GLM-5.2
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.
Same price as GLM-5.2, far stronger on agents and security.
Agentic engineering and security work on open weights
Your procurement process requires a SWE-bench Verified figure, or you need the closed-frontier reasoning ceiling.
Compare every model's knowledge cutoff, max output, and context window.
Released August 14, 2026. Z.ai list pricing is $1.40/$4.40, the same rate as GLM-5.2; resellers discount from that list. Also available through the GLM Coding Plan from $18/mo. Reported GPQA Diamond 91.7% and Artificial Analysis Intelligence Index 59.5.
Huge agentic gains over GLM-5.2: Terminal-Bench 3.0 from 4.6 to 28.3, DeepSWE v1.1 from 46.2 to 66.9, SWE-Marathon v1.1 from 19.4 to 42.5
84.5% on CyberGym, narrowly ahead of Claude Mythos 5 at 83.8%; ExploitBench more than doubled from 24.4% to 54.4%
88.2% on Terminal-Bench 2.1 with a 1M token context window
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
What people actually use GLM-5.3 for.
Long-horizon autonomous engineering tasks where GLM-5.2 ran out of headroom
Offensive and defensive security tooling — 84.5% on CyberGym, ahead of Claude Mythos 5
Self-hosted or coding-plan deployments that need frontier-adjacent quality at open-weights pricing
The nearest models people weigh against it, and what actually separates them.
vs GLM-5.2 — Against GLM-5.2 (Z.ai), GLM-5.3 lands within a few percent on price. Which one wins depends on whether context depth or latency is your constraint.
vs DeepSeek V4-Pro — Against DeepSeek V4-Pro (DeepSeek), GLM-5.3 costs about 78% more per token. DeepSeek V4-Pro is the one to check first if the price difference matters more than the ceiling.
vs DeepSeek V4-Flash — Against DeepSeek V4-Flash (DeepSeek), GLM-5.3 costs about 93% more per token and answers slower. DeepSeek V4-Flash is the one to check first if the price difference matters more than the ceiling.
Price History
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7 data points · tracked daily since Sep 1, 2026
Agentic engineering and security work on open weights. Start free — no card required.
Recommendations are made independently based on real-world use and public benchmarks. See our disclosures for details.
Similar models worth checking before you commit.
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.
DeepSeek's 1.6T-parameter (49B active) MoE flagship with hybrid sparse attention — near-frontier coding and reasoning at roughly a tenth of closed-rival pricing, MIT-licensed open weights.
A 284B-parameter (13B active) MoE workhorse re-post-trained for agentic and coding tasks — beats the V4-Pro preview on every published agent benchmark at ultra-commodity pricing.
GLM-5.3 costs $1.4 per million input tokens and $4.4 per million output tokens on the API. A month of 10M input and 2M output tokens runs about $22.80 at list price, before any batch or caching discounts.
GLM-5.3 has a 1M tokens context window, with up to 1M tokens of output per response. That is the total of prompt plus response the model can hold in one request.
GLM-5.3 is best for agentic engineering and security work on open weights. It is a strong fit when that workflow matters more than the tradeoffs around budget pricing and balanced speed.
Your procurement process requires a SWE-bench Verified figure, or you need the closed-frontier reasoning ceiling.
DeepSeek V4-Pro (DeepSeek) at $0.43/1M/1M input against GLM-5.3's $1.40/1M/1M — roughly 78% less per token all in. Best open-weights flagship — near-frontier coding at a tenth of the price. Compare it first if GLM-5.3's pricing is the thing stopping you.
DeepSeek V4-Flash — fast against GLM-5.3's balanced, with 1M tokens of context. Worth the swap when response time is what your users notice rather than the last few points of reasoning depth.
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