3× cheaper per blended million tokens ($2.15 vs $20). On Artificial Analysis' independent Intelligence Index, GPT-6 Astra leads 53 to 45.
When should I choose GPT-6 Astra over GLM-5.3?
Choose GPT-6 Astra for hard reasoning, research-grade analysis, when cost is not the constraint. The reasoning heavyweight: leads the hardest exams and the cipher-cracking party tricks, and charges like it.
When should I choose GLM-5.3 over GPT-6 Astra?
Choose GLM-5.3 for agentic coding in claude code / pi-style harnesses, security research and code auditing, self-hosted frontier-ish reasoning. The open model people actually ship coding agents on. Same 744B base as GLM-5.2 with heavy agentic post-training; drops into Claude Code harnesses and holds its own on SWE-Bench Pro V2 and MCP Atlas at a third of frontier prices.
Which is cheaper, GPT-6 Astra or GLM-5.3?
GPT-6 Astra costs $10 per million input tokens and $50 per million output tokens on OpenAI's list price (OpenAI API pricing, read Oct 11, 2026). GLM-5.3 costs $1.40 per million input tokens and $4.40 per million output tokens on Z.ai's list price (Z.ai 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.