GLM-5.3 wins on coding (91 vs 90). For most workflows, GLM-5.3 is the stronger default — same price as glm-5.2, far stronger on agents and security.
The safest GLM-5.3 vs GLM-5.2 default, the cheaper option worth trying first, and the specialist pick — before you read the detail below.
GLM-5.3 is the strongest answer here for GLM-5.3 vs GLM-5.2 — pick it when quality of output matters more than the $1.40/1M/1M input you pay for it.
GPT-5.1-Codex-Max is the cheaper way in for GLM-5.3 vs GLM-5.2, at $1.25/1M/1M input against GLM-5.3's $1.40/1M/1M.
GLM-5.2 is the fastest of these for GLM-5.3 vs GLM-5.2 — worth it when latency is what the reader notices, not the last few points of reasoning depth.
GLM-5.3 leads on coding with a score of 91 vs 90 for GLM-5.2.
Both models are similarly priced — the decision comes down to capability, not cost.
GLM-5.3 is the stronger default for coding tasks.
Choose GLM-5.3 for agentic engineering and security work on open weights. Its coding and reasoning scores are what carry the recommendation here.
GLM-5.2 earns its place when your work is mostly budget agentic coding at scale, even though it loses the overall count here.
Both models serve different primary workflows — GLM-5.3 for agentic engineering and security work on open weights, GLM-5.2 for budget agentic coding at scale — so running each where it has a clear edge often beats forcing one to do both.
Switch the scoring lens to see whether the GLM-5.3 vs GLM-5.2 answer changes when cost, speed, or long-document depth leads the decision.
Z.ai / Budget / Aug 27, 2026
Same price as GLM-5.2, far stronger on agents and security.
Ranks models by the broadest mix of coding, writing, research, and long-context usefulness.
Your procurement process requires a SWE-bench Verified figure, or you need the closed-frontier reasoning ceiling.
Where the GLM-5.3 vs GLM-5.2 recommendation shifts once you weigh price or latency differently.
Same price as GLM-5.2, far stronger on agents and security.
Top open-weights coder — beats GPT-5.5 at a sixth of the cost.
List prices and published scores — the numbers this page's pick is built from.
| Model | Input | Output | Est. month | Context | Speed | Coding | Writing | Research |
|---|---|---|---|---|---|---|---|---|
| GLM-5.3Z.ai | $1.40/1M | $4.40/1M | $23 | 1M tokens | Balanced | 91 | 79 | 82 |
| GLM-5.2Z.ai | $1.40/1M | $4.40/1M | $23 | 1M tokens | Balanced | 90 | 78 | 80 |
Scores out of 100 — how we evaluate models. “Est. month” is 10M in / 2M out at list price: a ceiling, no discounts.
Why each one is on the shortlist for GLM-5.3 vs GLM-5.2, what it is genuinely good at, and where we would steer you away from it.
Our pick for GLM-5.3 vs GLM-5.2. It scores 91/100 on the coding axis we weight this page by, and nothing else in this shortlist matches it on output quality.
Z.ai's newest flagship, aimed squarely at software engineering, autonomous agents and cybersecurity — and the first open-weights model to beat Claude Mythos 5 on a security benchmark.
Your procurement process requires a SWE-bench Verified figure, or you need the closed-frontier reasoning ceiling.
The clear upgrade over GLM-5.2 at the same price — take it unless you specifically need a published SWE-bench Verified number to sign off on. The security benchmark lead over Mythos 5 is the genuinely new thing here.
Full pricing, benchmark table and release notes on the GLM-5.3 page.
Here for latency: it answers fastest of anything listed for GLM-5.3 vs GLM-5.2, at 90/100 on coding.
Z.ai's MIT-licensed open-weight flagship — the top open-weights coding model of mid-2026, beating GPT-5.5 on agentic coding benchmarks at roughly a sixth of the cost.
You need frontier reasoning ceiling or launch-day verified benchmarks.
The open-weights coding value king of mid-2026 — GPT-5.5-beating agentic coding at a fraction of the price, with an MIT license. DeepSeek V4-Flash undercuts it on price; GLM-5.2 answers with higher ceiling and two reasoning-effort modes.
Full pricing, benchmark table and release notes on the GLM-5.2 page.
UseRightAI recommendations are based on practical decision factors people actually feel in day-to-day use.
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GLM-5.3 wins on more of the categories we score — coding, reasoning, budget — so it is the better default of the two. GLM-5.2 is the better pick when your work is mostly budget agentic coding at scale. Neither is universally "better": GLM-5.3 is aimed at agentic engineering and security work on open weights, GLM-5.2 at budget agentic coding at scale.
Both models are similarly priced at $1.4/1M input tokens. The decision should come down to capability, not cost.
Both GLM-5.3 and GLM-5.2 have the same 1M context window.
GLM-5.3 is better for coding with a score of 91 vs GLM-5.2's 90 (out of 100). GPT-6 Astra is the overall coding leader in this directory at 100/100.
Both GLM-5.3 and GLM-5.2 have similar speed profiles — rated balanced. Neither will be the bottleneck if latency is your deciding factor.
No published SWE-bench Verified score, so it is absent from the benchmark most buyers compare on. Priced identically to GLM-5.2 at $1.40/$4.40 — the upgrade is capability, not value. Avoid it if your procurement process requires a SWE-bench Verified figure, or you need the closed-frontier reasoning ceiling. That is the main case for looking at GLM-5.2 instead.
Clear gap to the closed frontier: AA Index 51 vs Claude Opus 5 (61) and GPT-5.6 Sol (59). Z.ai published no benchmark numbers at launch — buyers depended on third-party evals that arrived weeks later. Avoid it if you need frontier reasoning ceiling or launch-day verified benchmarks. Against GLM-5.3 specifically, the gap shows up most on coding (91 vs 90).
Take a moderate workload of 10M input and 2M output tokens a month. GLM-5.3 runs $22.80 (at $1.4/1M in and $4.4/1M out); GLM-5.2 runs $22.80 (at $1.4/1M in and $4.4/1M out). The gap is small enough that price should not decide this one. Output tokens dominate the bill on both, so prompt length matters far less than response length.
Yes, and for most teams that beats picking one. A common split is GLM-5.3 for agentic engineering and security work on open weights, with GLM-5.2 handling budget agentic coding at scale. Since GLM-5.3 is both the stronger and the cheaper option here, a split mainly makes sense if GLM-5.2 covers a capability you specifically need.